From patchwork Sat Nov 19 23:14:40 2022 Content-Type: text/plain; charset="utf-8" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit X-Patchwork-Submitter: Liliana Marie Prikler X-Patchwork-Id: 44874 Return-Path: X-Original-To: patchwork@mira.cbaines.net Delivered-To: patchwork@mira.cbaines.net Received: by mira.cbaines.net (Postfix, from userid 113) id D2C1727BBEC; Sat, 26 Nov 2022 12:14:07 +0000 (GMT) X-Spam-Checker-Version: SpamAssassin 3.4.6 (2021-04-09) on mira.cbaines.net X-Spam-Level: X-Spam-Status: No, score=-3.7 required=5.0 tests=BAYES_00,DKIM_ADSP_CUSTOM_MED, DKIM_INVALID,DKIM_SIGNED,FREEMAIL_FROM,MAILING_LIST_MULTI, RCVD_IN_MSPIKE_H2,SPF_HELO_PASS autolearn=unavailable autolearn_force=no version=3.4.6 Received: from lists.gnu.org (lists.gnu.org [209.51.188.17]) by mira.cbaines.net (Postfix) with ESMTPS id 04F7027BBE9 for ; Sat, 26 Nov 2022 12:14:06 +0000 (GMT) Received: from localhost ([::1] helo=lists1p.gnu.org) by lists.gnu.org with esmtp (Exim 4.90_1) (envelope-from ) id 1oyu3Q-0004qL-Mc; Sat, 26 Nov 2022 07:13:08 -0500 Received: from eggs.gnu.org ([2001:470:142:3::10]) by lists.gnu.org with esmtps (TLS1.2:ECDHE_RSA_AES_256_GCM_SHA384:256) (Exim 4.90_1) (envelope-from ) id 1oyu3L-0004pU-4Y for guix-patches@gnu.org; Sat, 26 Nov 2022 07:13:03 -0500 Received: from debbugs.gnu.org ([209.51.188.43]) by eggs.gnu.org with esmtps (TLS1.2:ECDHE_RSA_AES_128_GCM_SHA256:128) (Exim 4.90_1) (envelope-from ) id 1oyu3K-0005L4-SV for guix-patches@gnu.org; Sat, 26 Nov 2022 07:13:02 -0500 Received: from Debian-debbugs by debbugs.gnu.org with local (Exim 4.84_2) (envelope-from ) id 1oyu3J-000184-LS for guix-patches@gnu.org; Sat, 26 Nov 2022 07:13:01 -0500 X-Loop: help-debbugs@gnu.org Subject: [bug#59607] [PATCH 1/8] gnu: Add ncnn. 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[85.127.52.93]) by smtp.gmail.com with ESMTPSA id az26-20020a05600c601a00b003d01b84e9b2sm8695059wmb.27.2022.11.26.04.11.52 for <59607@debbugs.gnu.org> (version=TLS1_3 cipher=TLS_AES_256_GCM_SHA384 bits=256/256); Sat, 26 Nov 2022 04:11:53 -0800 (PST) From: Liliana Marie Prikler Date: Sun, 20 Nov 2022 00:14:40 +0100 Message-ID: MIME-Version: 1.0 X-BeenThere: debbugs-submit@debbugs.gnu.org X-Mailman-Version: 2.1.18 Precedence: list X-BeenThere: guix-patches@gnu.org List-Id: List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , Errors-To: guix-patches-bounces+patchwork=mira.cbaines.net@gnu.org Sender: guix-patches-bounces+patchwork=mira.cbaines.net@gnu.org X-getmail-retrieved-from-mailbox: Patches * gnu/packages/machine-learning.scm (ncnn): New variable. --- gnu/packages/machine-learning.scm | 32 +++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) diff --git a/gnu/packages/machine-learning.scm b/gnu/packages/machine-learning.scm index fbc06f96b6..e984e3004b 100644 --- a/gnu/packages/machine-learning.scm +++ b/gnu/packages/machine-learning.scm @@ -102,6 +102,7 @@ (define-module (gnu packages machine-learning) #:use-module (gnu packages swig) #:use-module (gnu packages tls) #:use-module (gnu packages video) + #:use-module (gnu packages vulkan) #:use-module (gnu packages web) #:use-module (gnu packages xml) #:use-module (gnu packages xdisorg) @@ -749,6 +750,37 @@ (define (delete-ifdefs file) in terms of new algorithms.") (license license:gpl3+))) +(define-public ncnn + (package + (name "ncnn") + (version "20220729") + (source (origin + (method git-fetch) + (uri (git-reference + (url "https://github.com/Tencent/ncnn") + (commit version))) + (file-name (git-file-name name version)) + (sha256 + (base32 "02na1crxph8m3sqb1c32v83ppxjcmaxyncql89q5mf9ggddmx5c5")))) + (build-system cmake-build-system) + (arguments + (list #:configure-flags + #~(list "-DNCNN_AVX=OFF" + "-DNCNN_BUILD_TESTS=TRUE" + "-DNCNN_SYSTEM_GLSLANG=ON" + (string-append "-DGLSLANG_TARGET_DIR=" + #$(this-package-input "glslang") + "/lib/cmake") + "-DNCNN_VULKAN=ON") + #:tests? #f)) ; XXX: half of the tests fail + (inputs (list glslang vulkan-headers vulkan-loader)) + (native-inputs (list protobuf)) + (home-page "https://github.com/Tencent/ncnn") + (synopsis "Neural network for mobile platforms") + (description "NCNN is a framework for building neural networks written in +C++. It supports parallel computing as well as GPU acceleration via Vulkan.") + (license license:bsd-3))) + (define-public onnx (package (name "onnx") From patchwork Sun Nov 20 01:16:54 2022 Content-Type: text/plain; charset="utf-8" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit X-Patchwork-Submitter: Liliana Marie Prikler X-Patchwork-Id: 44873 Return-Path: X-Original-To: patchwork@mira.cbaines.net Delivered-To: patchwork@mira.cbaines.net Received: by mira.cbaines.net (Postfix, from userid 113) id 3B12627BBEC; Sat, 26 Nov 2022 12:14:01 +0000 (GMT) X-Spam-Checker-Version: SpamAssassin 3.4.6 (2021-04-09) on mira.cbaines.net X-Spam-Level: X-Spam-Status: No, score=-3.7 required=5.0 tests=BAYES_00,DKIM_ADSP_CUSTOM_MED, DKIM_INVALID,DKIM_SIGNED,FREEMAIL_FROM,MAILING_LIST_MULTI, RCVD_IN_MSPIKE_H2,SPF_HELO_PASS autolearn=unavailable autolearn_force=no version=3.4.6 Received: from lists.gnu.org (lists.gnu.org [209.51.188.17]) by mira.cbaines.net (Postfix) with ESMTPS id 3D03727BBE9 for ; Sat, 26 Nov 2022 12:13:59 +0000 (GMT) Received: from localhost ([::1] helo=lists1p.gnu.org) by lists.gnu.org with esmtp (Exim 4.90_1) (envelope-from ) id 1oyu3Y-0004rh-DO; Sat, 26 Nov 2022 07:13:16 -0500 Received: from eggs.gnu.org ([2001:470:142:3::10]) by lists.gnu.org with esmtps (TLS1.2:ECDHE_RSA_AES_256_GCM_SHA384:256) (Exim 4.90_1) (envelope-from ) id 1oyu3K-0004pF-FK for guix-patches@gnu.org; Sat, 26 Nov 2022 07:13:02 -0500 Received: from debbugs.gnu.org ([209.51.188.43]) by eggs.gnu.org with esmtps (TLS1.2:ECDHE_RSA_AES_128_GCM_SHA256:128) (Exim 4.90_1) (envelope-from ) id 1oyu3K-0005L6-6s for guix-patches@gnu.org; Sat, 26 Nov 2022 07:13:02 -0500 Received: from Debian-debbugs by debbugs.gnu.org with local (Exim 4.84_2) (envelope-from ) id 1oyu3K-00018D-33 for guix-patches@gnu.org; Sat, 26 Nov 2022 07:13:02 -0500 X-Loop: help-debbugs@gnu.org Subject: [bug#59607] [PATCH 2/8] gnu: Add real-esrgan-ncnn. 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[85.127.52.93]) by smtp.gmail.com with ESMTPSA id az26-20020a05600c601a00b003d01b84e9b2sm8695059wmb.27.2022.11.26.04.11.53 for <59607@debbugs.gnu.org> (version=TLS1_3 cipher=TLS_AES_256_GCM_SHA384 bits=256/256); Sat, 26 Nov 2022 04:11:53 -0800 (PST) From: Liliana Marie Prikler Date: Sun, 20 Nov 2022 02:16:54 +0100 Message-ID: <3823ee69ed8ec08192f0c2ebc26fe8b3d399b381.camel@gmail.com> MIME-Version: 1.0 X-BeenThere: debbugs-submit@debbugs.gnu.org X-Mailman-Version: 2.1.18 Precedence: list X-BeenThere: guix-patches@gnu.org List-Id: List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , Errors-To: guix-patches-bounces+patchwork=mira.cbaines.net@gnu.org Sender: guix-patches-bounces+patchwork=mira.cbaines.net@gnu.org X-getmail-retrieved-from-mailbox: Patches * gnu/packages/machine-learning.scm (real-esrgan-ncnn): New variable. --- gnu/packages/machine-learning.scm | 44 ++++ ...real-resgan-ncnn-simplify-model-path.patch | 195 ++++++++++++++++++ 2 files changed, 239 insertions(+) create mode 100644 gnu/packages/patches/real-resgan-ncnn-simplify-model-path.patch diff --git a/gnu/packages/machine-learning.scm b/gnu/packages/machine-learning.scm index e984e3004b..0566f4bd69 100644 --- a/gnu/packages/machine-learning.scm +++ b/gnu/packages/machine-learning.scm @@ -781,6 +781,50 @@ (define-public ncnn C++. It supports parallel computing as well as GPU acceleration via Vulkan.") (license license:bsd-3))) +(define-public real-esrgan-ncnn + (package + (name "real-esrgan-ncnn") + (version "0.2.0") + (source (origin + (method git-fetch) + (uri (git-reference + (url "https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan") + (commit (string-append "v" version)))) + (file-name (git-file-name name version)) + (patches + (search-patches + "real-resgan-ncnn-simplify-model-path.patch")) + (sha256 + (base32 "1hlrq8b4848vgj2shcxz68d98p9wd5mf619v5d04pwg40s85zqqp")))) + (build-system cmake-build-system) + (arguments + (list #:tests? #f ; No tests + #:configure-flags + #~(list "-DUSE_SYSTEM_NCNN=TRUE" + "-DUSE_SYSTEM_WEBP=TRUE" + (string-append "-DGLSLANG_TARGET_DIR=" + #$(this-package-input "glslang") + "/lib/cmake")) + #:phases #~(modify-phases %standard-phases + (add-after 'unpack 'chdir + (lambda _ + (chdir "src"))) + (replace 'install + (lambda* (#:key outputs #:allow-other-keys) + (let ((bin (string-append (assoc-ref outputs "out") + "/bin"))) + (mkdir-p bin) + (install-file "realesrgan-ncnn-vulkan" bin))))))) + (inputs (list glslang libwebp ncnn vulkan-headers vulkan-loader)) + (home-page "https://github.com/xinntao/Real-ESRGAN") + (synopsis "Restore low-resolution images") + (description "Real-ESRGAN is a @acronym{GAN, Generative Adversarial Network} +aiming to restore low-resolution images. The techniques used are described in +the paper 'Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure +Synthetic Data' by Xintao Wang, Liangbin Xie, Chao Dong, and Ying Shan. +This package provides an implementation built on top of ncnn.") + (license license:expat))) + (define-public onnx (package (name "onnx") diff --git a/gnu/packages/patches/real-resgan-ncnn-simplify-model-path.patch b/gnu/packages/patches/real-resgan-ncnn-simplify-model-path.patch new file mode 100644 index 0000000000..9a02269718 --- /dev/null +++ b/gnu/packages/patches/real-resgan-ncnn-simplify-model-path.patch @@ -0,0 +1,195 @@ +diff --git a/src/main.cpp b/src/main.cpp +index ebe0e62..ddfb742 100644 +--- a/src/main.cpp ++++ b/src/main.cpp +@@ -109,8 +109,7 @@ static void print_usage() + fprintf(stderr, " -o output-path output image path (jpg/png/webp) or directory\n"); + fprintf(stderr, " -s scale upscale ratio (can be 2, 3, 4. default=4)\n"); + fprintf(stderr, " -t tile-size tile size (>=32/0=auto, default=0) can be 0,0,0 for multi-gpu\n"); +- fprintf(stderr, " -m model-path folder path to the pre-trained models. default=models\n"); +- fprintf(stderr, " -n model-name model name (default=realesr-animevideov3, can be realesr-animevideov3 | realesrgan-x4plus | realesrgan-x4plus-anime | realesrnet-x4plus)\n"); ++ fprintf(stderr, " -m model-path model and parameter file name (sans .bin and .param extension)\n"); + fprintf(stderr, " -g gpu-id gpu device to use (default=auto) can be 0,1,2 for multi-gpu\n"); + fprintf(stderr, " -j load:proc:save thread count for load/proc/save (default=1:2:2) can be 1:2,2,2:2 for multi-gpu\n"); + fprintf(stderr, " -x enable tta mode\n"); +@@ -438,8 +437,7 @@ int main(int argc, char** argv) + path_t outputpath; + int scale = 4; + std::vector tilesize; +- path_t model = PATHSTR("models"); +- path_t modelname = PATHSTR("realesr-animevideov3"); ++ path_t model = PATHSTR(""); + std::vector gpuid; + int jobs_load = 1; + std::vector jobs_proc; +@@ -451,7 +449,7 @@ int main(int argc, char** argv) + #if _WIN32 + setlocale(LC_ALL, ""); + wchar_t opt; +- while ((opt = getopt(argc, argv, L"i:o:s:t:m:n:g:j:f:vxh")) != (wchar_t)-1) ++ while ((opt = getopt(argc, argv, L"i:o:t:m:g:j:f:vxh")) != (wchar_t)-1) + { + switch (opt) + { +@@ -461,18 +459,12 @@ int main(int argc, char** argv) + case L'o': + outputpath = optarg; + break; +- case L's': +- scale = _wtoi(optarg); +- break; + case L't': + tilesize = parse_optarg_int_array(optarg); + break; + case L'm': + model = optarg; + break; +- case L'n': +- modelname = optarg; +- break; + case L'g': + gpuid = parse_optarg_int_array(optarg); + break; +@@ -497,7 +489,7 @@ int main(int argc, char** argv) + } + #else // _WIN32 + int opt; +- while ((opt = getopt(argc, argv, "i:o:s:t:m:n:g:j:f:vxh")) != -1) ++ while ((opt = getopt(argc, argv, "i:o:t:m:g:j:f:vxh")) != -1) + { + switch (opt) + { +@@ -507,18 +499,12 @@ int main(int argc, char** argv) + case 'o': + outputpath = optarg; + break; +- case 's': +- scale = atoi(optarg); +- break; + case 't': + tilesize = parse_optarg_int_array(optarg); + break; + case 'm': + model = optarg; + break; +- case 'n': +- modelname = optarg; +- break; + case 'g': + gpuid = parse_optarg_int_array(optarg); + break; +@@ -549,6 +535,12 @@ int main(int argc, char** argv) + return -1; + } + ++ if (model.empty()) ++ { ++ fprintf(stderr, "no model given\n"); ++ return -1; ++ } ++ + if (tilesize.size() != (gpuid.empty() ? 1 : gpuid.size()) && !tilesize.empty()) + { + fprintf(stderr, "invalid tilesize argument\n"); +@@ -671,61 +663,17 @@ int main(int argc, char** argv) + } + } + +- int prepadding = 0; +- +- if (model.find(PATHSTR("models")) != path_t::npos +- || model.find(PATHSTR("models2")) != path_t::npos) +- { +- prepadding = 10; +- } +- else +- { +- fprintf(stderr, "unknown model dir type\n"); +- return -1; +- } ++ int prepadding = 10; + +- // if (modelname.find(PATHSTR("realesrgan-x4plus")) != path_t::npos +- // || modelname.find(PATHSTR("realesrnet-x4plus")) != path_t::npos +- // || modelname.find(PATHSTR("esrgan-x4")) != path_t::npos) +- // {} +- // else +- // { +- // fprintf(stderr, "unknown model name\n"); +- // return -1; +- // } + + #if _WIN32 +- wchar_t parampath[256]; +- wchar_t modelpath[256]; +- +- if (modelname == PATHSTR("realesr-animevideov3")) +- { +- swprintf(parampath, 256, L"%s/%s-x%s.param", model.c_str(), modelname.c_str(), std::to_string(scale)); +- swprintf(modelpath, 256, L"%s/%s-x%s.bin", model.c_str(), modelname.c_str(), std::to_string(scale)); +- } +- else{ +- swprintf(parampath, 256, L"%s/%s.param", model.c_str(), modelname.c_str()); +- swprintf(modelpath, 256, L"%s/%s.bin", model.c_str(), modelname.c_str()); +- } +- ++ path_t parampath = model + L".param"; ++ path_t modelpath = model + L".bin"; + #else +- char parampath[256]; +- char modelpath[256]; +- +- if (modelname == PATHSTR("realesr-animevideov3")) +- { +- sprintf(parampath, "%s/%s-x%s.param", model.c_str(), modelname.c_str(), std::to_string(scale).c_str()); +- sprintf(modelpath, "%s/%s-x%s.bin", model.c_str(), modelname.c_str(), std::to_string(scale).c_str()); +- } +- else{ +- sprintf(parampath, "%s/%s.param", model.c_str(), modelname.c_str()); +- sprintf(modelpath, "%s/%s.bin", model.c_str(), modelname.c_str()); +- } ++ path_t parampath = model + ".param"; ++ path_t modelpath = model + ".bin"; + #endif + +- path_t paramfullpath = sanitize_filepath(parampath); +- path_t modelfullpath = sanitize_filepath(modelpath); +- + #if _WIN32 + CoInitializeEx(NULL, COINIT_MULTITHREADED); + #endif +@@ -781,17 +729,14 @@ int main(int argc, char** argv) + uint32_t heap_budget = ncnn::get_gpu_device(gpuid[i])->get_heap_budget(); + + // more fine-grained tilesize policy here +- if (model.find(PATHSTR("models")) != path_t::npos) +- { +- if (heap_budget > 1900) +- tilesize[i] = 200; +- else if (heap_budget > 550) +- tilesize[i] = 100; +- else if (heap_budget > 190) +- tilesize[i] = 64; +- else +- tilesize[i] = 32; +- } ++ if (heap_budget > 1900) ++ tilesize[i] = 200; ++ else if (heap_budget > 550) ++ tilesize[i] = 100; ++ else if (heap_budget > 190) ++ tilesize[i] = 64; ++ else ++ tilesize[i] = 32; + } + + { +@@ -801,7 +746,7 @@ int main(int argc, char** argv) + { + realesrgan[i] = new RealESRGAN(gpuid[i], tta_mode); + +- realesrgan[i]->load(paramfullpath, modelfullpath); ++ realesrgan[i]->load(parampath, modelpath); + + realesrgan[i]->scale = scale; + realesrgan[i]->tilesize = tilesize[i]; From patchwork Sun Nov 20 09:10:55 2022 Content-Type: text/plain; charset="utf-8" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit X-Patchwork-Submitter: Liliana Marie Prikler X-Patchwork-Id: 44872 Return-Path: X-Original-To: patchwork@mira.cbaines.net Delivered-To: patchwork@mira.cbaines.net Received: by mira.cbaines.net (Postfix, from userid 113) id 8BB8F27BBE9; Sat, 26 Nov 2022 12:13:55 +0000 (GMT) X-Spam-Checker-Version: SpamAssassin 3.4.6 (2021-04-09) on mira.cbaines.net X-Spam-Level: X-Spam-Status: No, score=-3.7 required=5.0 tests=BAYES_00,DKIM_ADSP_CUSTOM_MED, DKIM_INVALID,DKIM_SIGNED,FREEMAIL_FROM,MAILING_LIST_MULTI, RCVD_IN_MSPIKE_H2,SPF_HELO_PASS autolearn=ham autolearn_force=no version=3.4.6 Received: from lists.gnu.org (lists.gnu.org [209.51.188.17]) by mira.cbaines.net (Postfix) with ESMTPS id D87E527BBEC for ; Sat, 26 Nov 2022 12:13:54 +0000 (GMT) Received: from localhost ([::1] helo=lists1p.gnu.org) by lists.gnu.org with esmtp (Exim 4.90_1) (envelope-from ) id 1oyu3Y-0004rs-R7; Sat, 26 Nov 2022 07:13:16 -0500 Received: from eggs.gnu.org ([2001:470:142:3::10]) by lists.gnu.org with esmtps (TLS1.2:ECDHE_RSA_AES_256_GCM_SHA384:256) (Exim 4.90_1) (envelope-from ) id 1oyu3K-0004pN-Ro for guix-patches@gnu.org; Sat, 26 Nov 2022 07:13:02 -0500 Received: from debbugs.gnu.org ([209.51.188.43]) by eggs.gnu.org with esmtps (TLS1.2:ECDHE_RSA_AES_128_GCM_SHA256:128) (Exim 4.90_1) (envelope-from ) id 1oyu3K-0005LH-JM for guix-patches@gnu.org; Sat, 26 Nov 2022 07:13:02 -0500 Received: from Debian-debbugs by debbugs.gnu.org with local (Exim 4.84_2) (envelope-from ) id 1oyu3K-00018L-FJ for guix-patches@gnu.org; Sat, 26 Nov 2022 07:13:02 -0500 X-Loop: help-debbugs@gnu.org Subject: [bug#59607] [PATCH 3/8] gnu: Add python-addict. 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[85.127.52.93]) by smtp.gmail.com with ESMTPSA id az26-20020a05600c601a00b003d01b84e9b2sm8695059wmb.27.2022.11.26.04.11.54 for <59607@debbugs.gnu.org> (version=TLS1_3 cipher=TLS_AES_256_GCM_SHA384 bits=256/256); Sat, 26 Nov 2022 04:11:54 -0800 (PST) From: Liliana Marie Prikler Date: Sun, 20 Nov 2022 10:10:55 +0100 Message-ID: <06c7b8e263ad8d7191cadc00d1704e07ff7f5664.camel@gmail.com> MIME-Version: 1.0 X-BeenThere: debbugs-submit@debbugs.gnu.org X-Mailman-Version: 2.1.18 Precedence: list X-BeenThere: guix-patches@gnu.org List-Id: List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , Errors-To: guix-patches-bounces+patchwork=mira.cbaines.net@gnu.org Sender: guix-patches-bounces+patchwork=mira.cbaines.net@gnu.org X-getmail-retrieved-from-mailbox: Patches * gnu/packages/python-xyz.scm (python-addict): New variable. --- gnu/packages/python-xyz.scm | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/gnu/packages/python-xyz.scm b/gnu/packages/python-xyz.scm index 7fefbc5bff..d0591b4614 100644 --- a/gnu/packages/python-xyz.scm +++ b/gnu/packages/python-xyz.scm @@ -581,6 +581,24 @@ (define-public python-dotmap dictionary, can be convert to a dictionary, and is ordered by insertion.") (license license:expat))) +(define-public python-addict + (package + (name "python-addict") + (version "2.4.0") + (source (origin + (method url-fetch) + (uri (pypi-uri "addict" version)) + (sha256 + (base32 + "1574sicy5ydx9pvva3lbx8qp56z9jbdwbj26aqgjhyh61q723cmk")))) + (build-system python-build-system) + (home-page "https://github.com/mewwts/addict") + (synopsis "Dictionaries with items as attributes") + (description + "This package provides a dictionary type, whose values are both gettable +and settable using attributes, in addition to standard item-syntax.") + (license license:expat))) + (define-public python-twodict (package (name "python-twodict") From patchwork Sun Nov 20 16:25:32 2022 Content-Type: text/plain; charset="utf-8" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit X-Patchwork-Submitter: Liliana Marie Prikler X-Patchwork-Id: 44875 Return-Path: X-Original-To: patchwork@mira.cbaines.net Delivered-To: patchwork@mira.cbaines.net Received: by mira.cbaines.net (Postfix, from userid 113) id 6EF2027BBF2; Sat, 26 Nov 2022 12:14:16 +0000 (GMT) X-Spam-Checker-Version: SpamAssassin 3.4.6 (2021-04-09) on mira.cbaines.net X-Spam-Level: X-Spam-Status: No, score=-2.7 required=5.0 tests=BAYES_00,DKIM_ADSP_CUSTOM_MED, DKIM_INVALID,DKIM_SIGNED,FREEMAIL_FROM,MAILING_LIST_MULTI, PDS_OTHER_BAD_TLD,RCVD_IN_MSPIKE_H2,SPF_HELO_PASS,URIBL_BLOCKED autolearn=ham autolearn_force=no version=3.4.6 Received: from lists.gnu.org (lists.gnu.org [209.51.188.17]) by mira.cbaines.net (Postfix) with ESMTPS id 331B627BBEC for ; Sat, 26 Nov 2022 12:14:11 +0000 (GMT) Received: from localhost ([::1] helo=lists1p.gnu.org) by lists.gnu.org with esmtp (Exim 4.90_1) (envelope-from ) id 1oyu3Z-0004sQ-D4; Sat, 26 Nov 2022 07:13:18 -0500 Received: from eggs.gnu.org ([2001:470:142:3::10]) by lists.gnu.org with esmtps (TLS1.2:ECDHE_RSA_AES_256_GCM_SHA384:256) (Exim 4.90_1) (envelope-from ) id 1oyu3M-0004pm-Gz for guix-patches@gnu.org; Sat, 26 Nov 2022 07:13:04 -0500 Received: from debbugs.gnu.org ([209.51.188.43]) by eggs.gnu.org with esmtps (TLS1.2:ECDHE_RSA_AES_128_GCM_SHA256:128) (Exim 4.90_1) (envelope-from ) id 1oyu3M-0005LV-8V for guix-patches@gnu.org; Sat, 26 Nov 2022 07:13:04 -0500 Received: from Debian-debbugs by debbugs.gnu.org with local (Exim 4.84_2) (envelope-from ) id 1oyu3L-00018a-Az for guix-patches@gnu.org; Sat, 26 Nov 2022 07:13:03 -0500 X-Loop: help-debbugs@gnu.org Subject: [bug#59607] [PATCH 4/8] gnu: Add python-basicsr. 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[85.127.52.93]) by smtp.gmail.com with ESMTPSA id az26-20020a05600c601a00b003d01b84e9b2sm8695059wmb.27.2022.11.26.04.11.54 for <59607@debbugs.gnu.org> (version=TLS1_3 cipher=TLS_AES_256_GCM_SHA384 bits=256/256); Sat, 26 Nov 2022 04:11:55 -0800 (PST) From: Liliana Marie Prikler Date: Sun, 20 Nov 2022 17:25:32 +0100 Message-ID: MIME-Version: 1.0 X-BeenThere: debbugs-submit@debbugs.gnu.org X-Mailman-Version: 2.1.18 Precedence: list X-BeenThere: guix-patches@gnu.org List-Id: List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , Errors-To: guix-patches-bounces+patchwork=mira.cbaines.net@gnu.org Sender: guix-patches-bounces+patchwork=mira.cbaines.net@gnu.org X-getmail-retrieved-from-mailbox: Patches * gnu/packages/patches/python-basicsr-fuck-nvidia.patch: New file. * gnu/local.mk (dist_patch_DATA): Register it here. * gnu/packages/machine-learning.scm (python-basicsr): New variable. --- gnu/local.mk | 1 + gnu/packages/machine-learning.scm | 66 + .../patches/python-basicsr-fuck-nvidia.patch | 3233 +++++++++++++++++ 3 files changed, 3300 insertions(+) create mode 100644 gnu/packages/patches/python-basicsr-fuck-nvidia.patch diff --git a/gnu/local.mk b/gnu/local.mk index 7278c50e4f..8dd1abe07a 100644 --- a/gnu/local.mk +++ b/gnu/local.mk @@ -1720,6 +1720,7 @@ dist_patch_DATA = \ %D%/packages/patches/python-apsw-3.39.2.1-test-fix.patch \ %D%/packages/patches/python-aionotify-0.2.0-py3.8.patch \ %D%/packages/patches/python-argcomplete-1.11.1-fish31.patch \ + %D%/packages/patches/python-basicsr-fuck-nvidia.patch \ %D%/packages/patches/python-cross-compile.patch \ %D%/packages/patches/python-configobj-setuptools.patch \ %D%/packages/patches/python-dateutil-pytest-compat.patch \ diff --git a/gnu/packages/machine-learning.scm b/gnu/packages/machine-learning.scm index 0566f4bd69..a5767a2c31 100644 --- a/gnu/packages/machine-learning.scm +++ b/gnu/packages/machine-learning.scm @@ -750,6 +750,72 @@ (define (delete-ifdefs file) in terms of new algorithms.") (license license:gpl3+))) +(define-public python-basicsr + (package + (name "python-basicsr") + (version "1.4.2") + (source (origin + (method git-fetch) + (uri + (git-reference + (url "https://github.com/XPixelGroup/BasicSR") + (commit (string-append "v" version)))) + (patches + (search-patches + "python-basicsr-fuck-nvidia.patch")) + (modules '((guix build utils))) + (snippet + #~(begin (substitute* (find-files "." "\\.py") + (("\\.cuda\\(\\)") "") + (("pretrained=True") "weights=None")) + ;; Instead of images files, a custom lmdb is used + (delete-file-recursively "tests/data"))) + (sha256 + (base32 + "0qjk1hf1qjla3f6hb8fd6dv9w3b77568z8g17mlcxl91bp031z2i")))) + (build-system python-build-system) + (arguments + (list + #:phases + #~(modify-phases %standard-phases + (add-after 'unpack 'fix-requirements + (lambda _ + (substitute* "requirements.txt" + (("opencv-python") "") ; installed without egg-info + (("tb-nightly") "")))) + (add-before 'check 'pre-check + (lambda _ + (setenv "HOME" (getcwd)) + ;; Missing data... + (delete-file-recursively "tests/test_data") + ;; Model is fetched over the web + (delete-file-recursively "tests/test_models"))) + (replace 'check + (lambda* (#:key tests? #:allow-other-keys) + (when tests? + (invoke "pytest" "-vv"))))))) + (propagated-inputs (list opencv ; used via python bindings + python-addict + python-future + python-lmdb + python-numpy + python-pillow + python-pyyaml + python-requests + python-scikit-image + python-scipy + python-pytorch + python-torchvision + python-tqdm + python-yapf)) + (native-inputs (list lmdb python-cython python-pytest)) + (home-page "https://github.com/xinntao/BasicSR") + (synopsis "Image and Video Super-Resolution Toolbox") + (description "BasicSR is a pytorch-based toolbox to perform image restoration +tasks such as super-scaling, denoising, deblurring, and removal of JPEG +artifacts.") + (license license:asl2.0))) + (define-public ncnn (package (name "ncnn") diff --git a/gnu/packages/patches/python-basicsr-fuck-nvidia.patch b/gnu/packages/patches/python-basicsr-fuck-nvidia.patch new file mode 100644 index 0000000000..30cc1cb9ad --- /dev/null +++ b/gnu/packages/patches/python-basicsr-fuck-nvidia.patch @@ -0,0 +1,3233 @@ +diff --git a/basicsr/archs/arch_util.py b/basicsr/archs/arch_util.py +index 11b82a7..875b2b6 100644 +--- a/basicsr/archs/arch_util.py ++++ b/basicsr/archs/arch_util.py +@@ -10,7 +10,7 @@ from torch.nn import functional as F + from torch.nn import init as init + from torch.nn.modules.batchnorm import _BatchNorm + +-from basicsr.ops.dcn import ModulatedDeformConvPack, modulated_deform_conv ++from basicsr.ops.dcn import ModulatedDeformConvPack + from basicsr.utils import get_root_logger + + +@@ -228,12 +228,8 @@ class DCNv2Pack(ModulatedDeformConvPack): + logger = get_root_logger() + logger.warning(f'Offset abs mean is {offset_absmean}, larger than 50.') + +- if LooseVersion(torchvision.__version__) >= LooseVersion('0.9.0'): +- return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding, +- self.dilation, mask) +- else: +- return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding, +- self.dilation, self.groups, self.deformable_groups) ++ return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding, ++ self.dilation, mask) + + + def _no_grad_trunc_normal_(tensor, mean, std, a, b): +diff --git a/basicsr/archs/basicvsrpp_arch.py b/basicsr/archs/basicvsrpp_arch.py +index d9699cb..e726b8b 100644 +--- a/basicsr/archs/basicvsrpp_arch.py ++++ b/basicsr/archs/basicvsrpp_arch.py +@@ -69,14 +69,6 @@ class BasicVSRPlusPlus(nn.Module): + self.backbone = nn.ModuleDict() + modules = ['backward_1', 'forward_1', 'backward_2', 'forward_2'] + for i, module in enumerate(modules): +- if torch.cuda.is_available(): +- self.deform_align[module] = SecondOrderDeformableAlignment( +- 2 * mid_channels, +- mid_channels, +- 3, +- padding=1, +- deformable_groups=16, +- max_residue_magnitude=max_residue_magnitude) + self.backbone[module] = ConvResidualBlocks((2 + i) * mid_channels, mid_channels, num_blocks) + + # upsampling module +diff --git a/basicsr/archs/stylegan2_arch.py b/basicsr/archs/stylegan2_arch.py +index 9ab37f5..42cb08c 100644 +--- a/basicsr/archs/stylegan2_arch.py ++++ b/basicsr/archs/stylegan2_arch.py +@@ -4,7 +4,6 @@ import torch + from torch import nn + from torch.nn import functional as F + +-from basicsr.ops.fused_act import FusedLeakyReLU, fused_leaky_relu + from basicsr.ops.upfirdn2d import upfirdn2d + from basicsr.utils.registry import ARCH_REGISTRY + +@@ -141,8 +140,7 @@ class EqualLinear(nn.Module): + bias. Default: ``True``. + bias_init_val (float): Bias initialized value. Default: 0. + lr_mul (float): Learning rate multiplier. Default: 1. +- activation (None | str): The activation after ``linear`` operation. +- Supported: 'fused_lrelu', None. Default: None. ++ activation (None | str): Ignored. + """ + + def __init__(self, in_channels, out_channels, bias=True, bias_init_val=0, lr_mul=1, activation=None): +@@ -150,10 +148,7 @@ class EqualLinear(nn.Module): + self.in_channels = in_channels + self.out_channels = out_channels + self.lr_mul = lr_mul +- self.activation = activation +- if self.activation not in ['fused_lrelu', None]: +- raise ValueError(f'Wrong activation value in EqualLinear: {activation}' +- "Supported ones are: ['fused_lrelu', None].") ++ self.activation = None + self.scale = (1 / math.sqrt(in_channels)) * lr_mul + + self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul)) +@@ -167,12 +162,7 @@ class EqualLinear(nn.Module): + bias = None + else: + bias = self.bias * self.lr_mul +- if self.activation == 'fused_lrelu': +- out = F.linear(x, self.weight * self.scale) +- out = fused_leaky_relu(out, bias) +- else: +- out = F.linear(x, self.weight * self.scale, bias=bias) +- return out ++ return F.linear(x, self.weight * self.scale, bias=bias) + + def __repr__(self): + return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' +@@ -318,7 +308,7 @@ class StyleConv(nn.Module): + sample_mode=sample_mode, + resample_kernel=resample_kernel) + self.weight = nn.Parameter(torch.zeros(1)) # for noise injection +- self.activate = FusedLeakyReLU(out_channels) ++ self.activate = ScaledLeakyReLU() + + def forward(self, x, style, noise=None): + # modulate +@@ -693,10 +683,7 @@ class ConvLayer(nn.Sequential): + and not activate)) + # activation + if activate: +- if bias: +- layers.append(FusedLeakyReLU(out_channels)) +- else: +- layers.append(ScaledLeakyReLU(0.2)) ++ layers.append(ScaledLeakyReLU(0.2)) + + super(ConvLayer, self).__init__(*layers) + +diff --git a/basicsr/data/prefetch_dataloader.py b/basicsr/data/prefetch_dataloader.py +index 5088425..0cf35e6 100644 +--- a/basicsr/data/prefetch_dataloader.py ++++ b/basicsr/data/prefetch_dataloader.py +@@ -99,7 +99,7 @@ class CUDAPrefetcher(): + self.loader = iter(loader) + self.opt = opt + self.stream = torch.cuda.Stream() +- self.device = torch.device('cuda' if opt['num_gpu'] != 0 else 'cpu') ++ self.device = torch.device('cpu') + self.preload() + + def preload(self): +diff --git a/basicsr/models/base_model.py b/basicsr/models/base_model.py +index 05c8d2e..36442a2 100644 +--- a/basicsr/models/base_model.py ++++ b/basicsr/models/base_model.py +@@ -15,7 +15,7 @@ class BaseModel(): + + def __init__(self, opt): + self.opt = opt +- self.device = torch.device('cuda' if opt['num_gpu'] != 0 else 'cpu') ++ self.device = torch.device('cpu') + self.is_train = opt['is_train'] + self.schedulers = [] + self.optimizers = [] +@@ -91,14 +91,7 @@ class BaseModel(): + Args: + net (nn.Module) + """ +- net = net.to(self.device) +- if self.opt['dist']: +- find_unused_parameters = self.opt.get('find_unused_parameters', False) +- net = DistributedDataParallel( +- net, device_ids=[torch.cuda.current_device()], find_unused_parameters=find_unused_parameters) +- elif self.opt['num_gpu'] > 1: +- net = DataParallel(net) +- return net ++ return net.to(self.device) + + def get_optimizer(self, optim_type, params, lr, **kwargs): + if optim_type == 'Adam': +diff --git a/basicsr/ops/dcn/__init__.py b/basicsr/ops/dcn/__init__.py +index 32e3592..68033e0 100644 +--- a/basicsr/ops/dcn/__init__.py ++++ b/basicsr/ops/dcn/__init__.py +@@ -1,7 +1,4 @@ +-from .deform_conv import (DeformConv, DeformConvPack, ModulatedDeformConv, ModulatedDeformConvPack, deform_conv, +- modulated_deform_conv) ++from .deform_conv import (DeformConv, DeformConvPack, ModulatedDeformConv, ModulatedDeformConvPack) + + __all__ = [ +- 'DeformConv', 'DeformConvPack', 'ModulatedDeformConv', 'ModulatedDeformConvPack', 'deform_conv', +- 'modulated_deform_conv' +-] ++ 'DeformConv', 'DeformConvPack', 'ModulatedDeformConv', 'ModulatedDeformConvPack',] +diff --git a/basicsr/ops/dcn/deform_conv.py b/basicsr/ops/dcn/deform_conv.py +index 6268ca8..38ced57 100644 +--- a/basicsr/ops/dcn/deform_conv.py ++++ b/basicsr/ops/dcn/deform_conv.py +@@ -2,191 +2,9 @@ import math + import os + import torch + from torch import nn as nn +-from torch.autograd import Function +-from torch.autograd.function import once_differentiable + from torch.nn import functional as F + from torch.nn.modules.utils import _pair, _single + +-BASICSR_JIT = os.getenv('BASICSR_JIT') +-if BASICSR_JIT == 'True': +- from torch.utils.cpp_extension import load +- module_path = os.path.dirname(__file__) +- deform_conv_ext = load( +- 'deform_conv', +- sources=[ +- os.path.join(module_path, 'src', 'deform_conv_ext.cpp'), +- os.path.join(module_path, 'src', 'deform_conv_cuda.cpp'), +- os.path.join(module_path, 'src', 'deform_conv_cuda_kernel.cu'), +- ], +- ) +-else: +- try: +- from . import deform_conv_ext +- except ImportError: +- pass +- # avoid annoying print output +- # print(f'Cannot import deform_conv_ext. Error: {error}. You may need to: \n ' +- # '1. compile with BASICSR_EXT=True. or\n ' +- # '2. set BASICSR_JIT=True during running') +- +- +-class DeformConvFunction(Function): +- +- @staticmethod +- def forward(ctx, +- input, +- offset, +- weight, +- stride=1, +- padding=0, +- dilation=1, +- groups=1, +- deformable_groups=1, +- im2col_step=64): +- if input is not None and input.dim() != 4: +- raise ValueError(f'Expected 4D tensor as input, got {input.dim()}D tensor instead.') +- ctx.stride = _pair(stride) +- ctx.padding = _pair(padding) +- ctx.dilation = _pair(dilation) +- ctx.groups = groups +- ctx.deformable_groups = deformable_groups +- ctx.im2col_step = im2col_step +- +- ctx.save_for_backward(input, offset, weight) +- +- output = input.new_empty(DeformConvFunction._output_size(input, weight, ctx.padding, ctx.dilation, ctx.stride)) +- +- ctx.bufs_ = [input.new_empty(0), input.new_empty(0)] # columns, ones +- +- if not input.is_cuda: +- raise NotImplementedError +- else: +- cur_im2col_step = min(ctx.im2col_step, input.shape[0]) +- assert (input.shape[0] % cur_im2col_step) == 0, 'im2col step must divide batchsize' +- deform_conv_ext.deform_conv_forward(input, weight, +- offset, output, ctx.bufs_[0], ctx.bufs_[1], weight.size(3), +- weight.size(2), ctx.stride[1], ctx.stride[0], ctx.padding[1], +- ctx.padding[0], ctx.dilation[1], ctx.dilation[0], ctx.groups, +- ctx.deformable_groups, cur_im2col_step) +- return output +- +- @staticmethod +- @once_differentiable +- def backward(ctx, grad_output): +- input, offset, weight = ctx.saved_tensors +- +- grad_input = grad_offset = grad_weight = None +- +- if not grad_output.is_cuda: +- raise NotImplementedError +- else: +- cur_im2col_step = min(ctx.im2col_step, input.shape[0]) +- assert (input.shape[0] % cur_im2col_step) == 0, 'im2col step must divide batchsize' +- +- if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]: +- grad_input = torch.zeros_like(input) +- grad_offset = torch.zeros_like(offset) +- deform_conv_ext.deform_conv_backward_input(input, offset, grad_output, grad_input, +- grad_offset, weight, ctx.bufs_[0], weight.size(3), +- weight.size(2), ctx.stride[1], ctx.stride[0], ctx.padding[1], +- ctx.padding[0], ctx.dilation[1], ctx.dilation[0], ctx.groups, +- ctx.deformable_groups, cur_im2col_step) +- +- if ctx.needs_input_grad[2]: +- grad_weight = torch.zeros_like(weight) +- deform_conv_ext.deform_conv_backward_parameters(input, offset, grad_output, grad_weight, +- ctx.bufs_[0], ctx.bufs_[1], weight.size(3), +- weight.size(2), ctx.stride[1], ctx.stride[0], +- ctx.padding[1], ctx.padding[0], ctx.dilation[1], +- ctx.dilation[0], ctx.groups, ctx.deformable_groups, 1, +- cur_im2col_step) +- +- return (grad_input, grad_offset, grad_weight, None, None, None, None, None) +- +- @staticmethod +- def _output_size(input, weight, padding, dilation, stride): +- channels = weight.size(0) +- output_size = (input.size(0), channels) +- for d in range(input.dim() - 2): +- in_size = input.size(d + 2) +- pad = padding[d] +- kernel = dilation[d] * (weight.size(d + 2) - 1) + 1 +- stride_ = stride[d] +- output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, ) +- if not all(map(lambda s: s > 0, output_size)): +- raise ValueError(f'convolution input is too small (output would be {"x".join(map(str, output_size))})') +- return output_size +- +- +-class ModulatedDeformConvFunction(Function): +- +- @staticmethod +- def forward(ctx, +- input, +- offset, +- mask, +- weight, +- bias=None, +- stride=1, +- padding=0, +- dilation=1, +- groups=1, +- deformable_groups=1): +- ctx.stride = stride +- ctx.padding = padding +- ctx.dilation = dilation +- ctx.groups = groups +- ctx.deformable_groups = deformable_groups +- ctx.with_bias = bias is not None +- if not ctx.with_bias: +- bias = input.new_empty(1) # fake tensor +- if not input.is_cuda: +- raise NotImplementedError +- if weight.requires_grad or mask.requires_grad or offset.requires_grad or input.requires_grad: +- ctx.save_for_backward(input, offset, mask, weight, bias) +- output = input.new_empty(ModulatedDeformConvFunction._infer_shape(ctx, input, weight)) +- ctx._bufs = [input.new_empty(0), input.new_empty(0)] +- deform_conv_ext.modulated_deform_conv_forward(input, weight, bias, ctx._bufs[0], offset, mask, output, +- ctx._bufs[1], weight.shape[2], weight.shape[3], ctx.stride, +- ctx.stride, ctx.padding, ctx.padding, ctx.dilation, ctx.dilation, +- ctx.groups, ctx.deformable_groups, ctx.with_bias) +- return output +- +- @staticmethod +- @once_differentiable +- def backward(ctx, grad_output): +- if not grad_output.is_cuda: +- raise NotImplementedError +- input, offset, mask, weight, bias = ctx.saved_tensors +- grad_input = torch.zeros_like(input) +- grad_offset = torch.zeros_like(offset) +- grad_mask = torch.zeros_like(mask) +- grad_weight = torch.zeros_like(weight) +- grad_bias = torch.zeros_like(bias) +- deform_conv_ext.modulated_deform_conv_backward(input, weight, bias, ctx._bufs[0], offset, mask, ctx._bufs[1], +- grad_input, grad_weight, grad_bias, grad_offset, grad_mask, +- grad_output, weight.shape[2], weight.shape[3], ctx.stride, +- ctx.stride, ctx.padding, ctx.padding, ctx.dilation, ctx.dilation, +- ctx.groups, ctx.deformable_groups, ctx.with_bias) +- if not ctx.with_bias: +- grad_bias = None +- +- return (grad_input, grad_offset, grad_mask, grad_weight, grad_bias, None, None, None, None, None) +- +- @staticmethod +- def _infer_shape(ctx, input, weight): +- n = input.size(0) +- channels_out = weight.size(0) +- height, width = input.shape[2:4] +- kernel_h, kernel_w = weight.shape[2:4] +- height_out = (height + 2 * ctx.padding - (ctx.dilation * (kernel_h - 1) + 1)) // ctx.stride + 1 +- width_out = (width + 2 * ctx.padding - (ctx.dilation * (kernel_w - 1) + 1)) // ctx.stride + 1 +- return n, channels_out, height_out, width_out +- +- +-deform_conv = DeformConvFunction.apply +-modulated_deform_conv = ModulatedDeformConvFunction.apply +- + + class DeformConv(nn.Module): + +@@ -230,19 +48,7 @@ class DeformConv(nn.Module): + self.weight.data.uniform_(-stdv, stdv) + + def forward(self, x, offset): +- # To fix an assert error in deform_conv_cuda.cpp:128 +- # input image is smaller than kernel +- input_pad = (x.size(2) < self.kernel_size[0] or x.size(3) < self.kernel_size[1]) +- if input_pad: +- pad_h = max(self.kernel_size[0] - x.size(2), 0) +- pad_w = max(self.kernel_size[1] - x.size(3), 0) +- x = F.pad(x, (0, pad_w, 0, pad_h), 'constant', 0).contiguous() +- offset = F.pad(offset, (0, pad_w, 0, pad_h), 'constant', 0).contiguous() +- out = deform_conv(x, offset, self.weight, self.stride, self.padding, self.dilation, self.groups, +- self.deformable_groups) +- if input_pad: +- out = out[:, :, :out.size(2) - pad_h, :out.size(3) - pad_w].contiguous() +- return out ++ return NotImplemented + + + class DeformConvPack(DeformConv): +@@ -281,9 +87,7 @@ class DeformConvPack(DeformConv): + self.conv_offset.bias.data.zero_() + + def forward(self, x): +- offset = self.conv_offset(x) +- return deform_conv(x, offset, self.weight, self.stride, self.padding, self.dilation, self.groups, +- self.deformable_groups) ++ return NotImplemented + + + class ModulatedDeformConv(nn.Module): +@@ -329,8 +133,7 @@ class ModulatedDeformConv(nn.Module): + self.bias.data.zero_() + + def forward(self, x, offset, mask): +- return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation, +- self.groups, self.deformable_groups) ++ return NotImplemented + + + class ModulatedDeformConvPack(ModulatedDeformConv): +@@ -371,9 +174,4 @@ class ModulatedDeformConvPack(ModulatedDeformConv): + self.conv_offset.bias.data.zero_() + + def forward(self, x): +- out = self.conv_offset(x) +- o1, o2, mask = torch.chunk(out, 3, dim=1) +- offset = torch.cat((o1, o2), dim=1) +- mask = torch.sigmoid(mask) +- return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation, +- self.groups, self.deformable_groups) ++ return NotImplemented +diff --git a/basicsr/ops/dcn/src/deform_conv_cuda.cpp b/basicsr/ops/dcn/src/deform_conv_cuda.cpp +deleted file mode 100644 +index b465c49..0000000 +--- a/basicsr/ops/dcn/src/deform_conv_cuda.cpp ++++ /dev/null +@@ -1,685 +0,0 @@ +-// modify from +-// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c +- +-#include +-#include +- +-#include +-#include +- +-void deformable_im2col(const at::Tensor data_im, const at::Tensor data_offset, +- const int channels, const int height, const int width, +- const int ksize_h, const int ksize_w, const int pad_h, +- const int pad_w, const int stride_h, const int stride_w, +- const int dilation_h, const int dilation_w, +- const int parallel_imgs, const int deformable_group, +- at::Tensor data_col); +- +-void deformable_col2im(const at::Tensor data_col, const at::Tensor data_offset, +- const int channels, const int height, const int width, +- const int ksize_h, const int ksize_w, const int pad_h, +- const int pad_w, const int stride_h, const int stride_w, +- const int dilation_h, const int dilation_w, +- const int parallel_imgs, const int deformable_group, +- at::Tensor grad_im); +- +-void deformable_col2im_coord( +- const at::Tensor data_col, const at::Tensor data_im, +- const at::Tensor data_offset, const int channels, const int height, +- const int width, const int ksize_h, const int ksize_w, const int pad_h, +- const int pad_w, const int stride_h, const int stride_w, +- const int dilation_h, const int dilation_w, const int parallel_imgs, +- const int deformable_group, at::Tensor grad_offset); +- +-void modulated_deformable_im2col_cuda( +- const at::Tensor data_im, const at::Tensor data_offset, +- const at::Tensor data_mask, const int batch_size, const int channels, +- const int height_im, const int width_im, const int height_col, +- const int width_col, const int kernel_h, const int kenerl_w, +- const int pad_h, const int pad_w, const int stride_h, const int stride_w, +- const int dilation_h, const int dilation_w, const int deformable_group, +- at::Tensor data_col); +- +-void modulated_deformable_col2im_cuda( +- const at::Tensor data_col, const at::Tensor data_offset, +- const at::Tensor data_mask, const int batch_size, const int channels, +- const int height_im, const int width_im, const int height_col, +- const int width_col, const int kernel_h, const int kenerl_w, +- const int pad_h, const int pad_w, const int stride_h, const int stride_w, +- const int dilation_h, const int dilation_w, const int deformable_group, +- at::Tensor grad_im); +- +-void modulated_deformable_col2im_coord_cuda( +- const at::Tensor data_col, const at::Tensor data_im, +- const at::Tensor data_offset, const at::Tensor data_mask, +- const int batch_size, const int channels, const int height_im, +- const int width_im, const int height_col, const int width_col, +- const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w, +- const int stride_h, const int stride_w, const int dilation_h, +- const int dilation_w, const int deformable_group, at::Tensor grad_offset, +- at::Tensor grad_mask); +- +-void shape_check(at::Tensor input, at::Tensor offset, at::Tensor *gradOutput, +- at::Tensor weight, int kH, int kW, int dH, int dW, int padH, +- int padW, int dilationH, int dilationW, int group, +- int deformable_group) { +- TORCH_CHECK(weight.ndimension() == 4, +- "4D weight tensor (nOutputPlane,nInputPlane,kH,kW) expected, " +- "but got: %s", +- weight.ndimension()); +- +- TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); +- +- TORCH_CHECK(kW > 0 && kH > 0, +- "kernel size should be greater than zero, but got kH: %d kW: %d", kH, +- kW); +- +- TORCH_CHECK((weight.size(2) == kH && weight.size(3) == kW), +- "kernel size should be consistent with weight, ", +- "but got kH: %d kW: %d weight.size(2): %d, weight.size(3): %d", kH, +- kW, weight.size(2), weight.size(3)); +- +- TORCH_CHECK(dW > 0 && dH > 0, +- "stride should be greater than zero, but got dH: %d dW: %d", dH, dW); +- +- TORCH_CHECK( +- dilationW > 0 && dilationH > 0, +- "dilation should be greater than 0, but got dilationH: %d dilationW: %d", +- dilationH, dilationW); +- +- int ndim = input.ndimension(); +- int dimf = 0; +- int dimh = 1; +- int dimw = 2; +- +- if (ndim == 4) { +- dimf++; +- dimh++; +- dimw++; +- } +- +- TORCH_CHECK(ndim == 3 || ndim == 4, "3D or 4D input tensor expected but got: %s", +- ndim); +- +- long nInputPlane = weight.size(1) * group; +- long inputHeight = input.size(dimh); +- long inputWidth = input.size(dimw); +- long nOutputPlane = weight.size(0); +- long outputHeight = +- (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; +- long outputWidth = +- (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; +- +- TORCH_CHECK(nInputPlane % deformable_group == 0, +- "input channels must divide deformable group size"); +- +- if (outputWidth < 1 || outputHeight < 1) +- AT_ERROR( +- "Given input size: (%ld x %ld x %ld). " +- "Calculated output size: (%ld x %ld x %ld). Output size is too small", +- nInputPlane, inputHeight, inputWidth, nOutputPlane, outputHeight, +- outputWidth); +- +- TORCH_CHECK(input.size(1) == nInputPlane, +- "invalid number of input planes, expected: %d, but got: %d", +- nInputPlane, input.size(1)); +- +- TORCH_CHECK((inputHeight >= kH && inputWidth >= kW), +- "input image is smaller than kernel"); +- +- TORCH_CHECK((offset.size(2) == outputHeight && offset.size(3) == outputWidth), +- "invalid spatial size of offset, expected height: %d width: %d, but " +- "got height: %d width: %d", +- outputHeight, outputWidth, offset.size(2), offset.size(3)); +- +- TORCH_CHECK((offset.size(1) == deformable_group * 2 * kH * kW), +- "invalid number of channels of offset"); +- +- if (gradOutput != NULL) { +- TORCH_CHECK(gradOutput->size(dimf) == nOutputPlane, +- "invalid number of gradOutput planes, expected: %d, but got: %d", +- nOutputPlane, gradOutput->size(dimf)); +- +- TORCH_CHECK((gradOutput->size(dimh) == outputHeight && +- gradOutput->size(dimw) == outputWidth), +- "invalid size of gradOutput, expected height: %d width: %d , but " +- "got height: %d width: %d", +- outputHeight, outputWidth, gradOutput->size(dimh), +- gradOutput->size(dimw)); +- } +-} +- +-int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight, +- at::Tensor offset, at::Tensor output, +- at::Tensor columns, at::Tensor ones, int kW, +- int kH, int dW, int dH, int padW, int padH, +- int dilationW, int dilationH, int group, +- int deformable_group, int im2col_step) { +- // todo: resize columns to include im2col: done +- // todo: add im2col_step as input +- // todo: add new output buffer and transpose it to output (or directly +- // transpose output) todo: possibly change data indexing because of +- // parallel_imgs +- +- shape_check(input, offset, NULL, weight, kH, kW, dH, dW, padH, padW, +- dilationH, dilationW, group, deformable_group); +- at::DeviceGuard guard(input.device()); +- +- input = input.contiguous(); +- offset = offset.contiguous(); +- weight = weight.contiguous(); +- +- int batch = 1; +- if (input.ndimension() == 3) { +- // Force batch +- batch = 0; +- input.unsqueeze_(0); +- offset.unsqueeze_(0); +- } +- +- // todo: assert batchsize dividable by im2col_step +- +- long batchSize = input.size(0); +- long nInputPlane = input.size(1); +- long inputHeight = input.size(2); +- long inputWidth = input.size(3); +- +- long nOutputPlane = weight.size(0); +- +- long outputWidth = +- (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; +- long outputHeight = +- (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; +- +- TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset"); +- +- output = output.view({batchSize / im2col_step, im2col_step, nOutputPlane, +- outputHeight, outputWidth}); +- columns = at::zeros( +- {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, +- input.options()); +- +- if (ones.ndimension() != 2 || +- ones.size(0) * ones.size(1) < outputHeight * outputWidth) { +- ones = at::ones({outputHeight, outputWidth}, input.options()); +- } +- +- input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, +- inputHeight, inputWidth}); +- offset = +- offset.view({batchSize / im2col_step, im2col_step, +- deformable_group * 2 * kH * kW, outputHeight, outputWidth}); +- +- at::Tensor output_buffer = +- at::zeros({batchSize / im2col_step, nOutputPlane, +- im2col_step * outputHeight, outputWidth}, +- output.options()); +- +- output_buffer = output_buffer.view( +- {output_buffer.size(0), group, output_buffer.size(1) / group, +- output_buffer.size(2), output_buffer.size(3)}); +- +- for (int elt = 0; elt < batchSize / im2col_step; elt++) { +- deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight, +- inputWidth, kH, kW, padH, padW, dH, dW, dilationH, +- dilationW, im2col_step, deformable_group, columns); +- +- columns = columns.view({group, columns.size(0) / group, columns.size(1)}); +- weight = weight.view({group, weight.size(0) / group, weight.size(1), +- weight.size(2), weight.size(3)}); +- +- for (int g = 0; g < group; g++) { +- output_buffer[elt][g] = output_buffer[elt][g] +- .flatten(1) +- .addmm_(weight[g].flatten(1), columns[g]) +- .view_as(output_buffer[elt][g]); +- } +- } +- +- output_buffer = output_buffer.view( +- {output_buffer.size(0), output_buffer.size(1) * output_buffer.size(2), +- output_buffer.size(3), output_buffer.size(4)}); +- +- output_buffer = output_buffer.view({batchSize / im2col_step, nOutputPlane, +- im2col_step, outputHeight, outputWidth}); +- output_buffer.transpose_(1, 2); +- output.copy_(output_buffer); +- output = output.view({batchSize, nOutputPlane, outputHeight, outputWidth}); +- +- input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); +- offset = offset.view( +- {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); +- +- if (batch == 0) { +- output = output.view({nOutputPlane, outputHeight, outputWidth}); +- input = input.view({nInputPlane, inputHeight, inputWidth}); +- offset = offset.view({offset.size(1), offset.size(2), offset.size(3)}); +- } +- +- return 1; +-} +- +-int deform_conv_backward_input_cuda(at::Tensor input, at::Tensor offset, +- at::Tensor gradOutput, at::Tensor gradInput, +- at::Tensor gradOffset, at::Tensor weight, +- at::Tensor columns, int kW, int kH, int dW, +- int dH, int padW, int padH, int dilationW, +- int dilationH, int group, +- int deformable_group, int im2col_step) { +- shape_check(input, offset, &gradOutput, weight, kH, kW, dH, dW, padH, padW, +- dilationH, dilationW, group, deformable_group); +- at::DeviceGuard guard(input.device()); +- +- input = input.contiguous(); +- offset = offset.contiguous(); +- gradOutput = gradOutput.contiguous(); +- weight = weight.contiguous(); +- +- int batch = 1; +- +- if (input.ndimension() == 3) { +- // Force batch +- batch = 0; +- input = input.view({1, input.size(0), input.size(1), input.size(2)}); +- offset = offset.view({1, offset.size(0), offset.size(1), offset.size(2)}); +- gradOutput = gradOutput.view( +- {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)}); +- } +- +- long batchSize = input.size(0); +- long nInputPlane = input.size(1); +- long inputHeight = input.size(2); +- long inputWidth = input.size(3); +- +- long nOutputPlane = weight.size(0); +- +- long outputWidth = +- (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; +- long outputHeight = +- (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; +- +- TORCH_CHECK((offset.size(0) == batchSize), 3, "invalid batch size of offset"); +- gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth}); +- columns = at::zeros( +- {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, +- input.options()); +- +- // change order of grad output +- gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step, +- nOutputPlane, outputHeight, outputWidth}); +- gradOutput.transpose_(1, 2); +- +- gradInput = gradInput.view({batchSize / im2col_step, im2col_step, nInputPlane, +- inputHeight, inputWidth}); +- input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, +- inputHeight, inputWidth}); +- gradOffset = gradOffset.view({batchSize / im2col_step, im2col_step, +- deformable_group * 2 * kH * kW, outputHeight, +- outputWidth}); +- offset = +- offset.view({batchSize / im2col_step, im2col_step, +- deformable_group * 2 * kH * kW, outputHeight, outputWidth}); +- +- for (int elt = 0; elt < batchSize / im2col_step; elt++) { +- // divide into groups +- columns = columns.view({group, columns.size(0) / group, columns.size(1)}); +- weight = weight.view({group, weight.size(0) / group, weight.size(1), +- weight.size(2), weight.size(3)}); +- gradOutput = gradOutput.view( +- {gradOutput.size(0), group, gradOutput.size(1) / group, +- gradOutput.size(2), gradOutput.size(3), gradOutput.size(4)}); +- +- for (int g = 0; g < group; g++) { +- columns[g] = columns[g].addmm_(weight[g].flatten(1).transpose(0, 1), +- gradOutput[elt][g].flatten(1), 0.0f, 1.0f); +- } +- +- columns = +- columns.view({columns.size(0) * columns.size(1), columns.size(2)}); +- gradOutput = gradOutput.view( +- {gradOutput.size(0), gradOutput.size(1) * gradOutput.size(2), +- gradOutput.size(3), gradOutput.size(4), gradOutput.size(5)}); +- +- deformable_col2im_coord(columns, input[elt], offset[elt], nInputPlane, +- inputHeight, inputWidth, kH, kW, padH, padW, dH, dW, +- dilationH, dilationW, im2col_step, deformable_group, +- gradOffset[elt]); +- +- deformable_col2im(columns, offset[elt], nInputPlane, inputHeight, +- inputWidth, kH, kW, padH, padW, dH, dW, dilationH, +- dilationW, im2col_step, deformable_group, gradInput[elt]); +- } +- +- gradOutput.transpose_(1, 2); +- gradOutput = +- gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth}); +- +- gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth}); +- input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); +- gradOffset = gradOffset.view( +- {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); +- offset = offset.view( +- {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); +- +- if (batch == 0) { +- gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth}); +- input = input.view({nInputPlane, inputHeight, inputWidth}); +- gradInput = gradInput.view({nInputPlane, inputHeight, inputWidth}); +- offset = offset.view({offset.size(1), offset.size(2), offset.size(3)}); +- gradOffset = +- gradOffset.view({offset.size(1), offset.size(2), offset.size(3)}); +- } +- +- return 1; +-} +- +-int deform_conv_backward_parameters_cuda( +- at::Tensor input, at::Tensor offset, at::Tensor gradOutput, +- at::Tensor gradWeight, // at::Tensor gradBias, +- at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH, +- int padW, int padH, int dilationW, int dilationH, int group, +- int deformable_group, float scale, int im2col_step) { +- // todo: transpose and reshape outGrad +- // todo: reshape columns +- // todo: add im2col_step as input +- +- shape_check(input, offset, &gradOutput, gradWeight, kH, kW, dH, dW, padH, +- padW, dilationH, dilationW, group, deformable_group); +- at::DeviceGuard guard(input.device()); +- +- input = input.contiguous(); +- offset = offset.contiguous(); +- gradOutput = gradOutput.contiguous(); +- +- int batch = 1; +- +- if (input.ndimension() == 3) { +- // Force batch +- batch = 0; +- input = input.view( +- at::IntList({1, input.size(0), input.size(1), input.size(2)})); +- gradOutput = gradOutput.view( +- {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)}); +- } +- +- long batchSize = input.size(0); +- long nInputPlane = input.size(1); +- long inputHeight = input.size(2); +- long inputWidth = input.size(3); +- +- long nOutputPlane = gradWeight.size(0); +- +- long outputWidth = +- (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; +- long outputHeight = +- (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; +- +- TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset"); +- +- columns = at::zeros( +- {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, +- input.options()); +- +- gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step, +- nOutputPlane, outputHeight, outputWidth}); +- gradOutput.transpose_(1, 2); +- +- at::Tensor gradOutputBuffer = at::zeros_like(gradOutput); +- gradOutputBuffer = +- gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane, im2col_step, +- outputHeight, outputWidth}); +- gradOutputBuffer.copy_(gradOutput); +- gradOutputBuffer = +- gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane, +- im2col_step * outputHeight, outputWidth}); +- +- gradOutput.transpose_(1, 2); +- gradOutput = +- gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth}); +- +- input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, +- inputHeight, inputWidth}); +- offset = +- offset.view({batchSize / im2col_step, im2col_step, +- deformable_group * 2 * kH * kW, outputHeight, outputWidth}); +- +- for (int elt = 0; elt < batchSize / im2col_step; elt++) { +- deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight, +- inputWidth, kH, kW, padH, padW, dH, dW, dilationH, +- dilationW, im2col_step, deformable_group, columns); +- +- // divide into group +- gradOutputBuffer = gradOutputBuffer.view( +- {gradOutputBuffer.size(0), group, gradOutputBuffer.size(1) / group, +- gradOutputBuffer.size(2), gradOutputBuffer.size(3)}); +- columns = columns.view({group, columns.size(0) / group, columns.size(1)}); +- gradWeight = +- gradWeight.view({group, gradWeight.size(0) / group, gradWeight.size(1), +- gradWeight.size(2), gradWeight.size(3)}); +- +- for (int g = 0; g < group; g++) { +- gradWeight[g] = gradWeight[g] +- .flatten(1) +- .addmm_(gradOutputBuffer[elt][g].flatten(1), +- columns[g].transpose(1, 0), 1.0, scale) +- .view_as(gradWeight[g]); +- } +- gradOutputBuffer = gradOutputBuffer.view( +- {gradOutputBuffer.size(0), +- gradOutputBuffer.size(1) * gradOutputBuffer.size(2), +- gradOutputBuffer.size(3), gradOutputBuffer.size(4)}); +- columns = +- columns.view({columns.size(0) * columns.size(1), columns.size(2)}); +- gradWeight = gradWeight.view({gradWeight.size(0) * gradWeight.size(1), +- gradWeight.size(2), gradWeight.size(3), +- gradWeight.size(4)}); +- } +- +- input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); +- offset = offset.view( +- {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); +- +- if (batch == 0) { +- gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth}); +- input = input.view({nInputPlane, inputHeight, inputWidth}); +- } +- +- return 1; +-} +- +-void modulated_deform_conv_cuda_forward( +- at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, +- at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns, +- int kernel_h, int kernel_w, const int stride_h, const int stride_w, +- const int pad_h, const int pad_w, const int dilation_h, +- const int dilation_w, const int group, const int deformable_group, +- const bool with_bias) { +- TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous"); +- TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); +- at::DeviceGuard guard(input.device()); +- +- const int batch = input.size(0); +- const int channels = input.size(1); +- const int height = input.size(2); +- const int width = input.size(3); +- +- const int channels_out = weight.size(0); +- const int channels_kernel = weight.size(1); +- const int kernel_h_ = weight.size(2); +- const int kernel_w_ = weight.size(3); +- +- if (kernel_h_ != kernel_h || kernel_w_ != kernel_w) +- AT_ERROR("Input shape and kernel shape won't match: (%d x %d vs %d x %d).", +- kernel_h_, kernel_w, kernel_h_, kernel_w_); +- if (channels != channels_kernel * group) +- AT_ERROR("Input shape and kernel channels won't match: (%d vs %d).", +- channels, channels_kernel * group); +- +- const int height_out = +- (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; +- const int width_out = +- (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; +- +- if (ones.ndimension() != 2 || +- ones.size(0) * ones.size(1) < height_out * width_out) { +- // Resize plane and fill with ones... +- ones = at::ones({height_out, width_out}, input.options()); +- } +- +- // resize output +- output = output.view({batch, channels_out, height_out, width_out}).zero_(); +- // resize temporary columns +- columns = +- at::zeros({channels * kernel_h * kernel_w, 1 * height_out * width_out}, +- input.options()); +- +- output = output.view({output.size(0), group, output.size(1) / group, +- output.size(2), output.size(3)}); +- +- for (int b = 0; b < batch; b++) { +- modulated_deformable_im2col_cuda( +- input[b], offset[b], mask[b], 1, channels, height, width, height_out, +- width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, +- dilation_h, dilation_w, deformable_group, columns); +- +- // divide into group +- weight = weight.view({group, weight.size(0) / group, weight.size(1), +- weight.size(2), weight.size(3)}); +- columns = columns.view({group, columns.size(0) / group, columns.size(1)}); +- +- for (int g = 0; g < group; g++) { +- output[b][g] = output[b][g] +- .flatten(1) +- .addmm_(weight[g].flatten(1), columns[g]) +- .view_as(output[b][g]); +- } +- +- weight = weight.view({weight.size(0) * weight.size(1), weight.size(2), +- weight.size(3), weight.size(4)}); +- columns = +- columns.view({columns.size(0) * columns.size(1), columns.size(2)}); +- } +- +- output = output.view({output.size(0), output.size(1) * output.size(2), +- output.size(3), output.size(4)}); +- +- if (with_bias) { +- output += bias.view({1, bias.size(0), 1, 1}); +- } +-} +- +-void modulated_deform_conv_cuda_backward( +- at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, +- at::Tensor offset, at::Tensor mask, at::Tensor columns, +- at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias, +- at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output, +- int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, +- int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, +- const bool with_bias) { +- TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous"); +- TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); +- at::DeviceGuard guard(input.device()); +- +- const int batch = input.size(0); +- const int channels = input.size(1); +- const int height = input.size(2); +- const int width = input.size(3); +- +- const int channels_kernel = weight.size(1); +- const int kernel_h_ = weight.size(2); +- const int kernel_w_ = weight.size(3); +- if (kernel_h_ != kernel_h || kernel_w_ != kernel_w) +- AT_ERROR("Input shape and kernel shape won't match: (%d x %d vs %d x %d).", +- kernel_h_, kernel_w, kernel_h_, kernel_w_); +- if (channels != channels_kernel * group) +- AT_ERROR("Input shape and kernel channels won't match: (%d vs %d).", +- channels, channels_kernel * group); +- +- const int height_out = +- (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; +- const int width_out = +- (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; +- +- if (ones.ndimension() != 2 || +- ones.size(0) * ones.size(1) < height_out * width_out) { +- // Resize plane and fill with ones... +- ones = at::ones({height_out, width_out}, input.options()); +- } +- +- grad_input = grad_input.view({batch, channels, height, width}); +- columns = at::zeros({channels * kernel_h * kernel_w, height_out * width_out}, +- input.options()); +- +- grad_output = +- grad_output.view({grad_output.size(0), group, grad_output.size(1) / group, +- grad_output.size(2), grad_output.size(3)}); +- +- for (int b = 0; b < batch; b++) { +- // divide int group +- columns = columns.view({group, columns.size(0) / group, columns.size(1)}); +- weight = weight.view({group, weight.size(0) / group, weight.size(1), +- weight.size(2), weight.size(3)}); +- +- for (int g = 0; g < group; g++) { +- columns[g].addmm_(weight[g].flatten(1).transpose(0, 1), +- grad_output[b][g].flatten(1), 0.0f, 1.0f); +- } +- +- columns = +- columns.view({columns.size(0) * columns.size(1), columns.size(2)}); +- weight = weight.view({weight.size(0) * weight.size(1), weight.size(2), +- weight.size(3), weight.size(4)}); +- +- // gradient w.r.t. input coordinate data +- modulated_deformable_col2im_coord_cuda( +- columns, input[b], offset[b], mask[b], 1, channels, height, width, +- height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, +- stride_w, dilation_h, dilation_w, deformable_group, grad_offset[b], +- grad_mask[b]); +- // gradient w.r.t. input data +- modulated_deformable_col2im_cuda( +- columns, offset[b], mask[b], 1, channels, height, width, height_out, +- width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, +- dilation_h, dilation_w, deformable_group, grad_input[b]); +- +- // gradient w.r.t. weight, dWeight should accumulate across the batch and +- // group +- modulated_deformable_im2col_cuda( +- input[b], offset[b], mask[b], 1, channels, height, width, height_out, +- width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, +- dilation_h, dilation_w, deformable_group, columns); +- +- columns = columns.view({group, columns.size(0) / group, columns.size(1)}); +- grad_weight = grad_weight.view({group, grad_weight.size(0) / group, +- grad_weight.size(1), grad_weight.size(2), +- grad_weight.size(3)}); +- if (with_bias) +- grad_bias = grad_bias.view({group, grad_bias.size(0) / group}); +- +- for (int g = 0; g < group; g++) { +- grad_weight[g] = +- grad_weight[g] +- .flatten(1) +- .addmm_(grad_output[b][g].flatten(1), columns[g].transpose(0, 1)) +- .view_as(grad_weight[g]); +- if (with_bias) { +- grad_bias[g] = +- grad_bias[g] +- .view({-1, 1}) +- .addmm_(grad_output[b][g].flatten(1), ones.view({-1, 1})) +- .view(-1); +- } +- } +- +- columns = +- columns.view({columns.size(0) * columns.size(1), columns.size(2)}); +- grad_weight = grad_weight.view({grad_weight.size(0) * grad_weight.size(1), +- grad_weight.size(2), grad_weight.size(3), +- grad_weight.size(4)}); +- if (with_bias) +- grad_bias = grad_bias.view({grad_bias.size(0) * grad_bias.size(1)}); +- } +- grad_output = grad_output.view({grad_output.size(0) * grad_output.size(1), +- grad_output.size(2), grad_output.size(3), +- grad_output.size(4)}); +-} +diff --git a/basicsr/ops/dcn/src/deform_conv_cuda_kernel.cu b/basicsr/ops/dcn/src/deform_conv_cuda_kernel.cu +deleted file mode 100644 +index 98752dc..0000000 +--- a/basicsr/ops/dcn/src/deform_conv_cuda_kernel.cu ++++ /dev/null +@@ -1,867 +0,0 @@ +-/*! +- ******************* BEGIN Caffe Copyright Notice and Disclaimer **************** +- * +- * COPYRIGHT +- * +- * All contributions by the University of California: +- * Copyright (c) 2014-2017 The Regents of the University of California (Regents) +- * All rights reserved. +- * +- * All other contributions: +- * Copyright (c) 2014-2017, the respective contributors +- * All rights reserved. +- * +- * Caffe uses a shared copyright model: each contributor holds copyright over +- * their contributions to Caffe. The project versioning records all such +- * contribution and copyright details. If a contributor wants to further mark +- * their specific copyright on a particular contribution, they should indicate +- * their copyright solely in the commit message of the change when it is +- * committed. +- * +- * LICENSE +- * +- * Redistribution and use in source and binary forms, with or without +- * modification, are permitted provided that the following conditions are met: +- * +- * 1. Redistributions of source code must retain the above copyright notice, this +- * list of conditions and the following disclaimer. +- * 2. Redistributions in binary form must reproduce the above copyright notice, +- * this list of conditions and the following disclaimer in the documentation +- * and/or other materials provided with the distribution. +- * +- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +- * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR +- * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +- * +- * CONTRIBUTION AGREEMENT +- * +- * By contributing to the BVLC/caffe repository through pull-request, comment, +- * or otherwise, the contributor releases their content to the +- * license and copyright terms herein. +- * +- ***************** END Caffe Copyright Notice and Disclaimer ******************** +- * +- * Copyright (c) 2018 Microsoft +- * Licensed under The MIT License [see LICENSE for details] +- * \file modulated_deformable_im2col.cuh +- * \brief Function definitions of converting an image to +- * column matrix based on kernel, padding, dilation, and offset. +- * These functions are mainly used in deformable convolution operators. +- * \ref: https://arxiv.org/abs/1703.06211 +- * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu, Dazhi Cheng +- */ +- +-// modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda_kernel.cu +- +-#include +-#include +-#include +-#include +-#include +-#include +- +-using namespace at; +- +-#define CUDA_KERNEL_LOOP(i, n) \ +- for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \ +- i += blockDim.x * gridDim.x) +- +-const int CUDA_NUM_THREADS = 1024; +-const int kMaxGridNum = 65535; +- +-inline int GET_BLOCKS(const int N) +-{ +- return std::min(kMaxGridNum, (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS); +-} +- +-template +-__device__ scalar_t deformable_im2col_bilinear(const scalar_t *bottom_data, const int data_width, +- const int height, const int width, scalar_t h, scalar_t w) +-{ +- +- int h_low = floor(h); +- int w_low = floor(w); +- int h_high = h_low + 1; +- int w_high = w_low + 1; +- +- scalar_t lh = h - h_low; +- scalar_t lw = w - w_low; +- scalar_t hh = 1 - lh, hw = 1 - lw; +- +- scalar_t v1 = 0; +- if (h_low >= 0 && w_low >= 0) +- v1 = bottom_data[h_low * data_width + w_low]; +- scalar_t v2 = 0; +- if (h_low >= 0 && w_high <= width - 1) +- v2 = bottom_data[h_low * data_width + w_high]; +- scalar_t v3 = 0; +- if (h_high <= height - 1 && w_low >= 0) +- v3 = bottom_data[h_high * data_width + w_low]; +- scalar_t v4 = 0; +- if (h_high <= height - 1 && w_high <= width - 1) +- v4 = bottom_data[h_high * data_width + w_high]; +- +- scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; +- +- scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); +- return val; +-} +- +-template +-__device__ scalar_t get_gradient_weight(scalar_t argmax_h, scalar_t argmax_w, +- const int h, const int w, const int height, const int width) +-{ +- +- if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) +- { +- //empty +- return 0; +- } +- +- int argmax_h_low = floor(argmax_h); +- int argmax_w_low = floor(argmax_w); +- int argmax_h_high = argmax_h_low + 1; +- int argmax_w_high = argmax_w_low + 1; +- +- scalar_t weight = 0; +- if (h == argmax_h_low && w == argmax_w_low) +- weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); +- if (h == argmax_h_low && w == argmax_w_high) +- weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); +- if (h == argmax_h_high && w == argmax_w_low) +- weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); +- if (h == argmax_h_high && w == argmax_w_high) +- weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); +- return weight; +-} +- +-template +-__device__ scalar_t get_coordinate_weight(scalar_t argmax_h, scalar_t argmax_w, +- const int height, const int width, const scalar_t *im_data, +- const int data_width, const int bp_dir) +-{ +- +- if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) +- { +- //empty +- return 0; +- } +- +- int argmax_h_low = floor(argmax_h); +- int argmax_w_low = floor(argmax_w); +- int argmax_h_high = argmax_h_low + 1; +- int argmax_w_high = argmax_w_low + 1; +- +- scalar_t weight = 0; +- +- if (bp_dir == 0) +- { +- if (argmax_h_low >= 0 && argmax_w_low >= 0) +- weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low]; +- if (argmax_h_low >= 0 && argmax_w_high <= width - 1) +- weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high]; +- if (argmax_h_high <= height - 1 && argmax_w_low >= 0) +- weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low]; +- if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) +- weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high]; +- } +- else if (bp_dir == 1) +- { +- if (argmax_h_low >= 0 && argmax_w_low >= 0) +- weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low]; +- if (argmax_h_low >= 0 && argmax_w_high <= width - 1) +- weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high]; +- if (argmax_h_high <= height - 1 && argmax_w_low >= 0) +- weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low]; +- if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) +- weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high]; +- } +- +- return weight; +-} +- +-template +-__global__ void deformable_im2col_gpu_kernel(const int n, const scalar_t *data_im, const scalar_t *data_offset, +- const int height, const int width, const int kernel_h, const int kernel_w, +- const int pad_h, const int pad_w, const int stride_h, const int stride_w, +- const int dilation_h, const int dilation_w, const int channel_per_deformable_group, +- const int batch_size, const int num_channels, const int deformable_group, +- const int height_col, const int width_col, +- scalar_t *data_col) +-{ +- CUDA_KERNEL_LOOP(index, n) +- { +- // index index of output matrix +- const int w_col = index % width_col; +- const int h_col = (index / width_col) % height_col; +- const int b_col = (index / width_col / height_col) % batch_size; +- const int c_im = (index / width_col / height_col) / batch_size; +- const int c_col = c_im * kernel_h * kernel_w; +- +- // compute deformable group index +- const int deformable_group_index = c_im / channel_per_deformable_group; +- +- const int h_in = h_col * stride_h - pad_h; +- const int w_in = w_col * stride_w - pad_w; +- scalar_t *data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; +- //const scalar_t* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in; +- const scalar_t *data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width; +- const scalar_t *data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; +- +- for (int i = 0; i < kernel_h; ++i) +- { +- for (int j = 0; j < kernel_w; ++j) +- { +- const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; +- const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col; +- const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; +- const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; +- scalar_t val = static_cast(0); +- const scalar_t h_im = h_in + i * dilation_h + offset_h; +- const scalar_t w_im = w_in + j * dilation_w + offset_w; +- if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) +- { +- //const scalar_t map_h = i * dilation_h + offset_h; +- //const scalar_t map_w = j * dilation_w + offset_w; +- //const int cur_height = height - h_in; +- //const int cur_width = width - w_in; +- //val = deformable_im2col_bilinear(data_im_ptr, width, cur_height, cur_width, map_h, map_w); +- val = deformable_im2col_bilinear(data_im_ptr, width, height, width, h_im, w_im); +- } +- *data_col_ptr = val; +- data_col_ptr += batch_size * height_col * width_col; +- } +- } +- } +-} +- +-void deformable_im2col( +- const at::Tensor data_im, const at::Tensor data_offset, const int channels, +- const int height, const int width, const int ksize_h, const int ksize_w, +- const int pad_h, const int pad_w, const int stride_h, const int stride_w, +- const int dilation_h, const int dilation_w, const int parallel_imgs, +- const int deformable_group, at::Tensor data_col) +-{ +- // num_axes should be smaller than block size +- // todo: check parallel_imgs is correctly passed in +- int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; +- int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; +- int num_kernels = channels * height_col * width_col * parallel_imgs; +- int channel_per_deformable_group = channels / deformable_group; +- +- AT_DISPATCH_FLOATING_TYPES_AND_HALF( +- data_im.scalar_type(), "deformable_im2col_gpu", ([&] { +- const scalar_t *data_im_ = data_im.data_ptr(); +- const scalar_t *data_offset_ = data_offset.data_ptr(); +- scalar_t *data_col_ = data_col.data_ptr(); +- +- deformable_im2col_gpu_kernel<<>>( +- num_kernels, data_im_, data_offset_, height, width, ksize_h, ksize_w, +- pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, +- channel_per_deformable_group, parallel_imgs, channels, deformable_group, +- height_col, width_col, data_col_); +- })); +- +- cudaError_t err = cudaGetLastError(); +- if (err != cudaSuccess) +- { +- printf("error in deformable_im2col: %s\n", cudaGetErrorString(err)); +- } +-} +- +-template +-__global__ void deformable_col2im_gpu_kernel( +- const int n, const scalar_t *data_col, const scalar_t *data_offset, +- const int channels, const int height, const int width, +- const int kernel_h, const int kernel_w, +- const int pad_h, const int pad_w, +- const int stride_h, const int stride_w, +- const int dilation_h, const int dilation_w, +- const int channel_per_deformable_group, +- const int batch_size, const int deformable_group, +- const int height_col, const int width_col, +- scalar_t *grad_im) +-{ +- CUDA_KERNEL_LOOP(index, n) +- { +- const int j = (index / width_col / height_col / batch_size) % kernel_w; +- const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h; +- const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h; +- // compute the start and end of the output +- +- const int deformable_group_index = c / channel_per_deformable_group; +- +- int w_out = index % width_col; +- int h_out = (index / width_col) % height_col; +- int b = (index / width_col / height_col) % batch_size; +- int w_in = w_out * stride_w - pad_w; +- int h_in = h_out * stride_h - pad_h; +- +- const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * +- 2 * kernel_h * kernel_w * height_col * width_col; +- const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; +- const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; +- const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; +- const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; +- const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h; +- const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w; +- +- const scalar_t cur_top_grad = data_col[index]; +- const int cur_h = (int)cur_inv_h_data; +- const int cur_w = (int)cur_inv_w_data; +- for (int dy = -2; dy <= 2; dy++) +- { +- for (int dx = -2; dx <= 2; dx++) +- { +- if (cur_h + dy >= 0 && cur_h + dy < height && +- cur_w + dx >= 0 && cur_w + dx < width && +- abs(cur_inv_h_data - (cur_h + dy)) < 1 && +- abs(cur_inv_w_data - (cur_w + dx)) < 1) +- { +- int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; +- scalar_t weight = get_gradient_weight(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width); +- atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); +- } +- } +- } +- } +-} +- +-void deformable_col2im( +- const at::Tensor data_col, const at::Tensor data_offset, const int channels, +- const int height, const int width, const int ksize_h, +- const int ksize_w, const int pad_h, const int pad_w, +- const int stride_h, const int stride_w, +- const int dilation_h, const int dilation_w, +- const int parallel_imgs, const int deformable_group, +- at::Tensor grad_im) +-{ +- +- // todo: make sure parallel_imgs is passed in correctly +- int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; +- int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; +- int num_kernels = channels * ksize_h * ksize_w * height_col * width_col * parallel_imgs; +- int channel_per_deformable_group = channels / deformable_group; +- +- AT_DISPATCH_FLOATING_TYPES_AND_HALF( +- data_col.scalar_type(), "deformable_col2im_gpu", ([&] { +- const scalar_t *data_col_ = data_col.data_ptr(); +- const scalar_t *data_offset_ = data_offset.data_ptr(); +- scalar_t *grad_im_ = grad_im.data_ptr(); +- +- deformable_col2im_gpu_kernel<<>>( +- num_kernels, data_col_, data_offset_, channels, height, width, ksize_h, +- ksize_w, pad_h, pad_w, stride_h, stride_w, +- dilation_h, dilation_w, channel_per_deformable_group, +- parallel_imgs, deformable_group, height_col, width_col, grad_im_); +- })); +- +- cudaError_t err = cudaGetLastError(); +- if (err != cudaSuccess) +- { +- printf("error in deformable_col2im: %s\n", cudaGetErrorString(err)); +- } +-} +- +-template +-__global__ void deformable_col2im_coord_gpu_kernel(const int n, const scalar_t *data_col, +- const scalar_t *data_im, const scalar_t *data_offset, +- const int channels, const int height, const int width, +- const int kernel_h, const int kernel_w, +- const int pad_h, const int pad_w, +- const int stride_h, const int stride_w, +- const int dilation_h, const int dilation_w, +- const int channel_per_deformable_group, +- const int batch_size, const int offset_channels, const int deformable_group, +- const int height_col, const int width_col, scalar_t *grad_offset) +-{ +- CUDA_KERNEL_LOOP(index, n) +- { +- scalar_t val = 0; +- int w = index % width_col; +- int h = (index / width_col) % height_col; +- int c = (index / width_col / height_col) % offset_channels; +- int b = (index / width_col / height_col) / offset_channels; +- // compute the start and end of the output +- +- const int deformable_group_index = c / (2 * kernel_h * kernel_w); +- const int col_step = kernel_h * kernel_w; +- int cnt = 0; +- const scalar_t *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * +- batch_size * width_col * height_col; +- const scalar_t *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * +- channel_per_deformable_group / kernel_h / kernel_w * height * width; +- const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * +- kernel_h * kernel_w * height_col * width_col; +- +- const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; +- +- for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step) +- { +- const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w; +- const int bp_dir = offset_c % 2; +- +- int j = (col_pos / width_col / height_col / batch_size) % kernel_w; +- int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; +- int w_out = col_pos % width_col; +- int h_out = (col_pos / width_col) % height_col; +- int w_in = w_out * stride_w - pad_w; +- int h_in = h_out * stride_h - pad_h; +- const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); +- const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out); +- const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; +- const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; +- scalar_t inv_h = h_in + i * dilation_h + offset_h; +- scalar_t inv_w = w_in + j * dilation_w + offset_w; +- if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) +- { +- inv_h = inv_w = -2; +- } +- const scalar_t weight = get_coordinate_weight( +- inv_h, inv_w, +- height, width, data_im_ptr + cnt * height * width, width, bp_dir); +- val += weight * data_col_ptr[col_pos]; +- cnt += 1; +- } +- +- grad_offset[index] = val; +- } +-} +- +-void deformable_col2im_coord( +- const at::Tensor data_col, const at::Tensor data_im, const at::Tensor data_offset, +- const int channels, const int height, const int width, const int ksize_h, +- const int ksize_w, const int pad_h, const int pad_w, const int stride_h, +- const int stride_w, const int dilation_h, const int dilation_w, +- const int parallel_imgs, const int deformable_group, at::Tensor grad_offset) +-{ +- +- int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; +- int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; +- int num_kernels = height_col * width_col * 2 * ksize_h * ksize_w * deformable_group * parallel_imgs; +- int channel_per_deformable_group = channels * ksize_h * ksize_w / deformable_group; +- +- AT_DISPATCH_FLOATING_TYPES_AND_HALF( +- data_col.scalar_type(), "deformable_col2im_coord_gpu", ([&] { +- const scalar_t *data_col_ = data_col.data_ptr(); +- const scalar_t *data_im_ = data_im.data_ptr(); +- const scalar_t *data_offset_ = data_offset.data_ptr(); +- scalar_t *grad_offset_ = grad_offset.data_ptr(); +- +- deformable_col2im_coord_gpu_kernel<<>>( +- num_kernels, data_col_, data_im_, data_offset_, channels, height, width, +- ksize_h, ksize_w, pad_h, pad_w, stride_h, stride_w, +- dilation_h, dilation_w, channel_per_deformable_group, +- parallel_imgs, 2 * ksize_h * ksize_w * deformable_group, deformable_group, +- height_col, width_col, grad_offset_); +- })); +-} +- +-template +-__device__ scalar_t dmcn_im2col_bilinear(const scalar_t *bottom_data, const int data_width, +- const int height, const int width, scalar_t h, scalar_t w) +-{ +- int h_low = floor(h); +- int w_low = floor(w); +- int h_high = h_low + 1; +- int w_high = w_low + 1; +- +- scalar_t lh = h - h_low; +- scalar_t lw = w - w_low; +- scalar_t hh = 1 - lh, hw = 1 - lw; +- +- scalar_t v1 = 0; +- if (h_low >= 0 && w_low >= 0) +- v1 = bottom_data[h_low * data_width + w_low]; +- scalar_t v2 = 0; +- if (h_low >= 0 && w_high <= width - 1) +- v2 = bottom_data[h_low * data_width + w_high]; +- scalar_t v3 = 0; +- if (h_high <= height - 1 && w_low >= 0) +- v3 = bottom_data[h_high * data_width + w_low]; +- scalar_t v4 = 0; +- if (h_high <= height - 1 && w_high <= width - 1) +- v4 = bottom_data[h_high * data_width + w_high]; +- +- scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; +- +- scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); +- return val; +-} +- +-template +-__device__ scalar_t dmcn_get_gradient_weight(scalar_t argmax_h, scalar_t argmax_w, +- const int h, const int w, const int height, const int width) +-{ +- if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) +- { +- //empty +- return 0; +- } +- +- int argmax_h_low = floor(argmax_h); +- int argmax_w_low = floor(argmax_w); +- int argmax_h_high = argmax_h_low + 1; +- int argmax_w_high = argmax_w_low + 1; +- +- scalar_t weight = 0; +- if (h == argmax_h_low && w == argmax_w_low) +- weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); +- if (h == argmax_h_low && w == argmax_w_high) +- weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); +- if (h == argmax_h_high && w == argmax_w_low) +- weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); +- if (h == argmax_h_high && w == argmax_w_high) +- weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); +- return weight; +-} +- +-template +-__device__ scalar_t dmcn_get_coordinate_weight(scalar_t argmax_h, scalar_t argmax_w, +- const int height, const int width, const scalar_t *im_data, +- const int data_width, const int bp_dir) +-{ +- if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) +- { +- //empty +- return 0; +- } +- +- int argmax_h_low = floor(argmax_h); +- int argmax_w_low = floor(argmax_w); +- int argmax_h_high = argmax_h_low + 1; +- int argmax_w_high = argmax_w_low + 1; +- +- scalar_t weight = 0; +- +- if (bp_dir == 0) +- { +- if (argmax_h_low >= 0 && argmax_w_low >= 0) +- weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low]; +- if (argmax_h_low >= 0 && argmax_w_high <= width - 1) +- weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high]; +- if (argmax_h_high <= height - 1 && argmax_w_low >= 0) +- weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low]; +- if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) +- weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high]; +- } +- else if (bp_dir == 1) +- { +- if (argmax_h_low >= 0 && argmax_w_low >= 0) +- weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low]; +- if (argmax_h_low >= 0 && argmax_w_high <= width - 1) +- weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high]; +- if (argmax_h_high <= height - 1 && argmax_w_low >= 0) +- weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low]; +- if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) +- weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high]; +- } +- +- return weight; +-} +- +-template +-__global__ void modulated_deformable_im2col_gpu_kernel(const int n, +- const scalar_t *data_im, const scalar_t *data_offset, const scalar_t *data_mask, +- const int height, const int width, const int kernel_h, const int kernel_w, +- const int pad_h, const int pad_w, +- const int stride_h, const int stride_w, +- const int dilation_h, const int dilation_w, +- const int channel_per_deformable_group, +- const int batch_size, const int num_channels, const int deformable_group, +- const int height_col, const int width_col, +- scalar_t *data_col) +-{ +- CUDA_KERNEL_LOOP(index, n) +- { +- // index index of output matrix +- const int w_col = index % width_col; +- const int h_col = (index / width_col) % height_col; +- const int b_col = (index / width_col / height_col) % batch_size; +- const int c_im = (index / width_col / height_col) / batch_size; +- const int c_col = c_im * kernel_h * kernel_w; +- +- // compute deformable group index +- const int deformable_group_index = c_im / channel_per_deformable_group; +- +- const int h_in = h_col * stride_h - pad_h; +- const int w_in = w_col * stride_w - pad_w; +- +- scalar_t *data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; +- //const float* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in; +- const scalar_t *data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width; +- const scalar_t *data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; +- +- const scalar_t *data_mask_ptr = data_mask + (b_col * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; +- +- for (int i = 0; i < kernel_h; ++i) +- { +- for (int j = 0; j < kernel_w; ++j) +- { +- const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; +- const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col; +- const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_col) * width_col + w_col; +- const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; +- const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; +- const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; +- scalar_t val = static_cast(0); +- const scalar_t h_im = h_in + i * dilation_h + offset_h; +- const scalar_t w_im = w_in + j * dilation_w + offset_w; +- //if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) { +- if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) +- { +- //const float map_h = i * dilation_h + offset_h; +- //const float map_w = j * dilation_w + offset_w; +- //const int cur_height = height - h_in; +- //const int cur_width = width - w_in; +- //val = dmcn_im2col_bilinear(data_im_ptr, width, cur_height, cur_width, map_h, map_w); +- val = dmcn_im2col_bilinear(data_im_ptr, width, height, width, h_im, w_im); +- } +- *data_col_ptr = val * mask; +- data_col_ptr += batch_size * height_col * width_col; +- //data_col_ptr += height_col * width_col; +- } +- } +- } +-} +- +-template +-__global__ void modulated_deformable_col2im_gpu_kernel(const int n, +- const scalar_t *data_col, const scalar_t *data_offset, const scalar_t *data_mask, +- const int channels, const int height, const int width, +- const int kernel_h, const int kernel_w, +- const int pad_h, const int pad_w, +- const int stride_h, const int stride_w, +- const int dilation_h, const int dilation_w, +- const int channel_per_deformable_group, +- const int batch_size, const int deformable_group, +- const int height_col, const int width_col, +- scalar_t *grad_im) +-{ +- CUDA_KERNEL_LOOP(index, n) +- { +- const int j = (index / width_col / height_col / batch_size) % kernel_w; +- const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h; +- const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h; +- // compute the start and end of the output +- +- const int deformable_group_index = c / channel_per_deformable_group; +- +- int w_out = index % width_col; +- int h_out = (index / width_col) % height_col; +- int b = (index / width_col / height_col) % batch_size; +- int w_in = w_out * stride_w - pad_w; +- int h_in = h_out * stride_h - pad_h; +- +- const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; +- const scalar_t *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; +- const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; +- const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; +- const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_out) * width_col + w_out; +- const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; +- const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; +- const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; +- const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h; +- const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w; +- +- const scalar_t cur_top_grad = data_col[index] * mask; +- const int cur_h = (int)cur_inv_h_data; +- const int cur_w = (int)cur_inv_w_data; +- for (int dy = -2; dy <= 2; dy++) +- { +- for (int dx = -2; dx <= 2; dx++) +- { +- if (cur_h + dy >= 0 && cur_h + dy < height && +- cur_w + dx >= 0 && cur_w + dx < width && +- abs(cur_inv_h_data - (cur_h + dy)) < 1 && +- abs(cur_inv_w_data - (cur_w + dx)) < 1) +- { +- int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; +- scalar_t weight = dmcn_get_gradient_weight(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width); +- atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); +- } +- } +- } +- } +-} +- +-template +-__global__ void modulated_deformable_col2im_coord_gpu_kernel(const int n, +- const scalar_t *data_col, const scalar_t *data_im, +- const scalar_t *data_offset, const scalar_t *data_mask, +- const int channels, const int height, const int width, +- const int kernel_h, const int kernel_w, +- const int pad_h, const int pad_w, +- const int stride_h, const int stride_w, +- const int dilation_h, const int dilation_w, +- const int channel_per_deformable_group, +- const int batch_size, const int offset_channels, const int deformable_group, +- const int height_col, const int width_col, +- scalar_t *grad_offset, scalar_t *grad_mask) +-{ +- CUDA_KERNEL_LOOP(index, n) +- { +- scalar_t val = 0, mval = 0; +- int w = index % width_col; +- int h = (index / width_col) % height_col; +- int c = (index / width_col / height_col) % offset_channels; +- int b = (index / width_col / height_col) / offset_channels; +- // compute the start and end of the output +- +- const int deformable_group_index = c / (2 * kernel_h * kernel_w); +- const int col_step = kernel_h * kernel_w; +- int cnt = 0; +- const scalar_t *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * batch_size * width_col * height_col; +- const scalar_t *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * channel_per_deformable_group / kernel_h / kernel_w * height * width; +- const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; +- const scalar_t *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; +- +- const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; +- +- for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step) +- { +- const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w; +- const int bp_dir = offset_c % 2; +- +- int j = (col_pos / width_col / height_col / batch_size) % kernel_w; +- int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; +- int w_out = col_pos % width_col; +- int h_out = (col_pos / width_col) % height_col; +- int w_in = w_out * stride_w - pad_w; +- int h_in = h_out * stride_h - pad_h; +- const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); +- const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out); +- const int data_mask_hw_ptr = (((i * kernel_w + j) * height_col + h_out) * width_col + w_out); +- const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; +- const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; +- const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; +- scalar_t inv_h = h_in + i * dilation_h + offset_h; +- scalar_t inv_w = w_in + j * dilation_w + offset_w; +- if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) +- { +- inv_h = inv_w = -2; +- } +- else +- { +- mval += data_col_ptr[col_pos] * dmcn_im2col_bilinear(data_im_ptr + cnt * height * width, width, height, width, inv_h, inv_w); +- } +- const scalar_t weight = dmcn_get_coordinate_weight( +- inv_h, inv_w, +- height, width, data_im_ptr + cnt * height * width, width, bp_dir); +- val += weight * data_col_ptr[col_pos] * mask; +- cnt += 1; +- } +- // KERNEL_ASSIGN(grad_offset[index], offset_req, val); +- grad_offset[index] = val; +- if (offset_c % 2 == 0) +- // KERNEL_ASSIGN(grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w], mask_req, mval); +- grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w] = mval; +- } +-} +- +-void modulated_deformable_im2col_cuda( +- const at::Tensor data_im, const at::Tensor data_offset, const at::Tensor data_mask, +- const int batch_size, const int channels, const int height_im, const int width_im, +- const int height_col, const int width_col, const int kernel_h, const int kenerl_w, +- const int pad_h, const int pad_w, const int stride_h, const int stride_w, +- const int dilation_h, const int dilation_w, +- const int deformable_group, at::Tensor data_col) +-{ +- // num_axes should be smaller than block size +- const int channel_per_deformable_group = channels / deformable_group; +- const int num_kernels = channels * batch_size * height_col * width_col; +- +- AT_DISPATCH_FLOATING_TYPES_AND_HALF( +- data_im.scalar_type(), "modulated_deformable_im2col_gpu", ([&] { +- const scalar_t *data_im_ = data_im.data_ptr(); +- const scalar_t *data_offset_ = data_offset.data_ptr(); +- const scalar_t *data_mask_ = data_mask.data_ptr(); +- scalar_t *data_col_ = data_col.data_ptr(); +- +- modulated_deformable_im2col_gpu_kernel<<>>( +- num_kernels, data_im_, data_offset_, data_mask_, height_im, width_im, kernel_h, kenerl_w, +- pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, +- batch_size, channels, deformable_group, height_col, width_col, data_col_); +- })); +- +- cudaError_t err = cudaGetLastError(); +- if (err != cudaSuccess) +- { +- printf("error in modulated_deformable_im2col_cuda: %s\n", cudaGetErrorString(err)); +- } +-} +- +-void modulated_deformable_col2im_cuda( +- const at::Tensor data_col, const at::Tensor data_offset, const at::Tensor data_mask, +- const int batch_size, const int channels, const int height_im, const int width_im, +- const int height_col, const int width_col, const int kernel_h, const int kernel_w, +- const int pad_h, const int pad_w, const int stride_h, const int stride_w, +- const int dilation_h, const int dilation_w, +- const int deformable_group, at::Tensor grad_im) +-{ +- +- const int channel_per_deformable_group = channels / deformable_group; +- const int num_kernels = channels * kernel_h * kernel_w * batch_size * height_col * width_col; +- +- AT_DISPATCH_FLOATING_TYPES_AND_HALF( +- data_col.scalar_type(), "modulated_deformable_col2im_gpu", ([&] { +- const scalar_t *data_col_ = data_col.data_ptr(); +- const scalar_t *data_offset_ = data_offset.data_ptr(); +- const scalar_t *data_mask_ = data_mask.data_ptr(); +- scalar_t *grad_im_ = grad_im.data_ptr(); +- +- modulated_deformable_col2im_gpu_kernel<<>>( +- num_kernels, data_col_, data_offset_, data_mask_, channels, height_im, width_im, +- kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, +- dilation_h, dilation_w, channel_per_deformable_group, +- batch_size, deformable_group, height_col, width_col, grad_im_); +- })); +- +- cudaError_t err = cudaGetLastError(); +- if (err != cudaSuccess) +- { +- printf("error in modulated_deformable_col2im_cuda: %s\n", cudaGetErrorString(err)); +- } +-} +- +-void modulated_deformable_col2im_coord_cuda( +- const at::Tensor data_col, const at::Tensor data_im, const at::Tensor data_offset, const at::Tensor data_mask, +- const int batch_size, const int channels, const int height_im, const int width_im, +- const int height_col, const int width_col, const int kernel_h, const int kernel_w, +- const int pad_h, const int pad_w, const int stride_h, const int stride_w, +- const int dilation_h, const int dilation_w, +- const int deformable_group, +- at::Tensor grad_offset, at::Tensor grad_mask) +-{ +- const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h * kernel_w * deformable_group; +- const int channel_per_deformable_group = channels * kernel_h * kernel_w / deformable_group; +- +- AT_DISPATCH_FLOATING_TYPES_AND_HALF( +- data_col.scalar_type(), "modulated_deformable_col2im_coord_gpu", ([&] { +- const scalar_t *data_col_ = data_col.data_ptr(); +- const scalar_t *data_im_ = data_im.data_ptr(); +- const scalar_t *data_offset_ = data_offset.data_ptr(); +- const scalar_t *data_mask_ = data_mask.data_ptr(); +- scalar_t *grad_offset_ = grad_offset.data_ptr(); +- scalar_t *grad_mask_ = grad_mask.data_ptr(); +- +- modulated_deformable_col2im_coord_gpu_kernel<<>>( +- num_kernels, data_col_, data_im_, data_offset_, data_mask_, channels, height_im, width_im, +- kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, +- dilation_h, dilation_w, channel_per_deformable_group, +- batch_size, 2 * kernel_h * kernel_w * deformable_group, deformable_group, height_col, width_col, +- grad_offset_, grad_mask_); +- })); +- cudaError_t err = cudaGetLastError(); +- if (err != cudaSuccess) +- { +- printf("error in modulated_deformable_col2im_coord_cuda: %s\n", cudaGetErrorString(err)); +- } +-} +diff --git a/basicsr/ops/dcn/src/deform_conv_ext.cpp b/basicsr/ops/dcn/src/deform_conv_ext.cpp +deleted file mode 100644 +index 41c6df6..0000000 +--- a/basicsr/ops/dcn/src/deform_conv_ext.cpp ++++ /dev/null +@@ -1,164 +0,0 @@ +-// modify from +-// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c +- +-#include +-#include +- +-#include +-#include +- +-#define WITH_CUDA // always use cuda +-#ifdef WITH_CUDA +-int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight, +- at::Tensor offset, at::Tensor output, +- at::Tensor columns, at::Tensor ones, int kW, +- int kH, int dW, int dH, int padW, int padH, +- int dilationW, int dilationH, int group, +- int deformable_group, int im2col_step); +- +-int deform_conv_backward_input_cuda(at::Tensor input, at::Tensor offset, +- at::Tensor gradOutput, at::Tensor gradInput, +- at::Tensor gradOffset, at::Tensor weight, +- at::Tensor columns, int kW, int kH, int dW, +- int dH, int padW, int padH, int dilationW, +- int dilationH, int group, +- int deformable_group, int im2col_step); +- +-int deform_conv_backward_parameters_cuda( +- at::Tensor input, at::Tensor offset, at::Tensor gradOutput, +- at::Tensor gradWeight, // at::Tensor gradBias, +- at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH, +- int padW, int padH, int dilationW, int dilationH, int group, +- int deformable_group, float scale, int im2col_step); +- +-void modulated_deform_conv_cuda_forward( +- at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, +- at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns, +- int kernel_h, int kernel_w, const int stride_h, const int stride_w, +- const int pad_h, const int pad_w, const int dilation_h, +- const int dilation_w, const int group, const int deformable_group, +- const bool with_bias); +- +-void modulated_deform_conv_cuda_backward( +- at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, +- at::Tensor offset, at::Tensor mask, at::Tensor columns, +- at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias, +- at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output, +- int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, +- int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, +- const bool with_bias); +-#endif +- +-int deform_conv_forward(at::Tensor input, at::Tensor weight, +- at::Tensor offset, at::Tensor output, +- at::Tensor columns, at::Tensor ones, int kW, +- int kH, int dW, int dH, int padW, int padH, +- int dilationW, int dilationH, int group, +- int deformable_group, int im2col_step) { +- if (input.device().is_cuda()) { +-#ifdef WITH_CUDA +- return deform_conv_forward_cuda(input, weight, offset, output, columns, +- ones, kW, kH, dW, dH, padW, padH, dilationW, dilationH, group, +- deformable_group, im2col_step); +-#else +- AT_ERROR("deform conv is not compiled with GPU support"); +-#endif +- } +- AT_ERROR("deform conv is not implemented on CPU"); +-} +- +-int deform_conv_backward_input(at::Tensor input, at::Tensor offset, +- at::Tensor gradOutput, at::Tensor gradInput, +- at::Tensor gradOffset, at::Tensor weight, +- at::Tensor columns, int kW, int kH, int dW, +- int dH, int padW, int padH, int dilationW, +- int dilationH, int group, +- int deformable_group, int im2col_step) { +- if (input.device().is_cuda()) { +-#ifdef WITH_CUDA +- return deform_conv_backward_input_cuda(input, offset, gradOutput, +- gradInput, gradOffset, weight, columns, kW, kH, dW, dH, padW, padH, +- dilationW, dilationH, group, deformable_group, im2col_step); +-#else +- AT_ERROR("deform conv is not compiled with GPU support"); +-#endif +- } +- AT_ERROR("deform conv is not implemented on CPU"); +-} +- +-int deform_conv_backward_parameters( +- at::Tensor input, at::Tensor offset, at::Tensor gradOutput, +- at::Tensor gradWeight, // at::Tensor gradBias, +- at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH, +- int padW, int padH, int dilationW, int dilationH, int group, +- int deformable_group, float scale, int im2col_step) { +- if (input.device().is_cuda()) { +-#ifdef WITH_CUDA +- return deform_conv_backward_parameters_cuda(input, offset, gradOutput, +- gradWeight, columns, ones, kW, kH, dW, dH, padW, padH, dilationW, +- dilationH, group, deformable_group, scale, im2col_step); +-#else +- AT_ERROR("deform conv is not compiled with GPU support"); +-#endif +- } +- AT_ERROR("deform conv is not implemented on CPU"); +-} +- +-void modulated_deform_conv_forward( +- at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, +- at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns, +- int kernel_h, int kernel_w, const int stride_h, const int stride_w, +- const int pad_h, const int pad_w, const int dilation_h, +- const int dilation_w, const int group, const int deformable_group, +- const bool with_bias) { +- if (input.device().is_cuda()) { +-#ifdef WITH_CUDA +- return modulated_deform_conv_cuda_forward(input, weight, bias, ones, +- offset, mask, output, columns, kernel_h, kernel_w, stride_h, +- stride_w, pad_h, pad_w, dilation_h, dilation_w, group, +- deformable_group, with_bias); +-#else +- AT_ERROR("modulated deform conv is not compiled with GPU support"); +-#endif +- } +- AT_ERROR("modulated deform conv is not implemented on CPU"); +-} +- +-void modulated_deform_conv_backward( +- at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, +- at::Tensor offset, at::Tensor mask, at::Tensor columns, +- at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias, +- at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output, +- int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, +- int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, +- const bool with_bias) { +- if (input.device().is_cuda()) { +-#ifdef WITH_CUDA +- return modulated_deform_conv_cuda_backward(input, weight, bias, ones, +- offset, mask, columns, grad_input, grad_weight, grad_bias, grad_offset, +- grad_mask, grad_output, kernel_h, kernel_w, stride_h, stride_w, +- pad_h, pad_w, dilation_h, dilation_w, group, deformable_group, +- with_bias); +-#else +- AT_ERROR("modulated deform conv is not compiled with GPU support"); +-#endif +- } +- AT_ERROR("modulated deform conv is not implemented on CPU"); +-} +- +- +-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { +- m.def("deform_conv_forward", &deform_conv_forward, +- "deform forward"); +- m.def("deform_conv_backward_input", &deform_conv_backward_input, +- "deform_conv_backward_input"); +- m.def("deform_conv_backward_parameters", +- &deform_conv_backward_parameters, +- "deform_conv_backward_parameters"); +- m.def("modulated_deform_conv_forward", +- &modulated_deform_conv_forward, +- "modulated deform conv forward"); +- m.def("modulated_deform_conv_backward", +- &modulated_deform_conv_backward, +- "modulated deform conv backward"); +-} +diff --git a/basicsr/ops/fused_act/__init__.py b/basicsr/ops/fused_act/__init__.py +deleted file mode 100644 +index 241dc07..0000000 +--- a/basicsr/ops/fused_act/__init__.py ++++ /dev/null +@@ -1,3 +0,0 @@ +-from .fused_act import FusedLeakyReLU, fused_leaky_relu +- +-__all__ = ['FusedLeakyReLU', 'fused_leaky_relu'] +diff --git a/basicsr/ops/fused_act/fused_act.py b/basicsr/ops/fused_act/fused_act.py +deleted file mode 100644 +index 88edc44..0000000 +--- a/basicsr/ops/fused_act/fused_act.py ++++ /dev/null +@@ -1,95 +0,0 @@ +-# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501 +- +-import os +-import torch +-from torch import nn +-from torch.autograd import Function +- +-BASICSR_JIT = os.getenv('BASICSR_JIT') +-if BASICSR_JIT == 'True': +- from torch.utils.cpp_extension import load +- module_path = os.path.dirname(__file__) +- fused_act_ext = load( +- 'fused', +- sources=[ +- os.path.join(module_path, 'src', 'fused_bias_act.cpp'), +- os.path.join(module_path, 'src', 'fused_bias_act_kernel.cu'), +- ], +- ) +-else: +- try: +- from . import fused_act_ext +- except ImportError: +- pass +- # avoid annoying print output +- # print(f'Cannot import deform_conv_ext. Error: {error}. You may need to: \n ' +- # '1. compile with BASICSR_EXT=True. or\n ' +- # '2. set BASICSR_JIT=True during running') +- +- +-class FusedLeakyReLUFunctionBackward(Function): +- +- @staticmethod +- def forward(ctx, grad_output, out, negative_slope, scale): +- ctx.save_for_backward(out) +- ctx.negative_slope = negative_slope +- ctx.scale = scale +- +- empty = grad_output.new_empty(0) +- +- grad_input = fused_act_ext.fused_bias_act(grad_output, empty, out, 3, 1, negative_slope, scale) +- +- dim = [0] +- +- if grad_input.ndim > 2: +- dim += list(range(2, grad_input.ndim)) +- +- grad_bias = grad_input.sum(dim).detach() +- +- return grad_input, grad_bias +- +- @staticmethod +- def backward(ctx, gradgrad_input, gradgrad_bias): +- out, = ctx.saved_tensors +- gradgrad_out = fused_act_ext.fused_bias_act(gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, +- ctx.scale) +- +- return gradgrad_out, None, None, None +- +- +-class FusedLeakyReLUFunction(Function): +- +- @staticmethod +- def forward(ctx, input, bias, negative_slope, scale): +- empty = input.new_empty(0) +- out = fused_act_ext.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale) +- ctx.save_for_backward(out) +- ctx.negative_slope = negative_slope +- ctx.scale = scale +- +- return out +- +- @staticmethod +- def backward(ctx, grad_output): +- out, = ctx.saved_tensors +- +- grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(grad_output, out, ctx.negative_slope, ctx.scale) +- +- return grad_input, grad_bias, None, None +- +- +-class FusedLeakyReLU(nn.Module): +- +- def __init__(self, channel, negative_slope=0.2, scale=2**0.5): +- super().__init__() +- +- self.bias = nn.Parameter(torch.zeros(channel)) +- self.negative_slope = negative_slope +- self.scale = scale +- +- def forward(self, input): +- return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) +- +- +-def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2**0.5): +- return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale) +diff --git a/basicsr/ops/fused_act/src/fused_bias_act.cpp b/basicsr/ops/fused_act/src/fused_bias_act.cpp +deleted file mode 100644 +index 85ed0a7..0000000 +--- a/basicsr/ops/fused_act/src/fused_bias_act.cpp ++++ /dev/null +@@ -1,26 +0,0 @@ +-// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_bias_act.cpp +-#include +- +- +-torch::Tensor fused_bias_act_op(const torch::Tensor& input, +- const torch::Tensor& bias, +- const torch::Tensor& refer, +- int act, int grad, float alpha, float scale); +- +-#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") +-#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +-#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) +- +-torch::Tensor fused_bias_act(const torch::Tensor& input, +- const torch::Tensor& bias, +- const torch::Tensor& refer, +- int act, int grad, float alpha, float scale) { +- CHECK_CUDA(input); +- CHECK_CUDA(bias); +- +- return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale); +-} +- +-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { +- m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)"); +-} +diff --git a/basicsr/ops/fused_act/src/fused_bias_act_kernel.cu b/basicsr/ops/fused_act/src/fused_bias_act_kernel.cu +deleted file mode 100644 +index 54c7ff5..0000000 +--- a/basicsr/ops/fused_act/src/fused_bias_act_kernel.cu ++++ /dev/null +@@ -1,100 +0,0 @@ +-// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_bias_act_kernel.cu +-// Copyright (c) 2019, NVIDIA Corporation. All rights reserved. +-// +-// This work is made available under the Nvidia Source Code License-NC. +-// To view a copy of this license, visit +-// https://nvlabs.github.io/stylegan2/license.html +- +-#include +- +-#include +-#include +-#include +-#include +- +-#include +-#include +- +- +-template +-static __global__ void fused_bias_act_kernel(scalar_t* out, const scalar_t* p_x, const scalar_t* p_b, const scalar_t* p_ref, +- int act, int grad, scalar_t alpha, scalar_t scale, int loop_x, int size_x, int step_b, int size_b, int use_bias, int use_ref) { +- int xi = blockIdx.x * loop_x * blockDim.x + threadIdx.x; +- +- scalar_t zero = 0.0; +- +- for (int loop_idx = 0; loop_idx < loop_x && xi < size_x; loop_idx++, xi += blockDim.x) { +- scalar_t x = p_x[xi]; +- +- if (use_bias) { +- x += p_b[(xi / step_b) % size_b]; +- } +- +- scalar_t ref = use_ref ? p_ref[xi] : zero; +- +- scalar_t y; +- +- switch (act * 10 + grad) { +- default: +- case 10: y = x; break; +- case 11: y = x; break; +- case 12: y = 0.0; break; +- +- case 30: y = (x > 0.0) ? x : x * alpha; break; +- case 31: y = (ref > 0.0) ? x : x * alpha; break; +- case 32: y = 0.0; break; +- } +- +- out[xi] = y * scale; +- } +-} +- +- +-torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, +- int act, int grad, float alpha, float scale) { +- int curDevice = -1; +- cudaGetDevice(&curDevice); +- cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice); +- +- auto x = input.contiguous(); +- auto b = bias.contiguous(); +- auto ref = refer.contiguous(); +- +- int use_bias = b.numel() ? 1 : 0; +- int use_ref = ref.numel() ? 1 : 0; +- +- int size_x = x.numel(); +- int size_b = b.numel(); +- int step_b = 1; +- +- for (int i = 1 + 1; i < x.dim(); i++) { +- step_b *= x.size(i); +- } +- +- int loop_x = 4; +- int block_size = 4 * 32; +- int grid_size = (size_x - 1) / (loop_x * block_size) + 1; +- +- auto y = torch::empty_like(x); +- +- AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "fused_bias_act_kernel", [&] { +- fused_bias_act_kernel<<>>( +- y.data_ptr(), +- x.data_ptr(), +- b.data_ptr(), +- ref.data_ptr(), +- act, +- grad, +- alpha, +- scale, +- loop_x, +- size_x, +- step_b, +- size_b, +- use_bias, +- use_ref +- ); +- }); +- +- return y; +-} +diff --git a/basicsr/ops/upfirdn2d/src/upfirdn2d.cpp b/basicsr/ops/upfirdn2d/src/upfirdn2d.cpp +deleted file mode 100644 +index 43d0b67..0000000 +--- a/basicsr/ops/upfirdn2d/src/upfirdn2d.cpp ++++ /dev/null +@@ -1,24 +0,0 @@ +-// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.cpp +-#include +- +- +-torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel, +- int up_x, int up_y, int down_x, int down_y, +- int pad_x0, int pad_x1, int pad_y0, int pad_y1); +- +-#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") +-#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +-#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) +- +-torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel, +- int up_x, int up_y, int down_x, int down_y, +- int pad_x0, int pad_x1, int pad_y0, int pad_y1) { +- CHECK_CUDA(input); +- CHECK_CUDA(kernel); +- +- return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1); +-} +- +-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { +- m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)"); +-} +diff --git a/basicsr/ops/upfirdn2d/src/upfirdn2d_kernel.cu b/basicsr/ops/upfirdn2d/src/upfirdn2d_kernel.cu +deleted file mode 100644 +index 8870063..0000000 +--- a/basicsr/ops/upfirdn2d/src/upfirdn2d_kernel.cu ++++ /dev/null +@@ -1,370 +0,0 @@ +-// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d_kernel.cu +-// Copyright (c) 2019, NVIDIA Corporation. All rights reserved. +-// +-// This work is made available under the Nvidia Source Code License-NC. +-// To view a copy of this license, visit +-// https://nvlabs.github.io/stylegan2/license.html +- +-#include +- +-#include +-#include +-#include +-#include +- +-#include +-#include +- +-static __host__ __device__ __forceinline__ int floor_div(int a, int b) { +- int c = a / b; +- +- if (c * b > a) { +- c--; +- } +- +- return c; +-} +- +-struct UpFirDn2DKernelParams { +- int up_x; +- int up_y; +- int down_x; +- int down_y; +- int pad_x0; +- int pad_x1; +- int pad_y0; +- int pad_y1; +- +- int major_dim; +- int in_h; +- int in_w; +- int minor_dim; +- int kernel_h; +- int kernel_w; +- int out_h; +- int out_w; +- int loop_major; +- int loop_x; +-}; +- +-template +-__global__ void upfirdn2d_kernel_large(scalar_t *out, const scalar_t *input, +- const scalar_t *kernel, +- const UpFirDn2DKernelParams p) { +- int minor_idx = blockIdx.x * blockDim.x + threadIdx.x; +- int out_y = minor_idx / p.minor_dim; +- minor_idx -= out_y * p.minor_dim; +- int out_x_base = blockIdx.y * p.loop_x * blockDim.y + threadIdx.y; +- int major_idx_base = blockIdx.z * p.loop_major; +- +- if (out_x_base >= p.out_w || out_y >= p.out_h || +- major_idx_base >= p.major_dim) { +- return; +- } +- +- int mid_y = out_y * p.down_y + p.up_y - 1 - p.pad_y0; +- int in_y = min(max(floor_div(mid_y, p.up_y), 0), p.in_h); +- int h = min(max(floor_div(mid_y + p.kernel_h, p.up_y), 0), p.in_h) - in_y; +- int kernel_y = mid_y + p.kernel_h - (in_y + 1) * p.up_y; +- +- for (int loop_major = 0, major_idx = major_idx_base; +- loop_major < p.loop_major && major_idx < p.major_dim; +- loop_major++, major_idx++) { +- for (int loop_x = 0, out_x = out_x_base; +- loop_x < p.loop_x && out_x < p.out_w; loop_x++, out_x += blockDim.y) { +- int mid_x = out_x * p.down_x + p.up_x - 1 - p.pad_x0; +- int in_x = min(max(floor_div(mid_x, p.up_x), 0), p.in_w); +- int w = min(max(floor_div(mid_x + p.kernel_w, p.up_x), 0), p.in_w) - in_x; +- int kernel_x = mid_x + p.kernel_w - (in_x + 1) * p.up_x; +- +- const scalar_t *x_p = +- &input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * p.minor_dim + +- minor_idx]; +- const scalar_t *k_p = &kernel[kernel_y * p.kernel_w + kernel_x]; +- int x_px = p.minor_dim; +- int k_px = -p.up_x; +- int x_py = p.in_w * p.minor_dim; +- int k_py = -p.up_y * p.kernel_w; +- +- scalar_t v = 0.0f; +- +- for (int y = 0; y < h; y++) { +- for (int x = 0; x < w; x++) { +- v += static_cast(*x_p) * static_cast(*k_p); +- x_p += x_px; +- k_p += k_px; +- } +- +- x_p += x_py - w * x_px; +- k_p += k_py - w * k_px; +- } +- +- out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim + +- minor_idx] = v; +- } +- } +-} +- +-template +-__global__ void upfirdn2d_kernel(scalar_t *out, const scalar_t *input, +- const scalar_t *kernel, +- const UpFirDn2DKernelParams p) { +- const int tile_in_h = ((tile_out_h - 1) * down_y + kernel_h - 1) / up_y + 1; +- const int tile_in_w = ((tile_out_w - 1) * down_x + kernel_w - 1) / up_x + 1; +- +- __shared__ volatile float sk[kernel_h][kernel_w]; +- __shared__ volatile float sx[tile_in_h][tile_in_w]; +- +- int minor_idx = blockIdx.x; +- int tile_out_y = minor_idx / p.minor_dim; +- minor_idx -= tile_out_y * p.minor_dim; +- tile_out_y *= tile_out_h; +- int tile_out_x_base = blockIdx.y * p.loop_x * tile_out_w; +- int major_idx_base = blockIdx.z * p.loop_major; +- +- if (tile_out_x_base >= p.out_w | tile_out_y >= p.out_h | +- major_idx_base >= p.major_dim) { +- return; +- } +- +- for (int tap_idx = threadIdx.x; tap_idx < kernel_h * kernel_w; +- tap_idx += blockDim.x) { +- int ky = tap_idx / kernel_w; +- int kx = tap_idx - ky * kernel_w; +- scalar_t v = 0.0; +- +- if (kx < p.kernel_w & ky < p.kernel_h) { +- v = kernel[(p.kernel_h - 1 - ky) * p.kernel_w + (p.kernel_w - 1 - kx)]; +- } +- +- sk[ky][kx] = v; +- } +- +- for (int loop_major = 0, major_idx = major_idx_base; +- loop_major < p.loop_major & major_idx < p.major_dim; +- loop_major++, major_idx++) { +- for (int loop_x = 0, tile_out_x = tile_out_x_base; +- loop_x < p.loop_x & tile_out_x < p.out_w; +- loop_x++, tile_out_x += tile_out_w) { +- int tile_mid_x = tile_out_x * down_x + up_x - 1 - p.pad_x0; +- int tile_mid_y = tile_out_y * down_y + up_y - 1 - p.pad_y0; +- int tile_in_x = floor_div(tile_mid_x, up_x); +- int tile_in_y = floor_div(tile_mid_y, up_y); +- +- __syncthreads(); +- +- for (int in_idx = threadIdx.x; in_idx < tile_in_h * tile_in_w; +- in_idx += blockDim.x) { +- int rel_in_y = in_idx / tile_in_w; +- int rel_in_x = in_idx - rel_in_y * tile_in_w; +- int in_x = rel_in_x + tile_in_x; +- int in_y = rel_in_y + tile_in_y; +- +- scalar_t v = 0.0; +- +- if (in_x >= 0 & in_y >= 0 & in_x < p.in_w & in_y < p.in_h) { +- v = input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * +- p.minor_dim + +- minor_idx]; +- } +- +- sx[rel_in_y][rel_in_x] = v; +- } +- +- __syncthreads(); +- for (int out_idx = threadIdx.x; out_idx < tile_out_h * tile_out_w; +- out_idx += blockDim.x) { +- int rel_out_y = out_idx / tile_out_w; +- int rel_out_x = out_idx - rel_out_y * tile_out_w; +- int out_x = rel_out_x + tile_out_x; +- int out_y = rel_out_y + tile_out_y; +- +- int mid_x = tile_mid_x + rel_out_x * down_x; +- int mid_y = tile_mid_y + rel_out_y * down_y; +- int in_x = floor_div(mid_x, up_x); +- int in_y = floor_div(mid_y, up_y); +- int rel_in_x = in_x - tile_in_x; +- int rel_in_y = in_y - tile_in_y; +- int kernel_x = (in_x + 1) * up_x - mid_x - 1; +- int kernel_y = (in_y + 1) * up_y - mid_y - 1; +- +- scalar_t v = 0.0; +- +-#pragma unroll +- for (int y = 0; y < kernel_h / up_y; y++) +-#pragma unroll +- for (int x = 0; x < kernel_w / up_x; x++) +- v += sx[rel_in_y + y][rel_in_x + x] * +- sk[kernel_y + y * up_y][kernel_x + x * up_x]; +- +- if (out_x < p.out_w & out_y < p.out_h) { +- out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim + +- minor_idx] = v; +- } +- } +- } +- } +-} +- +-torch::Tensor upfirdn2d_op(const torch::Tensor &input, +- const torch::Tensor &kernel, int up_x, int up_y, +- int down_x, int down_y, int pad_x0, int pad_x1, +- int pad_y0, int pad_y1) { +- int curDevice = -1; +- cudaGetDevice(&curDevice); +- cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice); +- +- UpFirDn2DKernelParams p; +- +- auto x = input.contiguous(); +- auto k = kernel.contiguous(); +- +- p.major_dim = x.size(0); +- p.in_h = x.size(1); +- p.in_w = x.size(2); +- p.minor_dim = x.size(3); +- p.kernel_h = k.size(0); +- p.kernel_w = k.size(1); +- p.up_x = up_x; +- p.up_y = up_y; +- p.down_x = down_x; +- p.down_y = down_y; +- p.pad_x0 = pad_x0; +- p.pad_x1 = pad_x1; +- p.pad_y0 = pad_y0; +- p.pad_y1 = pad_y1; +- +- p.out_h = (p.in_h * p.up_y + p.pad_y0 + p.pad_y1 - p.kernel_h + p.down_y) / +- p.down_y; +- p.out_w = (p.in_w * p.up_x + p.pad_x0 + p.pad_x1 - p.kernel_w + p.down_x) / +- p.down_x; +- +- auto out = +- at::empty({p.major_dim, p.out_h, p.out_w, p.minor_dim}, x.options()); +- +- int mode = -1; +- +- int tile_out_h = -1; +- int tile_out_w = -1; +- +- if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && +- p.kernel_h <= 4 && p.kernel_w <= 4) { +- mode = 1; +- tile_out_h = 16; +- tile_out_w = 64; +- } +- +- if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && +- p.kernel_h <= 3 && p.kernel_w <= 3) { +- mode = 2; +- tile_out_h = 16; +- tile_out_w = 64; +- } +- +- if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && +- p.kernel_h <= 4 && p.kernel_w <= 4) { +- mode = 3; +- tile_out_h = 16; +- tile_out_w = 64; +- } +- +- if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && +- p.kernel_h <= 2 && p.kernel_w <= 2) { +- mode = 4; +- tile_out_h = 16; +- tile_out_w = 64; +- } +- +- if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && +- p.kernel_h <= 4 && p.kernel_w <= 4) { +- mode = 5; +- tile_out_h = 8; +- tile_out_w = 32; +- } +- +- if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && +- p.kernel_h <= 2 && p.kernel_w <= 2) { +- mode = 6; +- tile_out_h = 8; +- tile_out_w = 32; +- } +- +- dim3 block_size; +- dim3 grid_size; +- +- if (tile_out_h > 0 && tile_out_w > 0) { +- p.loop_major = (p.major_dim - 1) / 16384 + 1; +- p.loop_x = 1; +- block_size = dim3(32 * 8, 1, 1); +- grid_size = dim3(((p.out_h - 1) / tile_out_h + 1) * p.minor_dim, +- (p.out_w - 1) / (p.loop_x * tile_out_w) + 1, +- (p.major_dim - 1) / p.loop_major + 1); +- } else { +- p.loop_major = (p.major_dim - 1) / 16384 + 1; +- p.loop_x = 4; +- block_size = dim3(4, 32, 1); +- grid_size = dim3((p.out_h * p.minor_dim - 1) / block_size.x + 1, +- (p.out_w - 1) / (p.loop_x * block_size.y) + 1, +- (p.major_dim - 1) / p.loop_major + 1); +- } +- +- AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] { +- switch (mode) { +- case 1: +- upfirdn2d_kernel +- <<>>(out.data_ptr(), +- x.data_ptr(), +- k.data_ptr(), p); +- +- break; +- +- case 2: +- upfirdn2d_kernel +- <<>>(out.data_ptr(), +- x.data_ptr(), +- k.data_ptr(), p); +- +- break; +- +- case 3: +- upfirdn2d_kernel +- <<>>(out.data_ptr(), +- x.data_ptr(), +- k.data_ptr(), p); +- +- break; +- +- case 4: +- upfirdn2d_kernel +- <<>>(out.data_ptr(), +- x.data_ptr(), +- k.data_ptr(), p); +- +- break; +- +- case 5: +- upfirdn2d_kernel +- <<>>(out.data_ptr(), +- x.data_ptr(), +- k.data_ptr(), p); +- +- break; +- +- case 6: +- upfirdn2d_kernel +- <<>>(out.data_ptr(), +- x.data_ptr(), +- k.data_ptr(), p); +- +- break; +- +- default: +- upfirdn2d_kernel_large<<>>( +- out.data_ptr(), x.data_ptr(), +- k.data_ptr(), p); +- } +- }); +- +- return out; +-} +diff --git a/basicsr/ops/upfirdn2d/upfirdn2d.py b/basicsr/ops/upfirdn2d/upfirdn2d.py +index d6122d5..4768ec9 100644 +--- a/basicsr/ops/upfirdn2d/upfirdn2d.py ++++ b/basicsr/ops/upfirdn2d/upfirdn2d.py +@@ -2,161 +2,11 @@ + + import os + import torch +-from torch.autograd import Function + from torch.nn import functional as F + +-BASICSR_JIT = os.getenv('BASICSR_JIT') +-if BASICSR_JIT == 'True': +- from torch.utils.cpp_extension import load +- module_path = os.path.dirname(__file__) +- upfirdn2d_ext = load( +- 'upfirdn2d', +- sources=[ +- os.path.join(module_path, 'src', 'upfirdn2d.cpp'), +- os.path.join(module_path, 'src', 'upfirdn2d_kernel.cu'), +- ], +- ) +-else: +- try: +- from . import upfirdn2d_ext +- except ImportError: +- pass +- # avoid annoying print output +- # print(f'Cannot import deform_conv_ext. Error: {error}. You may need to: \n ' +- # '1. compile with BASICSR_EXT=True. or\n ' +- # '2. set BASICSR_JIT=True during running') +- +- +-class UpFirDn2dBackward(Function): +- +- @staticmethod +- def forward(ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size): +- +- up_x, up_y = up +- down_x, down_y = down +- g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad +- +- grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1) +- +- grad_input = upfirdn2d_ext.upfirdn2d( +- grad_output, +- grad_kernel, +- down_x, +- down_y, +- up_x, +- up_y, +- g_pad_x0, +- g_pad_x1, +- g_pad_y0, +- g_pad_y1, +- ) +- grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3]) +- +- ctx.save_for_backward(kernel) +- +- pad_x0, pad_x1, pad_y0, pad_y1 = pad +- +- ctx.up_x = up_x +- ctx.up_y = up_y +- ctx.down_x = down_x +- ctx.down_y = down_y +- ctx.pad_x0 = pad_x0 +- ctx.pad_x1 = pad_x1 +- ctx.pad_y0 = pad_y0 +- ctx.pad_y1 = pad_y1 +- ctx.in_size = in_size +- ctx.out_size = out_size +- +- return grad_input +- +- @staticmethod +- def backward(ctx, gradgrad_input): +- kernel, = ctx.saved_tensors +- +- gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1) +- +- gradgrad_out = upfirdn2d_ext.upfirdn2d( +- gradgrad_input, +- kernel, +- ctx.up_x, +- ctx.up_y, +- ctx.down_x, +- ctx.down_y, +- ctx.pad_x0, +- ctx.pad_x1, +- ctx.pad_y0, +- ctx.pad_y1, +- ) +- # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], +- # ctx.out_size[1], ctx.in_size[3]) +- gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1]) +- +- return gradgrad_out, None, None, None, None, None, None, None, None +- +- +-class UpFirDn2d(Function): +- +- @staticmethod +- def forward(ctx, input, kernel, up, down, pad): +- up_x, up_y = up +- down_x, down_y = down +- pad_x0, pad_x1, pad_y0, pad_y1 = pad +- +- kernel_h, kernel_w = kernel.shape +- _, channel, in_h, in_w = input.shape +- ctx.in_size = input.shape +- +- input = input.reshape(-1, in_h, in_w, 1) +- +- ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1])) +- +- out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 +- out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 +- ctx.out_size = (out_h, out_w) +- +- ctx.up = (up_x, up_y) +- ctx.down = (down_x, down_y) +- ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1) +- +- g_pad_x0 = kernel_w - pad_x0 - 1 +- g_pad_y0 = kernel_h - pad_y0 - 1 +- g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1 +- g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1 +- +- ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1) +- +- out = upfirdn2d_ext.upfirdn2d(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1) +- # out = out.view(major, out_h, out_w, minor) +- out = out.view(-1, channel, out_h, out_w) +- +- return out +- +- @staticmethod +- def backward(ctx, grad_output): +- kernel, grad_kernel = ctx.saved_tensors +- +- grad_input = UpFirDn2dBackward.apply( +- grad_output, +- kernel, +- grad_kernel, +- ctx.up, +- ctx.down, +- ctx.pad, +- ctx.g_pad, +- ctx.in_size, +- ctx.out_size, +- ) +- +- return grad_input, None, None, None, None +- + + def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): +- if input.device.type == 'cpu': +- out = upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]) +- else: +- out = UpFirDn2d.apply(input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1])) +- +- return out ++ return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]) + + + def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1): +diff --git a/basicsr/utils/dist_util.py b/basicsr/utils/dist_util.py +index 0fab887..2d0ae71 100644 +--- a/basicsr/utils/dist_util.py ++++ b/basicsr/utils/dist_util.py +@@ -20,8 +20,6 @@ def init_dist(launcher, backend='nccl', **kwargs): + + def _init_dist_pytorch(backend, **kwargs): + rank = int(os.environ['RANK']) +- num_gpus = torch.cuda.device_count() +- torch.cuda.set_device(rank % num_gpus) + dist.init_process_group(backend=backend, **kwargs) + + +@@ -39,8 +37,6 @@ def _init_dist_slurm(backend, port=None): + proc_id = int(os.environ['SLURM_PROCID']) + ntasks = int(os.environ['SLURM_NTASKS']) + node_list = os.environ['SLURM_NODELIST'] +- num_gpus = torch.cuda.device_count() +- torch.cuda.set_device(proc_id % num_gpus) + addr = subprocess.getoutput(f'scontrol show hostname {node_list} | head -n1') + # specify master port + if port is not None: +@@ -52,8 +48,6 @@ def _init_dist_slurm(backend, port=None): + os.environ['MASTER_PORT'] = '29500' + os.environ['MASTER_ADDR'] = addr + os.environ['WORLD_SIZE'] = str(ntasks) +- os.environ['LOCAL_RANK'] = str(proc_id % num_gpus) +- os.environ['RANK'] = str(proc_id) + dist.init_process_group(backend=backend) + + +diff --git a/basicsr/utils/options.py b/basicsr/utils/options.py +index 09bfa5a..f4333e9 100644 +--- a/basicsr/utils/options.py ++++ b/basicsr/utils/options.py +@@ -134,8 +134,7 @@ def parse_options(root_path, is_train=True): + if args.debug and not opt['name'].startswith('debug'): + opt['name'] = 'debug_' + opt['name'] + +- if opt['num_gpu'] == 'auto': +- opt['num_gpu'] = torch.cuda.device_count() ++ opt['num_gpu'] = 0 + + # datasets + for phase, dataset in opt['datasets'].items(): +diff --git a/inference/inference_basicvsr.py b/inference/inference_basicvsr.py +index 7b5e4b9..a55a182 100644 +--- a/inference/inference_basicvsr.py ++++ b/inference/inference_basicvsr.py +@@ -30,7 +30,7 @@ def main(): + parser.add_argument('--interval', type=int, default=15, help='interval size') + args = parser.parse_args() + +- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') ++ device = torch.device('cpu') + + # set up model + model = BasicVSR(num_feat=64, num_block=30) +diff --git a/inference/inference_basicvsrpp.py b/inference/inference_basicvsrpp.py +index b44aaa4..9cbb988 100644 +--- a/inference/inference_basicvsrpp.py ++++ b/inference/inference_basicvsrpp.py +@@ -30,7 +30,7 @@ def main(): + parser.add_argument('--interval', type=int, default=100, help='interval size') + args = parser.parse_args() + +- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') ++ device = torch.device('cpu') + + # set up model + model = BasicVSRPlusPlus(mid_channels=64, num_blocks=7) +diff --git a/inference/inference_dfdnet.py b/inference/inference_dfdnet.py +index 64a7a64..3a594ad 100644 +--- a/inference/inference_dfdnet.py ++++ b/inference/inference_dfdnet.py +@@ -60,7 +60,7 @@ if __name__ == '__main__': + differences: 1) we use dlib for 68 landmark detection; 2) the used image + package are different (especially for reading and writing.) + """ +- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') ++ device = torch.device('cpu') + parser = argparse.ArgumentParser() + + parser.add_argument('--upscale_factor', type=int, default=2) +diff --git a/inference/inference_esrgan.py b/inference/inference_esrgan.py +index e425b13..4dd3e6c 100644 +--- a/inference/inference_esrgan.py ++++ b/inference/inference_esrgan.py +@@ -20,7 +20,7 @@ def main(): + parser.add_argument('--output', type=str, default='results/ESRGAN', help='output folder') + args = parser.parse_args() + +- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') ++ device = torch.device('cpu') + # set up model + model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32) + model.load_state_dict(torch.load(args.model_path)['params'], strict=True) +diff --git a/inference/inference_ridnet.py b/inference/inference_ridnet.py +index 9825ba8..608efe2 100644 +--- a/inference/inference_ridnet.py ++++ b/inference/inference_ridnet.py +@@ -10,7 +10,7 @@ from basicsr.archs.ridnet_arch import RIDNet + from basicsr.utils.img_util import img2tensor, tensor2img + + if __name__ == '__main__': +- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') ++ device = torch.device('cpu') + parser = argparse.ArgumentParser() + parser.add_argument('--test_path', type=str, default='datasets/denoise/RNI15') + parser.add_argument('--noise_g', type=int, default=25) +diff --git a/inference/inference_stylegan2.py b/inference/inference_stylegan2.py +index 52545ac..9348cab 100644 +--- a/inference/inference_stylegan2.py ++++ b/inference/inference_stylegan2.py +@@ -30,7 +30,7 @@ def generate(args, g_ema, device, mean_latent, randomize_noise): + + + if __name__ == '__main__': +- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') ++ device = torch.device('cpu') + + parser = argparse.ArgumentParser() + +diff --git a/inference/inference_swinir.py b/inference/inference_swinir.py +index 28e9bde..cfa59b0 100644 +--- a/inference/inference_swinir.py ++++ b/inference/inference_swinir.py +@@ -33,7 +33,7 @@ def main(): + args = parser.parse_args() + + os.makedirs(args.output, exist_ok=True) +- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') ++ device = torch.device('cpu') + # set up model + model = define_model(args) + model.eval() +diff --git a/scripts/metrics/calculate_fid_folder.py b/scripts/metrics/calculate_fid_folder.py +index 71b02e1..33bfe92 100644 +--- a/scripts/metrics/calculate_fid_folder.py ++++ b/scripts/metrics/calculate_fid_folder.py +@@ -9,7 +9,7 @@ from basicsr.metrics.fid import calculate_fid, extract_inception_features, load_ + + + def calculate_fid_folder(): +- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') ++ device = torch.device('cpu') + + parser = argparse.ArgumentParser() + parser.add_argument('folder', type=str, help='Path to the folder.') +diff --git a/scripts/metrics/calculate_fid_stats_from_datasets.py b/scripts/metrics/calculate_fid_stats_from_datasets.py +index 56e3529..d5858d7 100644 +--- a/scripts/metrics/calculate_fid_stats_from_datasets.py ++++ b/scripts/metrics/calculate_fid_stats_from_datasets.py +@@ -9,7 +9,7 @@ from basicsr.metrics.fid import extract_inception_features, load_patched_incepti + + + def calculate_stats_from_dataset(): +- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') ++ device = torch.device('cpu') + + parser = argparse.ArgumentParser() + parser.add_argument('--num_sample', type=int, default=50000) +diff --git a/scripts/metrics/calculate_stylegan2_fid.py b/scripts/metrics/calculate_stylegan2_fid.py +index c5564b8..1723374 100644 +--- a/scripts/metrics/calculate_stylegan2_fid.py ++++ b/scripts/metrics/calculate_stylegan2_fid.py +@@ -9,7 +9,7 @@ from basicsr.metrics.fid import calculate_fid, extract_inception_features, load_ + + + def calculate_stylegan2_fid(): +- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') ++ device = torch.device('cpu') + + parser = argparse.ArgumentParser() + parser.add_argument('ckpt', type=str, help='Path to the stylegan2 checkpoint.') +diff --git a/setup.py b/setup.py +index bc228e4..e8999c2 100644 +--- a/setup.py ++++ b/setup.py +@@ -79,32 +79,6 @@ def get_version(): + return locals()['__version__'] + + +-def make_cuda_ext(name, module, sources, sources_cuda=None): +- if sources_cuda is None: +- sources_cuda = [] +- define_macros = [] +- extra_compile_args = {'cxx': []} +- +- if torch.cuda.is_available() or os.getenv('FORCE_CUDA', '0') == '1': +- define_macros += [('WITH_CUDA', None)] +- extension = CUDAExtension +- extra_compile_args['nvcc'] = [ +- '-D__CUDA_NO_HALF_OPERATORS__', +- '-D__CUDA_NO_HALF_CONVERSIONS__', +- '-D__CUDA_NO_HALF2_OPERATORS__', +- ] +- sources += sources_cuda +- else: +- print(f'Compiling {name} without CUDA') +- extension = CppExtension +- +- return extension( +- name=f'{module}.{name}', +- sources=[os.path.join(*module.split('.'), p) for p in sources], +- define_macros=define_macros, +- extra_compile_args=extra_compile_args) +- +- + def get_requirements(filename='requirements.txt'): + here = os.path.dirname(os.path.realpath(__file__)) + with open(os.path.join(here, filename), 'r') as f: +@@ -113,36 +87,6 @@ def get_requirements(filename='requirements.txt'): + + + if __name__ == '__main__': +- cuda_ext = os.getenv('BASICSR_EXT') # whether compile cuda ext +- if cuda_ext == 'True': +- try: +- import torch +- from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension +- except ImportError: +- raise ImportError('Unable to import torch - torch is needed to build cuda extensions') +- +- ext_modules = [ +- make_cuda_ext( +- name='deform_conv_ext', +- module='basicsr.ops.dcn', +- sources=['src/deform_conv_ext.cpp'], +- sources_cuda=['src/deform_conv_cuda.cpp', 'src/deform_conv_cuda_kernel.cu']), +- make_cuda_ext( +- name='fused_act_ext', +- module='basicsr.ops.fused_act', +- sources=['src/fused_bias_act.cpp'], +- sources_cuda=['src/fused_bias_act_kernel.cu']), +- make_cuda_ext( +- name='upfirdn2d_ext', +- module='basicsr.ops.upfirdn2d', +- sources=['src/upfirdn2d.cpp'], +- sources_cuda=['src/upfirdn2d_kernel.cu']), +- ] +- setup_kwargs = dict(cmdclass={'build_ext': BuildExtension}) +- else: +- ext_modules = [] +- setup_kwargs = dict() +- + write_version_py() + setup( + name='basicsr', +@@ -167,6 +111,4 @@ if __name__ == '__main__': + license='Apache License 2.0', + setup_requires=['cython', 'numpy', 'torch'], + install_requires=get_requirements(), +- ext_modules=ext_modules, +- zip_safe=False, +- **setup_kwargs) ++ zip_safe=False) From patchwork Fri Nov 25 20:37:58 2022 Content-Type: text/plain; charset="utf-8" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit X-Patchwork-Submitter: Liliana Marie Prikler X-Patchwork-Id: 44876 Return-Path: X-Original-To: patchwork@mira.cbaines.net Delivered-To: patchwork@mira.cbaines.net Received: by mira.cbaines.net (Postfix, from userid 113) id 774E727BBE9; Sat, 26 Nov 2022 12:14:18 +0000 (GMT) X-Spam-Checker-Version: SpamAssassin 3.4.6 (2021-04-09) on mira.cbaines.net X-Spam-Level: X-Spam-Status: No, score=-3.7 required=5.0 tests=BAYES_00,DKIM_ADSP_CUSTOM_MED, DKIM_INVALID,DKIM_SIGNED,FREEMAIL_FROM,MAILING_LIST_MULTI, RCVD_IN_MSPIKE_H2,SPF_HELO_PASS,URIBL_BLOCKED autolearn=ham autolearn_force=no version=3.4.6 Received: from lists.gnu.org (lists.gnu.org [209.51.188.17]) by mira.cbaines.net (Postfix) with ESMTPS id 9354227BBED for ; Sat, 26 Nov 2022 12:14:11 +0000 (GMT) Received: from localhost ([::1] helo=lists1p.gnu.org) by lists.gnu.org with esmtp (Exim 4.90_1) (envelope-from ) id 1oyu3Z-0004sF-6X; Sat, 26 Nov 2022 07:13:17 -0500 Received: from eggs.gnu.org ([2001:470:142:3::10]) by lists.gnu.org with esmtps (TLS1.2:ECDHE_RSA_AES_256_GCM_SHA384:256) (Exim 4.90_1) (envelope-from ) id 1oyu3M-0004pj-1J for guix-patches@gnu.org; Sat, 26 Nov 2022 07:13:04 -0500 Received: from debbugs.gnu.org ([209.51.188.43]) by eggs.gnu.org with esmtps (TLS1.2:ECDHE_RSA_AES_128_GCM_SHA256:128) (Exim 4.90_1) (envelope-from ) id 1oyu3L-0005LO-P2 for guix-patches@gnu.org; Sat, 26 Nov 2022 07:13:03 -0500 Received: from Debian-debbugs by debbugs.gnu.org with local (Exim 4.84_2) (envelope-from ) id 1oyu3K-00018S-SC for guix-patches@gnu.org; Sat, 26 Nov 2022 07:13:02 -0500 X-Loop: help-debbugs@gnu.org Subject: [bug#59607] [PATCH 5/8] gnu: Add python-filterpy. 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[85.127.52.93]) by smtp.gmail.com with ESMTPSA id az26-20020a05600c601a00b003d01b84e9b2sm8695059wmb.27.2022.11.26.04.11.55 for <59607@debbugs.gnu.org> (version=TLS1_3 cipher=TLS_AES_256_GCM_SHA384 bits=256/256); Sat, 26 Nov 2022 04:11:56 -0800 (PST) From: Liliana Marie Prikler Date: Fri, 25 Nov 2022 21:37:58 +0100 Message-ID: <7657246e20fc5dede55b714864d8eae90ec73dfd.camel@gmail.com> MIME-Version: 1.0 X-BeenThere: debbugs-submit@debbugs.gnu.org X-Mailman-Version: 2.1.18 Precedence: list X-BeenThere: guix-patches@gnu.org List-Id: List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , Errors-To: guix-patches-bounces+patchwork=mira.cbaines.net@gnu.org Sender: guix-patches-bounces+patchwork=mira.cbaines.net@gnu.org X-getmail-retrieved-from-mailbox: Patches * gnu/packages/python-science.scm (python-filterpy): New variable. --- gnu/packages/python-science.scm | 50 +++++++++++++++++++++++++++++++++ 1 file changed, 50 insertions(+) diff --git a/gnu/packages/python-science.scm b/gnu/packages/python-science.scm index 52fe1460bb..d69e43be4e 100644 --- a/gnu/packages/python-science.scm +++ b/gnu/packages/python-science.scm @@ -634,6 +634,56 @@ (define-public python-fbpca analysis} (PCA), SVD, and eigendecompositions via randomized methods") (license license:bsd-3))) +(define-public python-filterpy + (package + (name "python-filterpy") + (version "1.4.5") + (source (origin + (method git-fetch) + (uri (git-reference + (url "https://github.com/rlabbe/filterpy") + (commit version))) + (sha256 + (base32 + "1i7v7jfi0ysc2rz9fkxyrmdbh4a1ahcn6vgjajj0zs6wla3jnmxm")))) + (build-system python-build-system) + (arguments + (list #:phases + #~(modify-phases %standard-phases + (add-before 'check 'mark-failing-tests + (lambda _ + (substitute* "filterpy/kalman/tests/test_kf.py" + (("from __future__ import .*" all) + (string-append all "\nimport pytest\n")) + (("def test_(noisy_1d|steadystate)" all) + (string-append "@pytest.mark.xfail()\n" + all))) + (substitute* "filterpy/kalman/tests/test_fm.py" + (("from pytest import .*" all) + (string-append all "\nimport pytest\n")) + (("def test_noisy_1d" all) + (string-append "@pytest.mark.xfail()\n" + all))) + (substitute* "filterpy/stats/tests/test_stats.py" + (("from __future__ import .*" all) + (string-append all "\nimport pytest\n")) + (("def test_mahalanobis" all) + (string-append "@pytest.mark.xfail()\n" + all))))) + (replace 'check + (lambda* (#:key tests? #:allow-other-keys) + (when tests? + (invoke "pytest" "-vv"))))))) + (propagated-inputs (list python-numpy python-scipy)) + (native-inputs (list python-pytest)) + (home-page "https://filterpy.readthedocs.io/") + (synopsis "Kalman and Bayesian filters") + (description + "This package provides implementations of various filters, such as the +Kalman filter, its extended and unscented variants, recursive least squares, +and others.") + (license license:expat))) + (define-public python-geosketch (package (name "python-geosketch") From patchwork Fri Nov 25 22:31:24 2022 Content-Type: text/plain; charset="utf-8" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit X-Patchwork-Submitter: Liliana Marie Prikler X-Patchwork-Id: 44871 Return-Path: X-Original-To: patchwork@mira.cbaines.net Delivered-To: patchwork@mira.cbaines.net Received: by mira.cbaines.net (Postfix, from userid 113) id BB2FC27BBED; Sat, 26 Nov 2022 12:13:53 +0000 (GMT) X-Spam-Checker-Version: SpamAssassin 3.4.6 (2021-04-09) on mira.cbaines.net X-Spam-Level: X-Spam-Status: No, score=-3.7 required=5.0 tests=BAYES_00,DKIM_ADSP_CUSTOM_MED, DKIM_INVALID,DKIM_SIGNED,FREEMAIL_FROM,MAILING_LIST_MULTI, RCVD_IN_MSPIKE_H2,SPF_HELO_PASS autolearn=ham autolearn_force=no version=3.4.6 Received: from lists.gnu.org (lists.gnu.org [209.51.188.17]) by mira.cbaines.net (Postfix) with ESMTPS id 2DCC527BBE9 for ; Sat, 26 Nov 2022 12:13:53 +0000 (GMT) Received: from localhost ([::1] helo=lists1p.gnu.org) by lists.gnu.org with esmtp (Exim 4.90_1) (envelope-from ) id 1oyu3e-0004ta-RX; Sat, 26 Nov 2022 07:13:22 -0500 Received: from eggs.gnu.org ([2001:470:142:3::10]) by lists.gnu.org with esmtps (TLS1.2:ECDHE_RSA_AES_256_GCM_SHA384:256) (Exim 4.90_1) (envelope-from ) id 1oyu3M-0004pk-4O for guix-patches@gnu.org; Sat, 26 Nov 2022 07:13:04 -0500 Received: from debbugs.gnu.org ([209.51.188.43]) by eggs.gnu.org with esmtps (TLS1.2:ECDHE_RSA_AES_128_GCM_SHA256:128) (Exim 4.90_1) (envelope-from ) id 1oyu3L-0005LX-Rr for guix-patches@gnu.org; Sat, 26 Nov 2022 07:13:03 -0500 Received: from Debian-debbugs by debbugs.gnu.org with local (Exim 4.84_2) (envelope-from ) id 1oyu3L-00018h-Nw for guix-patches@gnu.org; Sat, 26 Nov 2022 07:13:03 -0500 X-Loop: help-debbugs@gnu.org Subject: [bug#59607] [PATCH 6/8] gnu: Add python-facexlib. 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[85.127.52.93]) by smtp.gmail.com with ESMTPSA id az26-20020a05600c601a00b003d01b84e9b2sm8695059wmb.27.2022.11.26.04.11.56 for <59607@debbugs.gnu.org> (version=TLS1_3 cipher=TLS_AES_256_GCM_SHA384 bits=256/256); Sat, 26 Nov 2022 04:11:56 -0800 (PST) From: Liliana Marie Prikler Date: Fri, 25 Nov 2022 23:31:24 +0100 Message-ID: <237e70d8b319a981b9b992e4d7577aca8a1e988f.camel@gmail.com> MIME-Version: 1.0 X-BeenThere: debbugs-submit@debbugs.gnu.org X-Mailman-Version: 2.1.18 Precedence: list X-BeenThere: guix-patches@gnu.org List-Id: List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , Errors-To: guix-patches-bounces+patchwork=mira.cbaines.net@gnu.org Sender: guix-patches-bounces+patchwork=mira.cbaines.net@gnu.org X-getmail-retrieved-from-mailbox: Patches * gnu/packages/machine-learning.scm (python-facexlib): New variable. --- gnu/packages/machine-learning.scm | 43 +++++++++++++++++++++++++++++++ 1 file changed, 43 insertions(+) diff --git a/gnu/packages/machine-learning.scm b/gnu/packages/machine-learning.scm index a5767a2c31..6116834578 100644 --- a/gnu/packages/machine-learning.scm +++ b/gnu/packages/machine-learning.scm @@ -816,6 +816,49 @@ (define-public python-basicsr artifacts.") (license license:asl2.0))) +(define-public python-facexlib + (package + (name "python-facexlib") + (version "0.2.5") + (source (origin + (method url-fetch) + (uri (pypi-uri "facexlib" version)) + (modules '((guix build utils))) + (snippet + #~(begin (substitute* (find-files "." "\\.py$") + (("\\.cuda\\(\\)") "") + (("'cuda' if .* else ") "") + (("'cuda'") "'cpu'")))) + (sha256 + (base32 + "1r378mb167k2hynrn1wsi78xbh2aw6x68i8f70nmcqsxxp20rqii")))) + (build-system python-build-system) + (arguments + (list #:tests? #f ; No tests + #:phases + #~(modify-phases %standard-phases + (add-after 'unpack 'fix-requirements + (lambda _ + (substitute* "requirements.txt" + (("opencv-python") ""))))))) ; installed without egg-info + (propagated-inputs (list opencv + python-filterpy + python-numba + python-numpy + python-pillow + python-scipy + python-pytorch + python-torchvision + python-tqdm)) + (native-inputs (list python-cython)) + (home-page "https://github.com/xinntao/facexlib") + (synopsis "Basic face library") + (description "This package provides a collection of face-related +functions, such as detection, alignment, recognition or tracking.") + (license (list license:gpl3+ + license:asl2.0 + license:expat)))) + (define-public ncnn (package (name "ncnn") From patchwork Sat Nov 26 10:46:23 2022 Content-Type: text/plain; charset="utf-8" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit X-Patchwork-Submitter: Liliana Marie Prikler X-Patchwork-Id: 44870 Return-Path: X-Original-To: patchwork@mira.cbaines.net Delivered-To: patchwork@mira.cbaines.net Received: by mira.cbaines.net (Postfix, from userid 113) id 05D3D27BBEC; Sat, 26 Nov 2022 12:13:44 +0000 (GMT) X-Spam-Checker-Version: SpamAssassin 3.4.6 (2021-04-09) on mira.cbaines.net X-Spam-Level: X-Spam-Status: No, score=-3.7 required=5.0 tests=BAYES_00,DKIM_ADSP_CUSTOM_MED, DKIM_INVALID,DKIM_SIGNED,FREEMAIL_FROM,MAILING_LIST_MULTI, RCVD_IN_MSPIKE_H2,SPF_HELO_PASS,URIBL_BLOCKED autolearn=unavailable autolearn_force=no version=3.4.6 Received: from lists.gnu.org (lists.gnu.org [209.51.188.17]) by mira.cbaines.net (Postfix) with ESMTPS id F07F627BBE9 for ; Sat, 26 Nov 2022 12:13:41 +0000 (GMT) Received: from localhost ([::1] helo=lists1p.gnu.org) by lists.gnu.org with esmtp (Exim 4.90_1) (envelope-from ) id 1oyu3S-0004r1-Pg; Sat, 26 Nov 2022 07:13:10 -0500 Received: from eggs.gnu.org ([2001:470:142:3::10]) by lists.gnu.org with esmtps (TLS1.2:ECDHE_RSA_AES_256_GCM_SHA384:256) (Exim 4.90_1) (envelope-from ) id 1oyu3M-0004po-R3 for guix-patches@gnu.org; Sat, 26 Nov 2022 07:13:04 -0500 Received: from debbugs.gnu.org ([209.51.188.43]) by eggs.gnu.org with esmtps (TLS1.2:ECDHE_RSA_AES_128_GCM_SHA256:128) (Exim 4.90_1) (envelope-from ) id 1oyu3M-0005Ls-JS for guix-patches@gnu.org; Sat, 26 Nov 2022 07:13:04 -0500 Received: from Debian-debbugs by debbugs.gnu.org with local (Exim 4.84_2) (envelope-from ) id 1oyu3M-00018w-F7 for guix-patches@gnu.org; Sat, 26 Nov 2022 07:13:04 -0500 X-Loop: help-debbugs@gnu.org Subject: [bug#59607] [PATCH 7/8] gnu: Add python-gfpgan. 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[85.127.52.93]) by smtp.gmail.com with ESMTPSA id az26-20020a05600c601a00b003d01b84e9b2sm8695059wmb.27.2022.11.26.04.11.56 for <59607@debbugs.gnu.org> (version=TLS1_3 cipher=TLS_AES_256_GCM_SHA384 bits=256/256); Sat, 26 Nov 2022 04:11:57 -0800 (PST) From: Liliana Marie Prikler Date: Sat, 26 Nov 2022 11:46:23 +0100 Message-ID: <9f038ba9a340cb10fea00673080c5d67d0a71cb9.camel@gmail.com> MIME-Version: 1.0 X-BeenThere: debbugs-submit@debbugs.gnu.org X-Mailman-Version: 2.1.18 Precedence: list X-BeenThere: guix-patches@gnu.org List-Id: List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , Errors-To: guix-patches-bounces+patchwork=mira.cbaines.net@gnu.org Sender: guix-patches-bounces+patchwork=mira.cbaines.net@gnu.org X-getmail-retrieved-from-mailbox: Patches * gnu/packages/patches/python-gfpgan-unfuse-leaky-relu.patch: New file. * gnu/local.mk (dist_patch_DATA): Register it here. * gnu/packages/machine-learning.scm (python-gfpgan): New variable. --- gnu/local.mk | 1 + gnu/packages/machine-learning.scm | 71 +++++++++++++++++++ .../python-gfpgan-unfuse-leaky-relu.patch | 57 +++++++++++++++ 3 files changed, 129 insertions(+) create mode 100644 gnu/packages/patches/python-gfpgan-unfuse-leaky-relu.patch diff --git a/gnu/local.mk b/gnu/local.mk index 8dd1abe07a..9312bec6d4 100644 --- a/gnu/local.mk +++ b/gnu/local.mk @@ -1729,6 +1729,7 @@ dist_patch_DATA = \ %D%/packages/patches/python-execnet-read-only-fix.patch \ %D%/packages/patches/python-fixtures-remove-monkeypatch-test.patch \ %D%/packages/patches/python-flask-restful-werkzeug-compat.patch \ + %D%/packages/patches/python-gfpgan-unfuse-leaky-relu.patch \ %D%/packages/patches/python-ipython-documentation-chars.patch \ %D%/packages/patches/python-ipython-documentation-repro.patch \ %D%/packages/patches/python-keras-integration-test.patch \ diff --git a/gnu/packages/machine-learning.scm b/gnu/packages/machine-learning.scm index 6116834578..73263962f5 100644 --- a/gnu/packages/machine-learning.scm +++ b/gnu/packages/machine-learning.scm @@ -859,6 +859,77 @@ (define-public python-facexlib license:asl2.0 license:expat)))) +(define-public python-gfpgan + (package + (name "python-gfpgan") + (version "1.3.8") + (source (origin + (method git-fetch) + (uri (git-reference + (url "https://github.com/TencentARC/GFPGAN") + (commit (string-append "v" version)))) + (file-name (git-file-name name version)) + (patches (search-patches + "python-gfpgan-unfuse-leaky-relu.patch")) + (modules '((guix build utils))) + (snippet + #~(begin (substitute* (find-files "." "\\.py$") + (("\\.cuda\\(\\)") "") + (("torch\\.cuda\\.is_available\\(\\)") "False") + (("'cuda' if False else ") "") + (("'cuda'") "'cpu'")))) + (sha256 + (base32 + "1s2wn3r4h1if35yvacaihk2r9fswdh16nxgh229g07p2562pgcky")))) + (build-system python-build-system) + (arguments + (list + #:phases + #~(modify-phases %standard-phases + (add-after 'unpack 'fix-api + (lambda _ + (substitute* (find-files "." "\\.py$") + (("from basicsr\\.losses\\.losses import .*") + "import basicsr.losses\n") + (("GANLoss") + "basicsr.losses.gan_loss.GANLoss") + (("(L1|Perceptual)Loss" loss) + (string-append "basicsr.losses.basic_loss." loss))))) + (add-after 'unpack 'fix-requirements + (lambda _ + (substitute* "requirements.txt" + (("opencv-python") "") ; installed without egg-info + (("tb-nightly") "")))) + (add-before 'check 'delete-failing-tests + (lambda _ + ;; XXX: Possibly genuine errors + (delete-file "tests/test_ffhq_degradation_dataset.py") + (delete-file "tests/test_gfpgan_model.py") + ;; Test requires pretrained model. + (delete-file "tests/test_utils.py"))) + (replace 'check + (lambda* (#:key tests? #:allow-other-keys) + (when tests? + (invoke "pytest" "-vv"))))))) + (propagated-inputs (list opencv + python-basicsr + python-facexlib + python-lmdb + python-numpy + python-pyyaml + python-scipy + python-pytorch + python-torchvision + python-tqdm + python-yapf)) + (native-inputs (list python-cython python-pytest)) + (home-page "https://github.com/TencentARC/GFPGAN") + (synopsis "Restore images of faces") + (description "GFPGAN uses generative adversarial networks to remove +degradations, such as blur, noise, or compression artifacts, from facial +images.") + (license license:asl2.0))) + (define-public ncnn (package (name "ncnn") diff --git a/gnu/packages/patches/python-gfpgan-unfuse-leaky-relu.patch b/gnu/packages/patches/python-gfpgan-unfuse-leaky-relu.patch new file mode 100644 index 0000000000..cd09312676 --- /dev/null +++ b/gnu/packages/patches/python-gfpgan-unfuse-leaky-relu.patch @@ -0,0 +1,57 @@ +diff --git a/gfpgan/archs/gfpganv1_arch.py b/gfpgan/archs/gfpganv1_arch.py +index eaf3162..34ae5a2 100644 +--- a/gfpgan/archs/gfpganv1_arch.py ++++ b/gfpgan/archs/gfpganv1_arch.py +@@ -3,7 +3,6 @@ import random + import torch + from basicsr.archs.stylegan2_arch import (ConvLayer, EqualConv2d, EqualLinear, ResBlock, ScaledLeakyReLU, + StyleGAN2Generator) +-from basicsr.ops.fused_act import FusedLeakyReLU + from basicsr.utils.registry import ARCH_REGISTRY + from torch import nn + from torch.nn import functional as F +@@ -170,10 +169,7 @@ class ConvUpLayer(nn.Module): + + # activation + if activate: +- if bias: +- self.activation = FusedLeakyReLU(out_channels) +- else: +- self.activation = ScaledLeakyReLU(0.2) ++ self.activation = ScaledLeakyReLU(0.2) + else: + self.activation = None + +diff --git a/gfpgan/archs/stylegan2_bilinear_arch.py b/gfpgan/archs/stylegan2_bilinear_arch.py +index 1342ee3..5cffb44 100644 +--- a/gfpgan/archs/stylegan2_bilinear_arch.py ++++ b/gfpgan/archs/stylegan2_bilinear_arch.py +@@ -1,7 +1,6 @@ + import math + import random + import torch +-from basicsr.ops.fused_act import FusedLeakyReLU, fused_leaky_relu + from basicsr.utils.registry import ARCH_REGISTRY + from torch import nn + from torch.nn import functional as F +@@ -190,7 +189,7 @@ class StyleConv(nn.Module): + sample_mode=sample_mode, + interpolation_mode=interpolation_mode) + self.weight = nn.Parameter(torch.zeros(1)) # for noise injection +- self.activate = FusedLeakyReLU(out_channels) ++ self.activate = ScaledLeakyReLU() + + def forward(self, x, style, noise=None): + # modulate +@@ -568,10 +567,7 @@ class ConvLayer(nn.Sequential): + and not activate)) + # activation + if activate: +- if bias: +- layers.append(FusedLeakyReLU(out_channels)) +- else: +- layers.append(ScaledLeakyReLU(0.2)) ++ layers.append(ScaledLeakyReLU(0.2)) + + super(ConvLayer, self).__init__(*layers) + From patchwork Tue Nov 22 20:55:02 2022 Content-Type: text/plain; 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[85.127.52.93]) by smtp.gmail.com with ESMTPSA id az26-20020a05600c601a00b003d01b84e9b2sm8695059wmb.27.2022.11.26.04.11.57 for <59607@debbugs.gnu.org> (version=TLS1_3 cipher=TLS_AES_256_GCM_SHA384 bits=256/256); Sat, 26 Nov 2022 04:11:57 -0800 (PST) From: Liliana Marie Prikler Date: Tue, 22 Nov 2022 21:55:02 +0100 Message-ID: <54e7f9a4a00e598b67876573dd91dd49095e966e.camel@gmail.com> MIME-Version: 1.0 X-BeenThere: debbugs-submit@debbugs.gnu.org X-Mailman-Version: 2.1.18 Precedence: list X-BeenThere: guix-patches@gnu.org List-Id: List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , Errors-To: guix-patches-bounces+patchwork=mira.cbaines.net@gnu.org Sender: guix-patches-bounces+patchwork=mira.cbaines.net@gnu.org X-getmail-retrieved-from-mailbox: Patches * gnu/packages/machine-learning.scm (python-real-esrgan): New variable. --- gnu/packages/machine-learning.scm | 62 +++++++++++++++++++++++++++++++ 1 file changed, 62 insertions(+) diff --git a/gnu/packages/machine-learning.scm b/gnu/packages/machine-learning.scm index 73263962f5..c49886f50d 100644 --- a/gnu/packages/machine-learning.scm +++ b/gnu/packages/machine-learning.scm @@ -930,6 +930,68 @@ (define-public python-gfpgan images.") (license license:asl2.0))) +(define-public python-real-esrgan + (package + (name "python-real-esrgan") + (version "0.3.0") + (source (origin + (method git-fetch) + (uri (git-reference + (url "https://github.com/xinntao/Real-ESRGAN") + (commit (string-append "v" version)))) + (file-name (git-file-name name version)) + (modules '((guix build utils))) + (snippet + #~(begin (substitute* (find-files "." "\\.py") + (("\\.cuda\\(\\)") "")))) + (sha256 + (base32 + "19qp8af1m50zawcnb31hq41nicad5k4hz7ffwizana17w069ilx5")))) + (build-system python-build-system) + (arguments + (list + #:phases + #~(modify-phases %standard-phases + (add-after 'unpack 'fix-api + (lambda _ + (substitute* (find-files "." "\\.py$") + (("from basicsr\\.losses\\.losses import .*") + "import basicsr.losses\n") + (("GANLoss") + "basicsr.losses.gan_loss.GANLoss") + (("(L1|Perceptual)Loss" loss) + (string-append "basicsr.losses.basic_loss." loss))))) + (add-after 'unpack 'fix-requirements + (lambda _ + (substitute* "requirements.txt" + (("opencv-python") "")))) ; installed without egg-info + (add-before 'check 'pre-check + (lambda _ + (setenv "HOME" (getcwd)) + ;; Test requires pretrained model. + (delete-file "tests/test_utils.py"))) + (replace 'check + (lambda* (#:key tests? #:allow-other-keys) + (when tests? + (invoke "pytest" "-vv"))))))) + (propagated-inputs (list opencv + python-basicsr + python-facexlib + python-gfpgan + python-numpy + python-pillow + python-pytorch + python-torchvision + python-tqdm)) + (native-inputs (list python-cython python-pytest)) + (home-page "https://github.com/xinntao/Real-ESRGAN") + (synopsis "Restore low-resolution images") + (description "Real-ESRGAN is a @acronym{GAN, Generative Adversarial Network} +aiming to restore low-resolution images. The techniques used are described in +the paper 'Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure +Synthetic Data' by Xintao Wang, Liangbin Xie, Chao Dong, and Ying Shan.") + (license license:bsd-3))) + (define-public ncnn (package (name "ncnn")