├── .github └── workflows │ └── main.yml ├── .gitignore ├── Dockerfile ├── LICENSE ├── README.md ├── data ├── imgs │ └── .keep └── masks │ └── .keep ├── evaluate.py ├── hubconf.py ├── predict.py ├── requirements.txt ├── scripts ├── download_data.bat └── download_data.sh ├── train.py ├── unet ├── __init__.py ├── unet_model.py └── unet_parts.py └── utils ├── __init__.py ├── data_loading.py ├── dice_score.py └── utils.py /.github/workflows/main.yml: -------------------------------------------------------------------------------- 1 | name: Publish Docker image 2 | 3 | on: 4 | push: 5 | branches: master 6 | 7 | jobs: 8 | push_to_registry: 9 | name: Push Docker image 10 | runs-on: ubuntu-latest 11 | steps: 12 | - name: Checkout 13 | uses: actions/checkout@v2 14 | 15 | - name: Set up Docker Buildx 16 | uses: docker/setup-buildx-action@v1 17 | 18 | - name: Log in to Docker Hub 19 | uses: docker/login-action@v1 20 | with: 21 | username: milesial 22 | password: ${{ secrets.DOCKER_PASSWORD }} 23 | 24 | - name: Log in to the Container registry 25 | uses: docker/login-action@f054a8b539a109f9f41c372932f1ae047eff08c9 26 | with: 27 | registry: ghcr.io 28 | username: ${{ github.repository_owner }} 29 | password: ${{ secrets.GITHUB_TOKEN }} 30 | 31 | - name: Extract metadata (tags, labels) for Docker 32 | id: meta 33 | uses: docker/metadata-action@v3 34 | with: 35 | images: milesial/unet 36 | 37 | - name: Build and push Docker image 38 | id: docker_build 39 | uses: docker/build-push-action@v2 40 | with: 41 | context: . 42 | push: true 43 | tags: | 44 | milesial/unet:latest 45 | ghcr.io/milesial/pytorch-unet:latest 46 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | *.pyc 2 | data/ 3 | __pycache__/ 4 | checkpoints/ 5 | *.pth 6 | *.jpg 7 | venv/ 8 | .idea/ 9 | wandb/ 10 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | FROM nvcr.io/nvidia/pytorch:22.11-py3 2 | 3 | RUN rm -rf /workspace/* 4 | WORKDIR /workspace/unet 5 | 6 | ADD requirements.txt . 7 | RUN pip install --no-cache-dir --upgrade --pre pip 8 | RUN pip install --no-cache-dir -r requirements.txt 9 | ADD . . 10 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. By contrast, 15 | the GNU General Public License is intended to guarantee your freedom to 16 | share and change all versions of a program--to make sure it remains free 17 | software for all its users. We, the Free Software Foundation, use the 18 | GNU General Public License for most of our software; it applies also to 19 | any other work released this way by its authors. You can apply it to 20 | your programs, too. 21 | 22 | When we speak of free software, we are referring to freedom, not 23 | price. Our General Public Licenses are designed to make sure that you 24 | have the freedom to distribute copies of free software (and charge for 25 | them if you wish), that you receive source code or can get it if you 26 | want it, that you can change the software or use pieces of it in new 27 | free programs, and that you know you can do these things. 28 | 29 | To protect your rights, we need to prevent others from denying you 30 | these rights or asking you to surrender the rights. Therefore, you have 31 | certain responsibilities if you distribute copies of the software, or if 32 | you modify it: responsibilities to respect the freedom of others. 33 | 34 | For example, if you distribute copies of such a program, whether 35 | gratis or for a fee, you must pass on to the recipients the same 36 | freedoms that you received. You must make sure that they, too, receive 37 | or can get the source code. And you must show them these terms so they 38 | know their rights. 39 | 40 | Developers that use the GNU GPL protect your rights with two steps: 41 | (1) assert copyright on the software, and (2) offer you this License 42 | giving you legal permission to copy, distribute and/or modify it. 43 | 44 | For the developers' and authors' protection, the GPL clearly explains 45 | that there is no warranty for this free software. For both users' and 46 | authors' sake, the GPL requires that modified versions be marked as 47 | changed, so that their problems will not be attributed erroneously to 48 | authors of previous versions. 49 | 50 | Some devices are designed to deny users access to install or run 51 | modified versions of the software inside them, although the manufacturer 52 | can do so. This is fundamentally incompatible with the aim of 53 | protecting users' freedom to change the software. The systematic 54 | pattern of such abuse occurs in the area of products for individuals to 55 | use, which is precisely where it is most unacceptable. Therefore, we 56 | have designed this version of the GPL to prohibit the practice for those 57 | products. If such problems arise substantially in other domains, we 58 | stand ready to extend this provision to those domains in future versions 59 | of the GPL, as needed to protect the freedom of users. 60 | 61 | Finally, every program is threatened constantly by software patents. 62 | States should not allow patents to restrict development and use of 63 | software on general-purpose computers, but in those that do, we wish to 64 | avoid the special danger that patents applied to a free program could 65 | make it effectively proprietary. To prevent this, the GPL assures that 66 | patents cannot be used to render the program non-free. 67 | 68 | The precise terms and conditions for copying, distribution and 69 | modification follow. 70 | 71 | TERMS AND CONDITIONS 72 | 73 | 0. Definitions. 74 | 75 | "This License" refers to version 3 of the GNU General Public License. 76 | 77 | "Copyright" also means copyright-like laws that apply to other kinds of 78 | works, such as semiconductor masks. 79 | 80 | "The Program" refers to any copyrightable work licensed under this 81 | License. Each licensee is addressed as "you". "Licensees" and 82 | "recipients" may be individuals or organizations. 83 | 84 | To "modify" a work means to copy from or adapt all or part of the work 85 | in a fashion requiring copyright permission, other than the making of an 86 | exact copy. The resulting work is called a "modified version" of the 87 | earlier work or a work "based on" the earlier work. 88 | 89 | A "covered work" means either the unmodified Program or a work based 90 | on the Program. 91 | 92 | To "propagate" a work means to do anything with it that, without 93 | permission, would make you directly or secondarily liable for 94 | infringement under applicable copyright law, except executing it on a 95 | computer or modifying a private copy. Propagation includes copying, 96 | distribution (with or without modification), making available to the 97 | public, and in some countries other activities as well. 98 | 99 | To "convey" a work means any kind of propagation that enables other 100 | parties to make or receive copies. Mere interaction with a user through 101 | a computer network, with no transfer of a copy, is not conveying. 102 | 103 | An interactive user interface displays "Appropriate Legal Notices" 104 | to the extent that it includes a convenient and prominently visible 105 | feature that (1) displays an appropriate copyright notice, and (2) 106 | tells the user that there is no warranty for the work (except to the 107 | extent that warranties are provided), that licensees may convey the 108 | work under this License, and how to view a copy of this License. If 109 | the interface presents a list of user commands or options, such as a 110 | menu, a prominent item in the list meets this criterion. 111 | 112 | 1. Source Code. 113 | 114 | The "source code" for a work means the preferred form of the work 115 | for making modifications to it. "Object code" means any non-source 116 | form of a work. 117 | 118 | A "Standard Interface" means an interface that either is an official 119 | standard defined by a recognized standards body, or, in the case of 120 | interfaces specified for a particular programming language, one that 121 | is widely used among developers working in that language. 122 | 123 | The "System Libraries" of an executable work include anything, other 124 | than the work as a whole, that (a) is included in the normal form of 125 | packaging a Major Component, but which is not part of that Major 126 | Component, and (b) serves only to enable use of the work with that 127 | Major Component, or to implement a Standard Interface for which an 128 | implementation is available to the public in source code form. A 129 | "Major Component", in this context, means a major essential component 130 | (kernel, window system, and so on) of the specific operating system 131 | (if any) on which the executable work runs, or a compiler used to 132 | produce the work, or an object code interpreter used to run it. 133 | 134 | The "Corresponding Source" for a work in object code form means all 135 | the source code needed to generate, install, and (for an executable 136 | work) run the object code and to modify the work, including scripts to 137 | control those activities. However, it does not include the work's 138 | System Libraries, or general-purpose tools or generally available free 139 | programs which are used unmodified in performing those activities but 140 | which are not part of the work. For example, Corresponding Source 141 | includes interface definition files associated with source files for 142 | the work, and the source code for shared libraries and dynamically 143 | linked subprograms that the work is specifically designed to require, 144 | such as by intimate data communication or control flow between those 145 | subprograms and other parts of the work. 146 | 147 | The Corresponding Source need not include anything that users 148 | can regenerate automatically from other parts of the Corresponding 149 | Source. 150 | 151 | The Corresponding Source for a work in source code form is that 152 | same work. 153 | 154 | 2. Basic Permissions. 155 | 156 | All rights granted under this License are granted for the term of 157 | copyright on the Program, and are irrevocable provided the stated 158 | conditions are met. This License explicitly affirms your unlimited 159 | permission to run the unmodified Program. The output from running a 160 | covered work is covered by this License only if the output, given its 161 | content, constitutes a covered work. This License acknowledges your 162 | rights of fair use or other equivalent, as provided by copyright law. 163 | 164 | You may make, run and propagate covered works that you do not 165 | convey, without conditions so long as your license otherwise remains 166 | in force. You may convey covered works to others for the sole purpose 167 | of having them make modifications exclusively for you, or provide you 168 | with facilities for running those works, provided that you comply with 169 | the terms of this License in conveying all material for which you do 170 | not control copyright. Those thus making or running the covered works 171 | for you must do so exclusively on your behalf, under your direction 172 | and control, on terms that prohibit them from making any copies of 173 | your copyrighted material outside their relationship with you. 174 | 175 | Conveying under any other circumstances is permitted solely under 176 | the conditions stated below. Sublicensing is not allowed; section 10 177 | makes it unnecessary. 178 | 179 | 3. Protecting Users' Legal Rights From Anti-Circumvention Law. 180 | 181 | No covered work shall be deemed part of an effective technological 182 | measure under any applicable law fulfilling obligations under article 183 | 11 of the WIPO copyright treaty adopted on 20 December 1996, or 184 | similar laws prohibiting or restricting circumvention of such 185 | measures. 186 | 187 | When you convey a covered work, you waive any legal power to forbid 188 | circumvention of technological measures to the extent such circumvention 189 | is effected by exercising rights under this License with respect to 190 | the covered work, and you disclaim any intention to limit operation or 191 | modification of the work as a means of enforcing, against the work's 192 | users, your or third parties' legal rights to forbid circumvention of 193 | technological measures. 194 | 195 | 4. Conveying Verbatim Copies. 196 | 197 | You may convey verbatim copies of the Program's source code as you 198 | receive it, in any medium, provided that you conspicuously and 199 | appropriately publish on each copy an appropriate copyright notice; 200 | keep intact all notices stating that this License and any 201 | non-permissive terms added in accord with section 7 apply to the code; 202 | keep intact all notices of the absence of any warranty; and give all 203 | recipients a copy of this License along with the Program. 204 | 205 | You may charge any price or no price for each copy that you convey, 206 | and you may offer support or warranty protection for a fee. 207 | 208 | 5. Conveying Modified Source Versions. 209 | 210 | You may convey a work based on the Program, or the modifications to 211 | produce it from the Program, in the form of source code under the 212 | terms of section 4, provided that you also meet all of these conditions: 213 | 214 | a) The work must carry prominent notices stating that you modified 215 | it, and giving a relevant date. 216 | 217 | b) The work must carry prominent notices stating that it is 218 | released under this License and any conditions added under section 219 | 7. This requirement modifies the requirement in section 4 to 220 | "keep intact all notices". 221 | 222 | c) You must license the entire work, as a whole, under this 223 | License to anyone who comes into possession of a copy. This 224 | License will therefore apply, along with any applicable section 7 225 | additional terms, to the whole of the work, and all its parts, 226 | regardless of how they are packaged. This License gives no 227 | permission to license the work in any other way, but it does not 228 | invalidate such permission if you have separately received it. 229 | 230 | d) If the work has interactive user interfaces, each must display 231 | Appropriate Legal Notices; however, if the Program has interactive 232 | interfaces that do not display Appropriate Legal Notices, your 233 | work need not make them do so. 234 | 235 | A compilation of a covered work with other separate and independent 236 | works, which are not by their nature extensions of the covered work, 237 | and which are not combined with it such as to form a larger program, 238 | in or on a volume of a storage or distribution medium, is called an 239 | "aggregate" if the compilation and its resulting copyright are not 240 | used to limit the access or legal rights of the compilation's users 241 | beyond what the individual works permit. Inclusion of a covered work 242 | in an aggregate does not cause this License to apply to the other 243 | parts of the aggregate. 244 | 245 | 6. Conveying Non-Source Forms. 246 | 247 | You may convey a covered work in object code form under the terms 248 | of sections 4 and 5, provided that you also convey the 249 | machine-readable Corresponding Source under the terms of this License, 250 | in one of these ways: 251 | 252 | a) Convey the object code in, or embodied in, a physical product 253 | (including a physical distribution medium), accompanied by the 254 | Corresponding Source fixed on a durable physical medium 255 | customarily used for software interchange. 256 | 257 | b) Convey the object code in, or embodied in, a physical product 258 | (including a physical distribution medium), accompanied by a 259 | written offer, valid for at least three years and valid for as 260 | long as you offer spare parts or customer support for that product 261 | model, to give anyone who possesses the object code either (1) a 262 | copy of the Corresponding Source for all the software in the 263 | product that is covered by this License, on a durable physical 264 | medium customarily used for software interchange, for a price no 265 | more than your reasonable cost of physically performing this 266 | conveying of source, or (2) access to copy the 267 | Corresponding Source from a network server at no charge. 268 | 269 | c) Convey individual copies of the object code with a copy of the 270 | written offer to provide the Corresponding Source. This 271 | alternative is allowed only occasionally and noncommercially, and 272 | only if you received the object code with such an offer, in accord 273 | with subsection 6b. 274 | 275 | d) Convey the object code by offering access from a designated 276 | place (gratis or for a charge), and offer equivalent access to the 277 | Corresponding Source in the same way through the same place at no 278 | further charge. You need not require recipients to copy the 279 | Corresponding Source along with the object code. If the place to 280 | copy the object code is a network server, the Corresponding Source 281 | may be on a different server (operated by you or a third party) 282 | that supports equivalent copying facilities, provided you maintain 283 | clear directions next to the object code saying where to find the 284 | Corresponding Source. Regardless of what server hosts the 285 | Corresponding Source, you remain obligated to ensure that it is 286 | available for as long as needed to satisfy these requirements. 287 | 288 | e) Convey the object code using peer-to-peer transmission, provided 289 | you inform other peers where the object code and Corresponding 290 | Source of the work are being offered to the general public at no 291 | charge under subsection 6d. 292 | 293 | A separable portion of the object code, whose source code is excluded 294 | from the Corresponding Source as a System Library, need not be 295 | included in conveying the object code work. 296 | 297 | A "User Product" is either (1) a "consumer product", which means any 298 | tangible personal property which is normally used for personal, family, 299 | or household purposes, or (2) anything designed or sold for incorporation 300 | into a dwelling. In determining whether a product is a consumer product, 301 | doubtful cases shall be resolved in favor of coverage. For a particular 302 | product received by a particular user, "normally used" refers to a 303 | typical or common use of that class of product, regardless of the status 304 | of the particular user or of the way in which the particular user 305 | actually uses, or expects or is expected to use, the product. A product 306 | is a consumer product regardless of whether the product has substantial 307 | commercial, industrial or non-consumer uses, unless such uses represent 308 | the only significant mode of use of the product. 309 | 310 | "Installation Information" for a User Product means any methods, 311 | procedures, authorization keys, or other information required to install 312 | and execute modified versions of a covered work in that User Product from 313 | a modified version of its Corresponding Source. The information must 314 | suffice to ensure that the continued functioning of the modified object 315 | code is in no case prevented or interfered with solely because 316 | modification has been made. 317 | 318 | If you convey an object code work under this section in, or with, or 319 | specifically for use in, a User Product, and the conveying occurs as 320 | part of a transaction in which the right of possession and use of the 321 | User Product is transferred to the recipient in perpetuity or for a 322 | fixed term (regardless of how the transaction is characterized), the 323 | Corresponding Source conveyed under this section must be accompanied 324 | by the Installation Information. But this requirement does not apply 325 | if neither you nor any third party retains the ability to install 326 | modified object code on the User Product (for example, the work has 327 | been installed in ROM). 328 | 329 | The requirement to provide Installation Information does not include a 330 | requirement to continue to provide support service, warranty, or updates 331 | for a work that has been modified or installed by the recipient, or for 332 | the User Product in which it has been modified or installed. Access to a 333 | network may be denied when the modification itself materially and 334 | adversely affects the operation of the network or violates the rules and 335 | protocols for communication across the network. 336 | 337 | Corresponding Source conveyed, and Installation Information provided, 338 | in accord with this section must be in a format that is publicly 339 | documented (and with an implementation available to the public in 340 | source code form), and must require no special password or key for 341 | unpacking, reading or copying. 342 | 343 | 7. Additional Terms. 344 | 345 | "Additional permissions" are terms that supplement the terms of this 346 | License by making exceptions from one or more of its conditions. 347 | Additional permissions that are applicable to the entire Program shall 348 | be treated as though they were included in this License, to the extent 349 | that they are valid under applicable law. If additional permissions 350 | apply only to part of the Program, that part may be used separately 351 | under those permissions, but the entire Program remains governed by 352 | this License without regard to the additional permissions. 353 | 354 | When you convey a copy of a covered work, you may at your option 355 | remove any additional permissions from that copy, or from any part of 356 | it. (Additional permissions may be written to require their own 357 | removal in certain cases when you modify the work.) You may place 358 | additional permissions on material, added by you to a covered work, 359 | for which you have or can give appropriate copyright permission. 360 | 361 | Notwithstanding any other provision of this License, for material you 362 | add to a covered work, you may (if authorized by the copyright holders of 363 | that material) supplement the terms of this License with terms: 364 | 365 | a) Disclaiming warranty or limiting liability differently from the 366 | terms of sections 15 and 16 of this License; or 367 | 368 | b) Requiring preservation of specified reasonable legal notices or 369 | author attributions in that material or in the Appropriate Legal 370 | Notices displayed by works containing it; or 371 | 372 | c) Prohibiting misrepresentation of the origin of that material, or 373 | requiring that modified versions of such material be marked in 374 | reasonable ways as different from the original version; or 375 | 376 | d) Limiting the use for publicity purposes of names of licensors or 377 | authors of the material; or 378 | 379 | e) Declining to grant rights under trademark law for use of some 380 | trade names, trademarks, or service marks; or 381 | 382 | f) Requiring indemnification of licensors and authors of that 383 | material by anyone who conveys the material (or modified versions of 384 | it) with contractual assumptions of liability to the recipient, for 385 | any liability that these contractual assumptions directly impose on 386 | those licensors and authors. 387 | 388 | All other non-permissive additional terms are considered "further 389 | restrictions" within the meaning of section 10. If the Program as you 390 | received it, or any part of it, contains a notice stating that it is 391 | governed by this License along with a term that is a further 392 | restriction, you may remove that term. If a license document contains 393 | a further restriction but permits relicensing or conveying under this 394 | License, you may add to a covered work material governed by the terms 395 | of that license document, provided that the further restriction does 396 | not survive such relicensing or conveying. 397 | 398 | If you add terms to a covered work in accord with this section, you 399 | must place, in the relevant source files, a statement of the 400 | additional terms that apply to those files, or a notice indicating 401 | where to find the applicable terms. 402 | 403 | Additional terms, permissive or non-permissive, may be stated in the 404 | form of a separately written license, or stated as exceptions; 405 | the above requirements apply either way. 406 | 407 | 8. Termination. 408 | 409 | You may not propagate or modify a covered work except as expressly 410 | provided under this License. Any attempt otherwise to propagate or 411 | modify it is void, and will automatically terminate your rights under 412 | this License (including any patent licenses granted under the third 413 | paragraph of section 11). 414 | 415 | However, if you cease all violation of this License, then your 416 | license from a particular copyright holder is reinstated (a) 417 | provisionally, unless and until the copyright holder explicitly and 418 | finally terminates your license, and (b) permanently, if the copyright 419 | holder fails to notify you of the violation by some reasonable means 420 | prior to 60 days after the cessation. 421 | 422 | Moreover, your license from a particular copyright holder is 423 | reinstated permanently if the copyright holder notifies you of the 424 | violation by some reasonable means, this is the first time you have 425 | received notice of violation of this License (for any work) from that 426 | copyright holder, and you cure the violation prior to 30 days after 427 | your receipt of the notice. 428 | 429 | Termination of your rights under this section does not terminate the 430 | licenses of parties who have received copies or rights from you under 431 | this License. If your rights have been terminated and not permanently 432 | reinstated, you do not qualify to receive new licenses for the same 433 | material under section 10. 434 | 435 | 9. Acceptance Not Required for Having Copies. 436 | 437 | You are not required to accept this License in order to receive or 438 | run a copy of the Program. Ancillary propagation of a covered work 439 | occurring solely as a consequence of using peer-to-peer transmission 440 | to receive a copy likewise does not require acceptance. However, 441 | nothing other than this License grants you permission to propagate or 442 | modify any covered work. These actions infringe copyright if you do 443 | not accept this License. Therefore, by modifying or propagating a 444 | covered work, you indicate your acceptance of this License to do so. 445 | 446 | 10. Automatic Licensing of Downstream Recipients. 447 | 448 | Each time you convey a covered work, the recipient automatically 449 | receives a license from the original licensors, to run, modify and 450 | propagate that work, subject to this License. You are not responsible 451 | for enforcing compliance by third parties with this License. 452 | 453 | An "entity transaction" is a transaction transferring control of an 454 | organization, or substantially all assets of one, or subdividing an 455 | organization, or merging organizations. If propagation of a covered 456 | work results from an entity transaction, each party to that 457 | transaction who receives a copy of the work also receives whatever 458 | licenses to the work the party's predecessor in interest had or could 459 | give under the previous paragraph, plus a right to possession of the 460 | Corresponding Source of the work from the predecessor in interest, if 461 | the predecessor has it or can get it with reasonable efforts. 462 | 463 | You may not impose any further restrictions on the exercise of the 464 | rights granted or affirmed under this License. For example, you may 465 | not impose a license fee, royalty, or other charge for exercise of 466 | rights granted under this License, and you may not initiate litigation 467 | (including a cross-claim or counterclaim in a lawsuit) alleging that 468 | any patent claim is infringed by making, using, selling, offering for 469 | sale, or importing the Program or any portion of it. 470 | 471 | 11. Patents. 472 | 473 | A "contributor" is a copyright holder who authorizes use under this 474 | License of the Program or a work on which the Program is based. The 475 | work thus licensed is called the contributor's "contributor version". 476 | 477 | A contributor's "essential patent claims" are all patent claims 478 | owned or controlled by the contributor, whether already acquired or 479 | hereafter acquired, that would be infringed by some manner, permitted 480 | by this License, of making, using, or selling its contributor version, 481 | but do not include claims that would be infringed only as a 482 | consequence of further modification of the contributor version. For 483 | purposes of this definition, "control" includes the right to grant 484 | patent sublicenses in a manner consistent with the requirements of 485 | this License. 486 | 487 | Each contributor grants you a non-exclusive, worldwide, royalty-free 488 | patent license under the contributor's essential patent claims, to 489 | make, use, sell, offer for sale, import and otherwise run, modify and 490 | propagate the contents of its contributor version. 491 | 492 | In the following three paragraphs, a "patent license" is any express 493 | agreement or commitment, however denominated, not to enforce a patent 494 | (such as an express permission to practice a patent or covenant not to 495 | sue for patent infringement). To "grant" such a patent license to a 496 | party means to make such an agreement or commitment not to enforce a 497 | patent against the party. 498 | 499 | If you convey a covered work, knowingly relying on a patent license, 500 | and the Corresponding Source of the work is not available for anyone 501 | to copy, free of charge and under the terms of this License, through a 502 | publicly available network server or other readily accessible means, 503 | then you must either (1) cause the Corresponding Source to be so 504 | available, or (2) arrange to deprive yourself of the benefit of the 505 | patent license for this particular work, or (3) arrange, in a manner 506 | consistent with the requirements of this License, to extend the patent 507 | license to downstream recipients. "Knowingly relying" means you have 508 | actual knowledge that, but for the patent license, your conveying the 509 | covered work in a country, or your recipient's use of the covered work 510 | in a country, would infringe one or more identifiable patents in that 511 | country that you have reason to believe are valid. 512 | 513 | If, pursuant to or in connection with a single transaction or 514 | arrangement, you convey, or propagate by procuring conveyance of, a 515 | covered work, and grant a patent license to some of the parties 516 | receiving the covered work authorizing them to use, propagate, modify 517 | or convey a specific copy of the covered work, then the patent license 518 | you grant is automatically extended to all recipients of the covered 519 | work and works based on it. 520 | 521 | A patent license is "discriminatory" if it does not include within 522 | the scope of its coverage, prohibits the exercise of, or is 523 | conditioned on the non-exercise of one or more of the rights that are 524 | specifically granted under this License. You may not convey a covered 525 | work if you are a party to an arrangement with a third party that is 526 | in the business of distributing software, under which you make payment 527 | to the third party based on the extent of your activity of conveying 528 | the work, and under which the third party grants, to any of the 529 | parties who would receive the covered work from you, a discriminatory 530 | patent license (a) in connection with copies of the covered work 531 | conveyed by you (or copies made from those copies), or (b) primarily 532 | for and in connection with specific products or compilations that 533 | contain the covered work, unless you entered into that arrangement, 534 | or that patent license was granted, prior to 28 March 2007. 535 | 536 | Nothing in this License shall be construed as excluding or limiting 537 | any implied license or other defenses to infringement that may 538 | otherwise be available to you under applicable patent law. 539 | 540 | 12. No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # U-Net: Semantic segmentation with PyTorch 2 | 3 | 4 | 5 | 6 | 7 | ![input and output for a random image in the test dataset](https://i.imgur.com/GD8FcB7.png) 8 | 9 | 10 | Customized implementation of the [U-Net](https://arxiv.org/abs/1505.04597) in PyTorch for Kaggle's [Carvana Image Masking Challenge](https://www.kaggle.com/c/carvana-image-masking-challenge) from high definition images. 11 | 12 | - [Quick start](#quick-start) 13 | - [Without Docker](#without-docker) 14 | - [With Docker](#with-docker) 15 | - [Description](#description) 16 | - [Usage](#usage) 17 | - [Docker](#docker) 18 | - [Training](#training) 19 | - [Prediction](#prediction) 20 | - [Weights & Biases](#weights--biases) 21 | - [Pretrained model](#pretrained-model) 22 | - [Data](#data) 23 | 24 | ## Quick start 25 | 26 | ### Without Docker 27 | 28 | 1. [Install CUDA](https://developer.nvidia.com/cuda-downloads) 29 | 30 | 2. [Install PyTorch 1.13 or later](https://pytorch.org/get-started/locally/) 31 | 32 | 3. Install dependencies 33 | ```bash 34 | pip install -r requirements.txt 35 | ``` 36 | 37 | 4. Download the data and run training: 38 | ```bash 39 | bash scripts/download_data.sh 40 | python train.py --amp 41 | ``` 42 | 43 | ### With Docker 44 | 45 | 1. [Install Docker 19.03 or later:](https://docs.docker.com/get-docker/) 46 | ```bash 47 | curl https://get.docker.com | sh && sudo systemctl --now enable docker 48 | ``` 49 | 2. [Install the NVIDIA container toolkit:](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) 50 | ```bash 51 | distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ 52 | && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \ 53 | && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list 54 | sudo apt-get update 55 | sudo apt-get install -y nvidia-docker2 56 | sudo systemctl restart docker 57 | ``` 58 | 3. [Download and run the image:](https://hub.docker.com/repository/docker/milesial/unet) 59 | ```bash 60 | sudo docker run --rm --shm-size=8g --ulimit memlock=-1 --gpus all -it milesial/unet 61 | ``` 62 | 63 | 4. Download the data and run training: 64 | ```bash 65 | bash scripts/download_data.sh 66 | python train.py --amp 67 | ``` 68 | 69 | ## Description 70 | This model was trained from scratch with 5k images and scored a [Dice coefficient](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient) of 0.988423 on over 100k test images. 71 | 72 | It can be easily used for multiclass segmentation, portrait segmentation, medical segmentation, ... 73 | 74 | 75 | ## Usage 76 | **Note : Use Python 3.6 or newer** 77 | 78 | ### Docker 79 | 80 | A docker image containing the code and the dependencies is available on [DockerHub](https://hub.docker.com/repository/docker/milesial/unet). 81 | You can download and jump in the container with ([docker >=19.03](https://docs.docker.com/get-docker/)): 82 | 83 | ```console 84 | docker run -it --rm --shm-size=8g --ulimit memlock=-1 --gpus all milesial/unet 85 | ``` 86 | 87 | 88 | ### Training 89 | 90 | ```console 91 | > python train.py -h 92 | usage: train.py [-h] [--epochs E] [--batch-size B] [--learning-rate LR] 93 | [--load LOAD] [--scale SCALE] [--validation VAL] [--amp] 94 | 95 | Train the UNet on images and target masks 96 | 97 | optional arguments: 98 | -h, --help show this help message and exit 99 | --epochs E, -e E Number of epochs 100 | --batch-size B, -b B Batch size 101 | --learning-rate LR, -l LR 102 | Learning rate 103 | --load LOAD, -f LOAD Load model from a .pth file 104 | --scale SCALE, -s SCALE 105 | Downscaling factor of the images 106 | --validation VAL, -v VAL 107 | Percent of the data that is used as validation (0-100) 108 | --amp Use mixed precision 109 | ``` 110 | 111 | By default, the `scale` is 0.5, so if you wish to obtain better results (but use more memory), set it to 1. 112 | 113 | Automatic mixed precision is also available with the `--amp` flag. [Mixed precision](https://arxiv.org/abs/1710.03740) allows the model to use less memory and to be faster on recent GPUs by using FP16 arithmetic. Enabling AMP is recommended. 114 | 115 | 116 | ### Prediction 117 | 118 | After training your model and saving it to `MODEL.pth`, you can easily test the output masks on your images via the CLI. 119 | 120 | To predict a single image and save it: 121 | 122 | `python predict.py -i image.jpg -o output.jpg` 123 | 124 | To predict a multiple images and show them without saving them: 125 | 126 | `python predict.py -i image1.jpg image2.jpg --viz --no-save` 127 | 128 | ```console 129 | > python predict.py -h 130 | usage: predict.py [-h] [--model FILE] --input INPUT [INPUT ...] 131 | [--output INPUT [INPUT ...]] [--viz] [--no-save] 132 | [--mask-threshold MASK_THRESHOLD] [--scale SCALE] 133 | 134 | Predict masks from input images 135 | 136 | optional arguments: 137 | -h, --help show this help message and exit 138 | --model FILE, -m FILE 139 | Specify the file in which the model is stored 140 | --input INPUT [INPUT ...], -i INPUT [INPUT ...] 141 | Filenames of input images 142 | --output INPUT [INPUT ...], -o INPUT [INPUT ...] 143 | Filenames of output images 144 | --viz, -v Visualize the images as they are processed 145 | --no-save, -n Do not save the output masks 146 | --mask-threshold MASK_THRESHOLD, -t MASK_THRESHOLD 147 | Minimum probability value to consider a mask pixel white 148 | --scale SCALE, -s SCALE 149 | Scale factor for the input images 150 | ``` 151 | You can specify which model file to use with `--model MODEL.pth`. 152 | 153 | ## Weights & Biases 154 | 155 | The training progress can be visualized in real-time using [Weights & Biases](https://wandb.ai/). Loss curves, validation curves, weights and gradient histograms, as well as predicted masks are logged to the platform. 156 | 157 | When launching a training, a link will be printed in the console. Click on it to go to your dashboard. If you have an existing W&B account, you can link it 158 | by setting the `WANDB_API_KEY` environment variable. If not, it will create an anonymous run which is automatically deleted after 7 days. 159 | 160 | 161 | ## Pretrained model 162 | A [pretrained model](https://github.com/milesial/Pytorch-UNet/releases/tag/v3.0) is available for the Carvana dataset. It can also be loaded from torch.hub: 163 | 164 | ```python 165 | net = torch.hub.load('milesial/Pytorch-UNet', 'unet_carvana', pretrained=True, scale=0.5) 166 | ``` 167 | Available scales are 0.5 and 1.0. 168 | 169 | ## Data 170 | The Carvana data is available on the [Kaggle website](https://www.kaggle.com/c/carvana-image-masking-challenge/data). 171 | 172 | You can also download it using the helper script: 173 | 174 | ``` 175 | bash scripts/download_data.sh 176 | ``` 177 | 178 | The input images and target masks should be in the `data/imgs` and `data/masks` folders respectively (note that the `imgs` and `masks` folder should not contain any sub-folder or any other files, due to the greedy data-loader). For Carvana, images are RGB and masks are black and white. 179 | 180 | You can use your own dataset as long as you make sure it is loaded properly in `utils/data_loading.py`. 181 | 182 | 183 | --- 184 | 185 | Original paper by Olaf Ronneberger, Philipp Fischer, Thomas Brox: 186 | 187 | [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597) 188 | 189 | ![network architecture](https://i.imgur.com/jeDVpqF.png) 190 | -------------------------------------------------------------------------------- /data/imgs/.keep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/milesial/Pytorch-UNet/21d7850f2af30a9695bbeea75f3136aa538cfc4a/data/imgs/.keep -------------------------------------------------------------------------------- /data/masks/.keep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/milesial/Pytorch-UNet/21d7850f2af30a9695bbeea75f3136aa538cfc4a/data/masks/.keep -------------------------------------------------------------------------------- /evaluate.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn.functional as F 3 | from tqdm import tqdm 4 | 5 | from utils.dice_score import multiclass_dice_coeff, dice_coeff 6 | 7 | 8 | @torch.inference_mode() 9 | def evaluate(net, dataloader, device, amp): 10 | net.eval() 11 | num_val_batches = len(dataloader) 12 | dice_score = 0 13 | 14 | # iterate over the validation set 15 | with torch.autocast(device.type if device.type != 'mps' else 'cpu', enabled=amp): 16 | for batch in tqdm(dataloader, total=num_val_batches, desc='Validation round', unit='batch', leave=False): 17 | image, mask_true = batch['image'], batch['mask'] 18 | 19 | # move images and labels to correct device and type 20 | image = image.to(device=device, dtype=torch.float32, memory_format=torch.channels_last) 21 | mask_true = mask_true.to(device=device, dtype=torch.long) 22 | 23 | # predict the mask 24 | mask_pred = net(image) 25 | 26 | if net.n_classes == 1: 27 | assert mask_true.min() >= 0 and mask_true.max() <= 1, 'True mask indices should be in [0, 1]' 28 | mask_pred = (F.sigmoid(mask_pred) > 0.5).float() 29 | # compute the Dice score 30 | dice_score += dice_coeff(mask_pred, mask_true, reduce_batch_first=False) 31 | else: 32 | assert mask_true.min() >= 0 and mask_true.max() < net.n_classes, 'True mask indices should be in [0, n_classes[' 33 | # convert to one-hot format 34 | mask_true = F.one_hot(mask_true, net.n_classes).permute(0, 3, 1, 2).float() 35 | mask_pred = F.one_hot(mask_pred.argmax(dim=1), net.n_classes).permute(0, 3, 1, 2).float() 36 | # compute the Dice score, ignoring background 37 | dice_score += multiclass_dice_coeff(mask_pred[:, 1:], mask_true[:, 1:], reduce_batch_first=False) 38 | 39 | net.train() 40 | return dice_score / max(num_val_batches, 1) 41 | -------------------------------------------------------------------------------- /hubconf.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from unet import UNet as _UNet 3 | 4 | def unet_carvana(pretrained=False, scale=0.5): 5 | """ 6 | UNet model trained on the Carvana dataset ( https://www.kaggle.com/c/carvana-image-masking-challenge/data ). 7 | Set the scale to 0.5 (50%) when predicting. 8 | """ 9 | net = _UNet(n_channels=3, n_classes=2, bilinear=False) 10 | if pretrained: 11 | if scale == 0.5: 12 | checkpoint = 'https://github.com/milesial/Pytorch-UNet/releases/download/v3.0/unet_carvana_scale0.5_epoch2.pth' 13 | elif scale == 1.0: 14 | checkpoint = 'https://github.com/milesial/Pytorch-UNet/releases/download/v3.0/unet_carvana_scale1.0_epoch2.pth' 15 | else: 16 | raise RuntimeError('Only 0.5 and 1.0 scales are available') 17 | state_dict = torch.hub.load_state_dict_from_url(checkpoint, progress=True) 18 | if 'mask_values' in state_dict: 19 | state_dict.pop('mask_values') 20 | net.load_state_dict(state_dict) 21 | 22 | return net 23 | 24 | -------------------------------------------------------------------------------- /predict.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import logging 3 | import os 4 | 5 | import numpy as np 6 | import torch 7 | import torch.nn.functional as F 8 | from PIL import Image 9 | from torchvision import transforms 10 | 11 | from utils.data_loading import BasicDataset 12 | from unet import UNet 13 | from utils.utils import plot_img_and_mask 14 | 15 | def predict_img(net, 16 | full_img, 17 | device, 18 | scale_factor=1, 19 | out_threshold=0.5): 20 | net.eval() 21 | img = torch.from_numpy(BasicDataset.preprocess(None, full_img, scale_factor, is_mask=False)) 22 | img = img.unsqueeze(0) 23 | img = img.to(device=device, dtype=torch.float32) 24 | 25 | with torch.no_grad(): 26 | output = net(img).cpu() 27 | output = F.interpolate(output, (full_img.size[1], full_img.size[0]), mode='bilinear') 28 | if net.n_classes > 1: 29 | mask = output.argmax(dim=1) 30 | else: 31 | mask = torch.sigmoid(output) > out_threshold 32 | 33 | return mask[0].long().squeeze().numpy() 34 | 35 | 36 | def get_args(): 37 | parser = argparse.ArgumentParser(description='Predict masks from input images') 38 | parser.add_argument('--model', '-m', default='MODEL.pth', metavar='FILE', 39 | help='Specify the file in which the model is stored') 40 | parser.add_argument('--input', '-i', metavar='INPUT', nargs='+', help='Filenames of input images', required=True) 41 | parser.add_argument('--output', '-o', metavar='OUTPUT', nargs='+', help='Filenames of output images') 42 | parser.add_argument('--viz', '-v', action='store_true', 43 | help='Visualize the images as they are processed') 44 | parser.add_argument('--no-save', '-n', action='store_true', help='Do not save the output masks') 45 | parser.add_argument('--mask-threshold', '-t', type=float, default=0.5, 46 | help='Minimum probability value to consider a mask pixel white') 47 | parser.add_argument('--scale', '-s', type=float, default=0.5, 48 | help='Scale factor for the input images') 49 | parser.add_argument('--bilinear', action='store_true', default=False, help='Use bilinear upsampling') 50 | parser.add_argument('--classes', '-c', type=int, default=2, help='Number of classes') 51 | 52 | return parser.parse_args() 53 | 54 | 55 | def get_output_filenames(args): 56 | def _generate_name(fn): 57 | return f'{os.path.splitext(fn)[0]}_OUT.png' 58 | 59 | return args.output or list(map(_generate_name, args.input)) 60 | 61 | 62 | def mask_to_image(mask: np.ndarray, mask_values): 63 | if isinstance(mask_values[0], list): 64 | out = np.zeros((mask.shape[-2], mask.shape[-1], len(mask_values[0])), dtype=np.uint8) 65 | elif mask_values == [0, 1]: 66 | out = np.zeros((mask.shape[-2], mask.shape[-1]), dtype=bool) 67 | else: 68 | out = np.zeros((mask.shape[-2], mask.shape[-1]), dtype=np.uint8) 69 | 70 | if mask.ndim == 3: 71 | mask = np.argmax(mask, axis=0) 72 | 73 | for i, v in enumerate(mask_values): 74 | out[mask == i] = v 75 | 76 | return Image.fromarray(out) 77 | 78 | 79 | if __name__ == '__main__': 80 | args = get_args() 81 | logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s') 82 | 83 | in_files = args.input 84 | out_files = get_output_filenames(args) 85 | 86 | net = UNet(n_channels=3, n_classes=args.classes, bilinear=args.bilinear) 87 | 88 | device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') 89 | logging.info(f'Loading model {args.model}') 90 | logging.info(f'Using device {device}') 91 | 92 | net.to(device=device) 93 | state_dict = torch.load(args.model, map_location=device) 94 | mask_values = state_dict.pop('mask_values', [0, 1]) 95 | net.load_state_dict(state_dict) 96 | 97 | logging.info('Model loaded!') 98 | 99 | for i, filename in enumerate(in_files): 100 | logging.info(f'Predicting image {filename} ...') 101 | img = Image.open(filename) 102 | 103 | mask = predict_img(net=net, 104 | full_img=img, 105 | scale_factor=args.scale, 106 | out_threshold=args.mask_threshold, 107 | device=device) 108 | 109 | if not args.no_save: 110 | out_filename = out_files[i] 111 | result = mask_to_image(mask, mask_values) 112 | result.save(out_filename) 113 | logging.info(f'Mask saved to {out_filename}') 114 | 115 | if args.viz: 116 | logging.info(f'Visualizing results for image {filename}, close to continue...') 117 | plot_img_and_mask(img, mask) 118 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | matplotlib==3.6.2 2 | numpy==1.23.5 3 | Pillow==9.3.0 4 | tqdm==4.64.1 5 | wandb==0.13.5 6 | -------------------------------------------------------------------------------- /scripts/download_data.bat: -------------------------------------------------------------------------------- 1 | @echo off 2 | setlocal enabledelayedexpansion 3 | 4 | if not exist "%userprofile%\.kaggle\kaggle.json" ( 5 | set /p USERNAME=Kaggle username: 6 | echo. 7 | set /p APIKEY=Kaggle API key: 8 | 9 | mkdir "%userprofile%\.kaggle" 10 | echo {"username":"!USERNAME!","key":"!APIKEY!"} > "%userprofile%\.kaggle\kaggle.json" 11 | attrib +R "%userprofile%\.kaggle\kaggle.json" 12 | ) 13 | 14 | pip install kaggle --upgrade 15 | 16 | kaggle competitions download -c carvana-image-masking-challenge -f train_hq.zip 17 | powershell Expand-Archive train_hq.zip -DestinationPath data\imgs 18 | move data\imgs\train_hq\* data\imgs\ 19 | rmdir /s /q data\imgs\train_hq 20 | del /q train_hq.zip 21 | 22 | kaggle competitions download -c carvana-image-masking-challenge -f train_masks.zip 23 | powershell Expand-Archive train_masks.zip -DestinationPath data\masks 24 | move data\masks\train_masks\* data\masks\ 25 | rmdir /s /q data\masks\train_masks 26 | del /q train_masks.zip 27 | 28 | exit /b 0 29 | -------------------------------------------------------------------------------- /scripts/download_data.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | if [[ ! -f ~/.kaggle/kaggle.json ]]; then 4 | echo -n "Kaggle username: " 5 | read USERNAME 6 | echo 7 | echo -n "Kaggle API key: " 8 | read APIKEY 9 | 10 | mkdir -p ~/.kaggle 11 | echo "{\"username\":\"$USERNAME\",\"key\":\"$APIKEY\"}" > ~/.kaggle/kaggle.json 12 | chmod 600 ~/.kaggle/kaggle.json 13 | fi 14 | 15 | pip install kaggle --upgrade 16 | 17 | kaggle competitions download -c carvana-image-masking-challenge -f train_hq.zip 18 | unzip train_hq.zip 19 | mv train_hq/* data/imgs/ 20 | rm -d train_hq 21 | rm train_hq.zip 22 | 23 | kaggle competitions download -c carvana-image-masking-challenge -f train_masks.zip 24 | unzip train_masks.zip 25 | mv train_masks/* data/masks/ 26 | rm -d train_masks 27 | rm train_masks.zip 28 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import logging 3 | import os 4 | import random 5 | import sys 6 | import torch 7 | import torch.nn as nn 8 | import torch.nn.functional as F 9 | import torchvision.transforms as transforms 10 | import torchvision.transforms.functional as TF 11 | from pathlib import Path 12 | from torch import optim 13 | from torch.utils.data import DataLoader, random_split 14 | from tqdm import tqdm 15 | 16 | import wandb 17 | from evaluate import evaluate 18 | from unet import UNet 19 | from utils.data_loading import BasicDataset, CarvanaDataset 20 | from utils.dice_score import dice_loss 21 | 22 | dir_img = Path('./data/imgs/') 23 | dir_mask = Path('./data/masks/') 24 | dir_checkpoint = Path('./checkpoints/') 25 | 26 | 27 | def train_model( 28 | model, 29 | device, 30 | epochs: int = 5, 31 | batch_size: int = 1, 32 | learning_rate: float = 1e-5, 33 | val_percent: float = 0.1, 34 | save_checkpoint: bool = True, 35 | img_scale: float = 0.5, 36 | amp: bool = False, 37 | weight_decay: float = 1e-8, 38 | momentum: float = 0.999, 39 | gradient_clipping: float = 1.0, 40 | ): 41 | # 1. Create dataset 42 | try: 43 | dataset = CarvanaDataset(dir_img, dir_mask, img_scale) 44 | except (AssertionError, RuntimeError, IndexError): 45 | dataset = BasicDataset(dir_img, dir_mask, img_scale) 46 | 47 | # 2. Split into train / validation partitions 48 | n_val = int(len(dataset) * val_percent) 49 | n_train = len(dataset) - n_val 50 | train_set, val_set = random_split(dataset, [n_train, n_val], generator=torch.Generator().manual_seed(0)) 51 | 52 | # 3. Create data loaders 53 | loader_args = dict(batch_size=batch_size, num_workers=os.cpu_count(), pin_memory=True) 54 | train_loader = DataLoader(train_set, shuffle=True, **loader_args) 55 | val_loader = DataLoader(val_set, shuffle=False, drop_last=True, **loader_args) 56 | 57 | # (Initialize logging) 58 | experiment = wandb.init(project='U-Net', resume='allow', anonymous='must') 59 | experiment.config.update( 60 | dict(epochs=epochs, batch_size=batch_size, learning_rate=learning_rate, 61 | val_percent=val_percent, save_checkpoint=save_checkpoint, img_scale=img_scale, amp=amp) 62 | ) 63 | 64 | logging.info(f'''Starting training: 65 | Epochs: {epochs} 66 | Batch size: {batch_size} 67 | Learning rate: {learning_rate} 68 | Training size: {n_train} 69 | Validation size: {n_val} 70 | Checkpoints: {save_checkpoint} 71 | Device: {device.type} 72 | Images scaling: {img_scale} 73 | Mixed Precision: {amp} 74 | ''') 75 | 76 | # 4. Set up the optimizer, the loss, the learning rate scheduler and the loss scaling for AMP 77 | optimizer = optim.RMSprop(model.parameters(), 78 | lr=learning_rate, weight_decay=weight_decay, momentum=momentum, foreach=True) 79 | scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'max', patience=5) # goal: maximize Dice score 80 | grad_scaler = torch.cuda.amp.GradScaler(enabled=amp) 81 | criterion = nn.CrossEntropyLoss() if model.n_classes > 1 else nn.BCEWithLogitsLoss() 82 | global_step = 0 83 | 84 | # 5. Begin training 85 | for epoch in range(1, epochs + 1): 86 | model.train() 87 | epoch_loss = 0 88 | with tqdm(total=n_train, desc=f'Epoch {epoch}/{epochs}', unit='img') as pbar: 89 | for batch in train_loader: 90 | images, true_masks = batch['image'], batch['mask'] 91 | 92 | assert images.shape[1] == model.n_channels, \ 93 | f'Network has been defined with {model.n_channels} input channels, ' \ 94 | f'but loaded images have {images.shape[1]} channels. Please check that ' \ 95 | 'the images are loaded correctly.' 96 | 97 | images = images.to(device=device, dtype=torch.float32, memory_format=torch.channels_last) 98 | true_masks = true_masks.to(device=device, dtype=torch.long) 99 | 100 | with torch.autocast(device.type if device.type != 'mps' else 'cpu', enabled=amp): 101 | masks_pred = model(images) 102 | if model.n_classes == 1: 103 | loss = criterion(masks_pred.squeeze(1), true_masks.float()) 104 | loss += dice_loss(F.sigmoid(masks_pred.squeeze(1)), true_masks.float(), multiclass=False) 105 | else: 106 | loss = criterion(masks_pred, true_masks) 107 | loss += dice_loss( 108 | F.softmax(masks_pred, dim=1).float(), 109 | F.one_hot(true_masks, model.n_classes).permute(0, 3, 1, 2).float(), 110 | multiclass=True 111 | ) 112 | 113 | optimizer.zero_grad(set_to_none=True) 114 | grad_scaler.scale(loss).backward() 115 | grad_scaler.unscale_(optimizer) 116 | torch.nn.utils.clip_grad_norm_(model.parameters(), gradient_clipping) 117 | grad_scaler.step(optimizer) 118 | grad_scaler.update() 119 | 120 | pbar.update(images.shape[0]) 121 | global_step += 1 122 | epoch_loss += loss.item() 123 | experiment.log({ 124 | 'train loss': loss.item(), 125 | 'step': global_step, 126 | 'epoch': epoch 127 | }) 128 | pbar.set_postfix(**{'loss (batch)': loss.item()}) 129 | 130 | # Evaluation round 131 | division_step = (n_train // (5 * batch_size)) 132 | if division_step > 0: 133 | if global_step % division_step == 0: 134 | histograms = {} 135 | for tag, value in model.named_parameters(): 136 | tag = tag.replace('/', '.') 137 | if not (torch.isinf(value) | torch.isnan(value)).any(): 138 | histograms['Weights/' + tag] = wandb.Histogram(value.data.cpu()) 139 | if not (torch.isinf(value.grad) | torch.isnan(value.grad)).any(): 140 | histograms['Gradients/' + tag] = wandb.Histogram(value.grad.data.cpu()) 141 | 142 | val_score = evaluate(model, val_loader, device, amp) 143 | scheduler.step(val_score) 144 | 145 | logging.info('Validation Dice score: {}'.format(val_score)) 146 | try: 147 | experiment.log({ 148 | 'learning rate': optimizer.param_groups[0]['lr'], 149 | 'validation Dice': val_score, 150 | 'images': wandb.Image(images[0].cpu()), 151 | 'masks': { 152 | 'true': wandb.Image(true_masks[0].float().cpu()), 153 | 'pred': wandb.Image(masks_pred.argmax(dim=1)[0].float().cpu()), 154 | }, 155 | 'step': global_step, 156 | 'epoch': epoch, 157 | **histograms 158 | }) 159 | except: 160 | pass 161 | 162 | if save_checkpoint: 163 | Path(dir_checkpoint).mkdir(parents=True, exist_ok=True) 164 | state_dict = model.state_dict() 165 | state_dict['mask_values'] = dataset.mask_values 166 | torch.save(state_dict, str(dir_checkpoint / 'checkpoint_epoch{}.pth'.format(epoch))) 167 | logging.info(f'Checkpoint {epoch} saved!') 168 | 169 | 170 | def get_args(): 171 | parser = argparse.ArgumentParser(description='Train the UNet on images and target masks') 172 | parser.add_argument('--epochs', '-e', metavar='E', type=int, default=5, help='Number of epochs') 173 | parser.add_argument('--batch-size', '-b', dest='batch_size', metavar='B', type=int, default=1, help='Batch size') 174 | parser.add_argument('--learning-rate', '-l', metavar='LR', type=float, default=1e-5, 175 | help='Learning rate', dest='lr') 176 | parser.add_argument('--load', '-f', type=str, default=False, help='Load model from a .pth file') 177 | parser.add_argument('--scale', '-s', type=float, default=0.5, help='Downscaling factor of the images') 178 | parser.add_argument('--validation', '-v', dest='val', type=float, default=10.0, 179 | help='Percent of the data that is used as validation (0-100)') 180 | parser.add_argument('--amp', action='store_true', default=False, help='Use mixed precision') 181 | parser.add_argument('--bilinear', action='store_true', default=False, help='Use bilinear upsampling') 182 | parser.add_argument('--classes', '-c', type=int, default=2, help='Number of classes') 183 | 184 | return parser.parse_args() 185 | 186 | 187 | if __name__ == '__main__': 188 | args = get_args() 189 | 190 | logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s') 191 | device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') 192 | logging.info(f'Using device {device}') 193 | 194 | # Change here to adapt to your data 195 | # n_channels=3 for RGB images 196 | # n_classes is the number of probabilities you want to get per pixel 197 | model = UNet(n_channels=3, n_classes=args.classes, bilinear=args.bilinear) 198 | model = model.to(memory_format=torch.channels_last) 199 | 200 | logging.info(f'Network:\n' 201 | f'\t{model.n_channels} input channels\n' 202 | f'\t{model.n_classes} output channels (classes)\n' 203 | f'\t{"Bilinear" if model.bilinear else "Transposed conv"} upscaling') 204 | 205 | if args.load: 206 | state_dict = torch.load(args.load, map_location=device) 207 | del state_dict['mask_values'] 208 | model.load_state_dict(state_dict) 209 | logging.info(f'Model loaded from {args.load}') 210 | 211 | model.to(device=device) 212 | try: 213 | train_model( 214 | model=model, 215 | epochs=args.epochs, 216 | batch_size=args.batch_size, 217 | learning_rate=args.lr, 218 | device=device, 219 | img_scale=args.scale, 220 | val_percent=args.val / 100, 221 | amp=args.amp 222 | ) 223 | except torch.cuda.OutOfMemoryError: 224 | logging.error('Detected OutOfMemoryError! ' 225 | 'Enabling checkpointing to reduce memory usage, but this slows down training. ' 226 | 'Consider enabling AMP (--amp) for fast and memory efficient training') 227 | torch.cuda.empty_cache() 228 | model.use_checkpointing() 229 | train_model( 230 | model=model, 231 | epochs=args.epochs, 232 | batch_size=args.batch_size, 233 | learning_rate=args.lr, 234 | device=device, 235 | img_scale=args.scale, 236 | val_percent=args.val / 100, 237 | amp=args.amp 238 | ) 239 | -------------------------------------------------------------------------------- /unet/__init__.py: -------------------------------------------------------------------------------- 1 | from .unet_model import UNet 2 | -------------------------------------------------------------------------------- /unet/unet_model.py: -------------------------------------------------------------------------------- 1 | """ Full assembly of the parts to form the complete network """ 2 | 3 | from .unet_parts import * 4 | 5 | 6 | class UNet(nn.Module): 7 | def __init__(self, n_channels, n_classes, bilinear=False): 8 | super(UNet, self).__init__() 9 | self.n_channels = n_channels 10 | self.n_classes = n_classes 11 | self.bilinear = bilinear 12 | 13 | self.inc = (DoubleConv(n_channels, 64)) 14 | self.down1 = (Down(64, 128)) 15 | self.down2 = (Down(128, 256)) 16 | self.down3 = (Down(256, 512)) 17 | factor = 2 if bilinear else 1 18 | self.down4 = (Down(512, 1024 // factor)) 19 | self.up1 = (Up(1024, 512 // factor, bilinear)) 20 | self.up2 = (Up(512, 256 // factor, bilinear)) 21 | self.up3 = (Up(256, 128 // factor, bilinear)) 22 | self.up4 = (Up(128, 64, bilinear)) 23 | self.outc = (OutConv(64, n_classes)) 24 | 25 | def forward(self, x): 26 | x1 = self.inc(x) 27 | x2 = self.down1(x1) 28 | x3 = self.down2(x2) 29 | x4 = self.down3(x3) 30 | x5 = self.down4(x4) 31 | x = self.up1(x5, x4) 32 | x = self.up2(x, x3) 33 | x = self.up3(x, x2) 34 | x = self.up4(x, x1) 35 | logits = self.outc(x) 36 | return logits 37 | 38 | def use_checkpointing(self): 39 | self.inc = torch.utils.checkpoint(self.inc) 40 | self.down1 = torch.utils.checkpoint(self.down1) 41 | self.down2 = torch.utils.checkpoint(self.down2) 42 | self.down3 = torch.utils.checkpoint(self.down3) 43 | self.down4 = torch.utils.checkpoint(self.down4) 44 | self.up1 = torch.utils.checkpoint(self.up1) 45 | self.up2 = torch.utils.checkpoint(self.up2) 46 | self.up3 = torch.utils.checkpoint(self.up3) 47 | self.up4 = torch.utils.checkpoint(self.up4) 48 | self.outc = torch.utils.checkpoint(self.outc) -------------------------------------------------------------------------------- /unet/unet_parts.py: -------------------------------------------------------------------------------- 1 | """ Parts of the U-Net model """ 2 | 3 | import torch 4 | import torch.nn as nn 5 | import torch.nn.functional as F 6 | 7 | 8 | class DoubleConv(nn.Module): 9 | """(convolution => [BN] => ReLU) * 2""" 10 | 11 | def __init__(self, in_channels, out_channels, mid_channels=None): 12 | super().__init__() 13 | if not mid_channels: 14 | mid_channels = out_channels 15 | self.double_conv = nn.Sequential( 16 | nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False), 17 | nn.BatchNorm2d(mid_channels), 18 | nn.ReLU(inplace=True), 19 | nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False), 20 | nn.BatchNorm2d(out_channels), 21 | nn.ReLU(inplace=True) 22 | ) 23 | 24 | def forward(self, x): 25 | return self.double_conv(x) 26 | 27 | 28 | class Down(nn.Module): 29 | """Downscaling with maxpool then double conv""" 30 | 31 | def __init__(self, in_channels, out_channels): 32 | super().__init__() 33 | self.maxpool_conv = nn.Sequential( 34 | nn.MaxPool2d(2), 35 | DoubleConv(in_channels, out_channels) 36 | ) 37 | 38 | def forward(self, x): 39 | return self.maxpool_conv(x) 40 | 41 | 42 | class Up(nn.Module): 43 | """Upscaling then double conv""" 44 | 45 | def __init__(self, in_channels, out_channels, bilinear=True): 46 | super().__init__() 47 | 48 | # if bilinear, use the normal convolutions to reduce the number of channels 49 | if bilinear: 50 | self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) 51 | self.conv = DoubleConv(in_channels, out_channels, in_channels // 2) 52 | else: 53 | self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2) 54 | self.conv = DoubleConv(in_channels, out_channels) 55 | 56 | def forward(self, x1, x2): 57 | x1 = self.up(x1) 58 | # input is CHW 59 | diffY = x2.size()[2] - x1.size()[2] 60 | diffX = x2.size()[3] - x1.size()[3] 61 | 62 | x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2, 63 | diffY // 2, diffY - diffY // 2]) 64 | # if you have padding issues, see 65 | # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a 66 | # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd 67 | x = torch.cat([x2, x1], dim=1) 68 | return self.conv(x) 69 | 70 | 71 | class OutConv(nn.Module): 72 | def __init__(self, in_channels, out_channels): 73 | super(OutConv, self).__init__() 74 | self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1) 75 | 76 | def forward(self, x): 77 | return self.conv(x) 78 | -------------------------------------------------------------------------------- /utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/milesial/Pytorch-UNet/21d7850f2af30a9695bbeea75f3136aa538cfc4a/utils/__init__.py -------------------------------------------------------------------------------- /utils/data_loading.py: -------------------------------------------------------------------------------- 1 | import logging 2 | import numpy as np 3 | import torch 4 | from PIL import Image 5 | from functools import lru_cache 6 | from functools import partial 7 | from itertools import repeat 8 | from multiprocessing import Pool 9 | from os import listdir 10 | from os.path import splitext, isfile, join 11 | from pathlib import Path 12 | from torch.utils.data import Dataset 13 | from tqdm import tqdm 14 | 15 | 16 | def load_image(filename): 17 | ext = splitext(filename)[1] 18 | if ext == '.npy': 19 | return Image.fromarray(np.load(filename)) 20 | elif ext in ['.pt', '.pth']: 21 | return Image.fromarray(torch.load(filename).numpy()) 22 | else: 23 | return Image.open(filename) 24 | 25 | 26 | def unique_mask_values(idx, mask_dir, mask_suffix): 27 | mask_file = list(mask_dir.glob(idx + mask_suffix + '.*'))[0] 28 | mask = np.asarray(load_image(mask_file)) 29 | if mask.ndim == 2: 30 | return np.unique(mask) 31 | elif mask.ndim == 3: 32 | mask = mask.reshape(-1, mask.shape[-1]) 33 | return np.unique(mask, axis=0) 34 | else: 35 | raise ValueError(f'Loaded masks should have 2 or 3 dimensions, found {mask.ndim}') 36 | 37 | 38 | class BasicDataset(Dataset): 39 | def __init__(self, images_dir: str, mask_dir: str, scale: float = 1.0, mask_suffix: str = ''): 40 | self.images_dir = Path(images_dir) 41 | self.mask_dir = Path(mask_dir) 42 | assert 0 < scale <= 1, 'Scale must be between 0 and 1' 43 | self.scale = scale 44 | self.mask_suffix = mask_suffix 45 | 46 | self.ids = [splitext(file)[0] for file in listdir(images_dir) if isfile(join(images_dir, file)) and not file.startswith('.')] 47 | if not self.ids: 48 | raise RuntimeError(f'No input file found in {images_dir}, make sure you put your images there') 49 | 50 | logging.info(f'Creating dataset with {len(self.ids)} examples') 51 | logging.info('Scanning mask files to determine unique values') 52 | with Pool() as p: 53 | unique = list(tqdm( 54 | p.imap(partial(unique_mask_values, mask_dir=self.mask_dir, mask_suffix=self.mask_suffix), self.ids), 55 | total=len(self.ids) 56 | )) 57 | 58 | self.mask_values = list(sorted(np.unique(np.concatenate(unique), axis=0).tolist())) 59 | logging.info(f'Unique mask values: {self.mask_values}') 60 | 61 | def __len__(self): 62 | return len(self.ids) 63 | 64 | @staticmethod 65 | def preprocess(mask_values, pil_img, scale, is_mask): 66 | w, h = pil_img.size 67 | newW, newH = int(scale * w), int(scale * h) 68 | assert newW > 0 and newH > 0, 'Scale is too small, resized images would have no pixel' 69 | pil_img = pil_img.resize((newW, newH), resample=Image.NEAREST if is_mask else Image.BICUBIC) 70 | img = np.asarray(pil_img) 71 | 72 | if is_mask: 73 | mask = np.zeros((newH, newW), dtype=np.int64) 74 | for i, v in enumerate(mask_values): 75 | if img.ndim == 2: 76 | mask[img == v] = i 77 | else: 78 | mask[(img == v).all(-1)] = i 79 | 80 | return mask 81 | 82 | else: 83 | if img.ndim == 2: 84 | img = img[np.newaxis, ...] 85 | else: 86 | img = img.transpose((2, 0, 1)) 87 | 88 | if (img > 1).any(): 89 | img = img / 255.0 90 | 91 | return img 92 | 93 | def __getitem__(self, idx): 94 | name = self.ids[idx] 95 | mask_file = list(self.mask_dir.glob(name + self.mask_suffix + '.*')) 96 | img_file = list(self.images_dir.glob(name + '.*')) 97 | 98 | assert len(img_file) == 1, f'Either no image or multiple images found for the ID {name}: {img_file}' 99 | assert len(mask_file) == 1, f'Either no mask or multiple masks found for the ID {name}: {mask_file}' 100 | mask = load_image(mask_file[0]) 101 | img = load_image(img_file[0]) 102 | 103 | assert img.size == mask.size, \ 104 | f'Image and mask {name} should be the same size, but are {img.size} and {mask.size}' 105 | 106 | img = self.preprocess(self.mask_values, img, self.scale, is_mask=False) 107 | mask = self.preprocess(self.mask_values, mask, self.scale, is_mask=True) 108 | 109 | return { 110 | 'image': torch.as_tensor(img.copy()).float().contiguous(), 111 | 'mask': torch.as_tensor(mask.copy()).long().contiguous() 112 | } 113 | 114 | 115 | class CarvanaDataset(BasicDataset): 116 | def __init__(self, images_dir, mask_dir, scale=1): 117 | super().__init__(images_dir, mask_dir, scale, mask_suffix='_mask') 118 | -------------------------------------------------------------------------------- /utils/dice_score.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import Tensor 3 | 4 | 5 | def dice_coeff(input: Tensor, target: Tensor, reduce_batch_first: bool = False, epsilon: float = 1e-6): 6 | # Average of Dice coefficient for all batches, or for a single mask 7 | assert input.size() == target.size() 8 | assert input.dim() == 3 or not reduce_batch_first 9 | 10 | sum_dim = (-1, -2) if input.dim() == 2 or not reduce_batch_first else (-1, -2, -3) 11 | 12 | inter = 2 * (input * target).sum(dim=sum_dim) 13 | sets_sum = input.sum(dim=sum_dim) + target.sum(dim=sum_dim) 14 | sets_sum = torch.where(sets_sum == 0, inter, sets_sum) 15 | 16 | dice = (inter + epsilon) / (sets_sum + epsilon) 17 | return dice.mean() 18 | 19 | 20 | def multiclass_dice_coeff(input: Tensor, target: Tensor, reduce_batch_first: bool = False, epsilon: float = 1e-6): 21 | # Average of Dice coefficient for all classes 22 | return dice_coeff(input.flatten(0, 1), target.flatten(0, 1), reduce_batch_first, epsilon) 23 | 24 | 25 | def dice_loss(input: Tensor, target: Tensor, multiclass: bool = False): 26 | # Dice loss (objective to minimize) between 0 and 1 27 | fn = multiclass_dice_coeff if multiclass else dice_coeff 28 | return 1 - fn(input, target, reduce_batch_first=True) 29 | -------------------------------------------------------------------------------- /utils/utils.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | 3 | 4 | def plot_img_and_mask(img, mask): 5 | classes = mask.max() + 1 6 | fig, ax = plt.subplots(1, classes + 1) 7 | ax[0].set_title('Input image') 8 | ax[0].imshow(img) 9 | for i in range(classes): 10 | ax[i + 1].set_title(f'Mask (class {i + 1})') 11 | ax[i + 1].imshow(mask == i) 12 | plt.xticks([]), plt.yticks([]) 13 | plt.show() 14 | --------------------------------------------------------------------------------