diff --git a/.gitattributes b/.gitattributes index c7d9f3332a950355d5a77d85000f05e6f45435ea..4dcd80c697955cdf5c9cf7f424606ebb0f63a307 100644 --- a/.gitattributes +++ b/.gitattributes @@ -32,3 +32,28 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +OpenFace/FaceLandmarkVidMulti filter=lfs diff=lfs merge=lfs -text +OpenFace/FeatureExtraction filter=lfs diff=lfs merge=lfs -text +OpenFace/FaceLandmarkVid filter=lfs diff=lfs merge=lfs -text +OpenFace/FaceLandmarkImg filter=lfs diff=lfs merge=lfs -text +OpenFace/model/detection_validation/validator_cnn.txt filter=lfs diff=lfs merge=lfs -text +OpenFace/model/detection_validation/validator_cnn_68.txt filter=lfs diff=lfs merge=lfs -text +OpenFace/model/model_inner/patch_experts/ccnf_patches_1.00_inner.txt filter=lfs diff=lfs merge=lfs -text +OpenFace/model/patch_experts/ccnf_patches_0.5_wild.txt filter=lfs diff=lfs merge=lfs -text +OpenFace/model/patch_experts/ccnf_patches_1_wild.txt filter=lfs diff=lfs merge=lfs -text +OpenFace/model/patch_experts/ccnf_patches_0.35_multi_pie.txt filter=lfs diff=lfs merge=lfs -text +OpenFace/model/patch_experts/ccnf_patches_0.35_wild.txt filter=lfs diff=lfs merge=lfs -text +OpenFace/model/patch_experts/ccnf_patches_0.25_wild.txt filter=lfs diff=lfs merge=lfs -text +OpenFace/model/patch_experts/ccnf_patches_0.5_multi_pie.txt filter=lfs diff=lfs merge=lfs -text +OpenFace/model/patch_experts/ccnf_patches_0.25_multi_pie.txt filter=lfs diff=lfs merge=lfs -text +OpenFace/model/patch_experts/ccnf_patches_0.5_general.txt filter=lfs diff=lfs merge=lfs -text +OpenFace/model/patch_experts/ccnf_patches_0.25_general.txt filter=lfs diff=lfs merge=lfs -text +OpenFace/model/patch_experts/ccnf_patches_0.35_general.txt filter=lfs diff=lfs merge=lfs -text +OpenFace/model/mtcnn_detector/ONet.dat filter=lfs diff=lfs merge=lfs -text +samples/audios/trump.wav filter=lfs diff=lfs merge=lfs -text +samples/audios/abstract.wav filter=lfs diff=lfs merge=lfs -text +samples/audios/obama2.wav filter=lfs diff=lfs merge=lfs -text +OpenFace/model/patch_experts/cen_patches_0.35_of.dat filter=lfs diff=lfs merge=lfs -text +OpenFace/model/patch_experts/cen_patches_0.25_of.dat filter=lfs diff=lfs merge=lfs -text +OpenFace/model/patch_experts/cen_patches_1.00_of.dat filter=lfs diff=lfs merge=lfs -text +OpenFace/model/patch_experts/cen_patches_0.50_of.dat filter=lfs diff=lfs merge=lfs -text diff --git a/.ipynb_checkpoints/config-checkpoint.py b/.ipynb_checkpoints/config-checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..11fa6424389fdd22d7a5e4a14844b58515905154 --- /dev/null +++ b/.ipynb_checkpoints/config-checkpoint.py @@ -0,0 +1,3 @@ +OPENFACE_POSE_EXTRACTOR_PATH = "/content/one-shot-talking-face/OpenFace/FeatureExtraction" +GENERATOR_CKPT = "/content/one-shot-talking-face/checkpoints/generator.ckpt" +AUDIO2POSE_CKPT = "/content/one-shot-talking-face/checkpoints/audio2pose.ckpt" diff --git a/.ipynb_checkpoints/requirements-checkpoint.txt b/.ipynb_checkpoints/requirements-checkpoint.txt new file mode 100644 index 0000000000000000000000000000000000000000..e22899b2c29fa4289ac2a2750e1f97dae1b5199a --- /dev/null +++ b/.ipynb_checkpoints/requirements-checkpoint.txt @@ -0,0 +1,8 @@ +scikit-image +python_speech_features +pyworld +pyyaml +imageio +scipy +pyworld +opencv-python \ No newline at end of file diff --git a/OpenFace/AU_predictors/AU_DISFA_best.txt b/OpenFace/AU_predictors/AU_DISFA_best.txt new file mode 100644 index 0000000000000000000000000000000000000000..cadfed6f7429ba8641cc44debc083582924c0757 --- /dev/null +++ b/OpenFace/AU_predictors/AU_DISFA_best.txt @@ -0,0 +1,24 @@ +svm_disfa/AU_1_dyn.dat AU01 +svm_disfa/AU_2_dyn.dat AU02 +svm_disfa/AU_4_stat.dat AU04 +svm_disfa/AU_5_dyn.dat AU05 +svm_disfa/AU_6_stat.dat AU06 +svm_disfa/AU_9_dyn.dat AU09 +svm_disfa/AU_12_dyn.dat AU12 +svm_disfa/AU_15_dyn.dat AU15 +svm_disfa/AU_17_dyn.dat AU17 +svm_disfa/AU_20_dyn.dat AU20 +svm_disfa/AU_25_dyn.dat AU25 +svm_disfa/AU_26_dyn.dat AU26 +svr_disfa/AU_1_dynamic_intensity.dat AU01 +svr_disfa/AU_2_dynamic_intensity.dat AU02 +svr_disfa/AU_4_dynamic_intensity.dat AU04 +svr_disfa/AU_5_dynamic_intensity.dat AU05 +svr_disfa/AU_6_dynamic_intensity.dat AU06 +svr_disfa/AU_9_dynamic_intensity.dat AU09 +svr_disfa/AU_12_dynamic_intensity.dat AU12 +svr_disfa/AU_15_dynamic_intensity.dat AU15 +svr_disfa/AU_17_dynamic_intensity.dat AU17 +svr_disfa/AU_20_dynamic_intensity.dat AU20 +svr_disfa/AU_25_dynamic_intensity.dat AU25 +svr_disfa/AU_26_dynamic_intensity.dat AU26 diff --git a/OpenFace/AU_predictors/AU_DISFA_static.txt b/OpenFace/AU_predictors/AU_DISFA_static.txt new file mode 100644 index 0000000000000000000000000000000000000000..1479c3e1c7bfe055f76173fe9dadc32a67427d17 --- /dev/null +++ b/OpenFace/AU_predictors/AU_DISFA_static.txt @@ -0,0 +1,24 @@ +svm_disfa/AU_1_stat.dat AU01 +svm_disfa/AU_2_stat.dat AU02 +svm_disfa/AU_4_stat.dat AU04 +svm_disfa/AU_5_stat.dat AU05 +svm_disfa/AU_6_stat.dat AU06 +svm_disfa/AU_9_stat.dat AU09 +svm_disfa/AU_12_stat.dat AU12 +svm_disfa/AU_15_stat.dat AU15 +svm_disfa/AU_17_stat.dat AU17 +svm_disfa/AU_20_stat.dat AU20 +svm_disfa/AU_25_stat.dat AU25 +svm_disfa/AU_26_stat.dat AU26 +svr_disfa/AU_1_static_intensity.dat AU01 +svr_disfa/AU_2_static_intensity.dat AU02 +svr_disfa/AU_4_static_intensity.dat AU04 +svr_disfa/AU_5_static_intensity.dat AU05 +svr_disfa/AU_6_static_intensity.dat AU06 +svr_disfa/AU_9_static_intensity.dat AU09 +svr_disfa/AU_12_static_intensity.dat AU12 +svr_disfa/AU_15_static_intensity.dat AU15 +svr_disfa/AU_17_static_intensity.dat AU17 +svr_disfa/AU_20_static_intensity.dat AU20 +svr_disfa/AU_25_static_intensity.dat AU25 +svr_disfa/AU_26_static_intensity.dat AU26 diff --git a/OpenFace/AU_predictors/AU_all_best.txt b/OpenFace/AU_predictors/AU_all_best.txt new file mode 100644 index 0000000000000000000000000000000000000000..e35569888e3c9f602e6057ae2bbea59468e38061 --- /dev/null +++ b/OpenFace/AU_predictors/AU_all_best.txt @@ -0,0 +1,35 @@ +svm_combined/AU_1_dynamic.dat AU01 +svm_combined/AU_2_dynamic.dat AU02 +svm_combined/AU_4_static.dat AU04 +svm_combined/AU_5_static.dat AU05 +svm_combined/AU_6_static.dat AU06 +svm_combined/AU_7_static.dat AU07 +svm_combined/AU_9_dynamic.dat AU09 +svm_combined/AU_10_static.dat AU10 +svm_combined/AU_12_static.dat AU12 +svm_combined/AU_14_static.dat AU14 +svm_combined/AU_15_dynamic.dat AU15 +svm_combined/AU_17_dynamic.dat AU17 +svm_combined/AU_20_dynamic.dat AU20 +svm_combined/AU_23_static.dat AU23 +svm_combined/AU_25_dynamic.dat AU25 +svm_combined/AU_26_dynamic.dat AU26 +svm_combined/AU_28_dynamic.dat AU28 +svm_combined/AU_45_dynamic.dat AU45 +svr_combined/AU_1_dynamic_intensity_comb.dat AU01 +svr_combined/AU_2_dynamic_intensity_comb.dat AU02 +svr_combined/AU_4_static_intensity_comb.dat AU04 +svr_combined/AU_5_dynamic_intensity.dat AU05 +svr_combined/AU_6_static_intensity_comb.dat AU06 +svr_combined/AU_7_static_intensity_comb.dat AU07 +svr_combined/AU_9_dynamic_intensity.dat AU09 +svr_combined/AU_10_static_intensity_comb.dat AU10 +svr_combined/AU_12_static_intensity_comb.dat AU12 +svr_combined/AU_14_static_intensity.dat AU14 +svr_combined/AU_15_dynamic_intensity_comb.dat AU15 +svr_combined/AU_17_dynamic_intensity_comb.dat AU17 +svr_combined/AU_20_dynamic_intensity.dat AU20 +svr_combined/AU_23_dynamic_intensity_comb.dat AU23 +svr_combined/AU_25_dynamic_intensity_comb.dat AU25 +svr_combined/AU_26_dynamic_intensity_comb.dat AU26 +svr_combined/AU_45_dynamic_intensity_comb.dat AU45 \ No newline at end of file diff --git a/OpenFace/AU_predictors/AU_all_static.txt b/OpenFace/AU_predictors/AU_all_static.txt new file mode 100644 index 0000000000000000000000000000000000000000..4d9504aa552a5fd6bb89470f2f93ccd03696b87e --- /dev/null +++ b/OpenFace/AU_predictors/AU_all_static.txt @@ -0,0 +1,35 @@ +svm_combined/AU_1_static.dat AU01 +svm_combined/AU_2_static.dat AU02 +svm_combined/AU_4_static.dat AU04 +svm_combined/AU_5_static.dat AU05 +svm_combined/AU_6_static.dat AU06 +svm_combined/AU_7_static.dat AU07 +svm_combined/AU_9_static.dat AU09 +svm_combined/AU_10_static.dat AU10 +svm_combined/AU_12_static.dat AU12 +svm_combined/AU_14_static.dat AU14 +svm_combined/AU_15_static.dat AU15 +svm_combined/AU_17_static.dat AU17 +svm_combined/AU_20_static.dat AU20 +svm_combined/AU_23_static.dat AU23 +svm_combined/AU_25_static.dat AU25 +svm_combined/AU_26_static.dat AU26 +svm_combined/AU_28_static.dat AU28 +svm_combined/AU_45_static.dat AU45 +svr_combined/AU_1_static_intensity_comb.dat AU01 +svr_combined/AU_2_static_intensity_comb.dat AU02 +svr_combined/AU_4_static_intensity_comb.dat AU04 +svr_combined/AU_5_static_intensity.dat AU05 +svr_combined/AU_6_static_intensity_comb.dat AU06 +svr_combined/AU_7_static_intensity_comb.dat AU07 +svr_combined/AU_9_static_intensity.dat AU09 +svr_combined/AU_10_static_intensity_comb.dat AU10 +svr_combined/AU_12_static_intensity_comb.dat AU12 +svr_combined/AU_14_static_intensity.dat AU14 +svr_combined/AU_15_static_intensity_comb.dat AU15 +svr_combined/AU_17_static_intensity_comb.dat AU17 +svr_combined/AU_20_static_intensity.dat AU20 +svr_combined/AU_23_static_intensity_comb.dat AU23 +svr_combined/AU_25_static_intensity.dat AU25 +svr_combined/AU_26_static_intensity_comb.dat AU26 +svr_combined/AU_45_static_intensity_comb.dat AU45 \ No newline at end of file diff --git a/OpenFace/AU_predictors/DISFA_dynamic_svms.txt b/OpenFace/AU_predictors/DISFA_dynamic_svms.txt new file mode 100644 index 0000000000000000000000000000000000000000..716ea4b44d86602991f0946def3ddab6e3339043 --- /dev/null +++ b/OpenFace/AU_predictors/DISFA_dynamic_svms.txt @@ -0,0 +1,3 @@ +AUPredictor AU_DISFA_best.txt +PDM In-the-wild_aligned_PDM_68.txt +Triangulation tris_68_full.txt \ No newline at end of file diff --git a/OpenFace/AU_predictors/DISFA_static_svms.txt b/OpenFace/AU_predictors/DISFA_static_svms.txt new file mode 100644 index 0000000000000000000000000000000000000000..5e438967485f74a2792f2a90c0e1b593c7d89b65 --- /dev/null +++ b/OpenFace/AU_predictors/DISFA_static_svms.txt @@ -0,0 +1,3 @@ +AUPredictor AU_DISFA_static.txt +PDM In-the-wild_aligned_PDM_68.txt +Triangulation tris_68_full.txt \ No newline at end of file diff --git a/OpenFace/AU_predictors/In-the-wild_aligned_PDM_68.txt b/OpenFace/AU_predictors/In-the-wild_aligned_PDM_68.txt new file mode 100644 index 0000000000000000000000000000000000000000..d538397cde6a6ee0dc172787b37a377f002f3630 --- /dev/null +++ b/OpenFace/AU_predictors/In-the-wild_aligned_PDM_68.txt @@ -0,0 +1,421 @@ +# The mean values of the components (in mm) +204 +1 +6 +-73.393523 +-72.775014 +-70.533638 +-66.850058 +-59.790187 +-48.368973 +-34.121101 +-17.875411 +0.098749 +17.477031 +32.648966 +46.372358 +57.343480 +64.388482 +68.212038 +70.486405 +71.375822 +-61.119406 +-51.287588 +-37.804800 +-24.022754 +-11.635713 +12.056636 +25.106256 +38.338588 +51.191007 +60.053851 +0.653940 +0.804809 +0.992204 +1.226783 +-14.772472 +-7.180239 +0.555920 +8.272499 +15.214351 +-46.047290 +-37.674688 +-27.883856 +-19.648268 +-28.272965 +-38.082418 +19.265868 +27.894191 +37.437529 +45.170805 +38.196454 +28.764989 +-28.916267 +-17.533194 +-6.684590 +0.381001 +8.375443 +18.876618 +28.794412 +19.057574 +8.956375 +0.381549 +-7.428895 +-18.160634 +-24.377490 +-6.897633 +0.340663 +8.444722 +24.474473 +8.449166 +0.205322 +-7.198266 +-29.801432 +-10.949766 +7.929818 +26.074280 +42.564390 +56.481080 +67.246992 +75.056892 +77.061286 +74.758448 +66.929021 +56.311389 +42.419126 +25.455880 +6.990805 +-11.666193 +-30.365191 +-49.361602 +-58.769795 +-61.996155 +-61.033399 +-56.686759 +-57.391033 +-61.902186 +-62.777713 +-59.302347 +-50.190255 +-42.193790 +-30.993721 +-19.944596 +-8.414541 +2.598255 +4.751589 +6.562900 +4.661005 +2.643046 +-37.471411 +-42.730510 +-42.711517 +-36.754742 +-35.134493 +-34.919043 +-37.032306 +-43.342445 +-43.110822 +-38.086515 +-35.532024 +-35.484289 +28.612716 +22.172187 +19.029051 +20.721118 +19.035460 +22.394109 +28.079924 +36.298248 +39.634575 +40.395647 +39.836405 +36.677899 +28.677771 +25.475976 +26.014269 +25.326198 +28.323008 +30.596216 +31.408738 +30.844876 +47.667532 +45.909403 +44.842580 +43.141114 +38.635298 +30.750622 +18.456453 +3.609035 +-0.881698 +5.181201 +19.176563 +30.770570 +37.628629 +40.886309 +42.281449 +44.142567 +47.140426 +14.254422 +7.268147 +0.442051 +-6.606501 +-11.967398 +-12.051204 +-7.315098 +-1.022953 +5.349435 +11.615746 +-13.380835 +-21.150853 +-29.284036 +-36.948060 +-20.132003 +-23.536684 +-25.944448 +-23.695741 +-20.858157 +7.037989 +3.021217 +1.353629 +-0.111088 +-0.147273 +1.476612 +-0.665746 +0.247660 +1.696435 +4.894163 +0.282961 +-1.172675 +-2.240310 +-15.934335 +-22.611355 +-23.748437 +-22.721995 +-15.610679 +-3.217393 +-14.987997 +-22.554245 +-23.591626 +-22.406106 +-15.121907 +-4.785684 +-20.893742 +-22.220479 +-21.025520 +-5.712776 +-20.671489 +-21.903670 +-20.328022 +# The principal components (eigenvectors) of identity or combined identity and expression model +204 +34 +6 +-0.007395 -0.093690 0.039362 0.204443 -0.104698 0.033568 -0.090173 0.010170 -0.042341 0.104375 0.032695 0.038750 0.064385 -0.037025 0.058377 0.032457 -0.100005 -0.082042 -0.053440 0.008782 -0.027164 -0.000368 -0.129614 0.035436 -0.062685 -0.075349 0.140764 -0.032290 -0.115829 -0.037865 0.068590 0.008886 -0.066442 0.259211 +-0.002553 -0.075344 0.037127 0.201967 -0.069124 0.041645 -0.100939 0.018352 0.006598 0.081569 0.004894 0.038886 0.050631 0.056656 0.055353 -0.028457 -0.022518 -0.089693 -0.079196 0.010851 -0.017583 0.015095 -0.077834 -0.000944 -0.104255 -0.060338 0.106169 -0.065799 -0.067068 -0.051588 0.080490 0.063152 -0.047531 0.178216 +-0.002097 -0.055385 0.025705 0.215785 -0.026274 0.053548 -0.086364 0.041926 0.035784 0.033485 -0.014602 0.049999 0.031950 0.125201 0.045372 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-0.050390 0.063237 -0.133203 -0.070794 0.005133 0.178032 -0.002768 0.153446 0.105966 0.061693 0.011010 -0.027442 0.001944 -0.103967 0.045708 -0.083197 -0.172889 +0.016954 0.088125 0.034199 0.081242 0.170872 0.048964 0.063434 0.083842 -0.058188 -0.164769 -0.012135 0.002128 0.084787 -0.009691 -0.136231 0.068411 -0.091616 0.042046 0.053339 -0.068859 -0.141834 -0.040710 0.017020 0.034007 0.053187 0.096749 0.068416 0.075079 0.038728 0.063588 -0.109581 0.007737 0.033105 -0.152075 +0.021733 0.081042 0.015310 0.029492 0.112212 0.012959 0.065003 0.095565 -0.047228 -0.120559 -0.022904 -0.051461 0.036411 -0.036990 -0.101765 0.150918 -0.102123 0.163128 0.047199 0.037063 -0.223718 -0.065303 -0.149191 -0.017823 -0.134138 0.084419 0.010718 0.087970 0.043030 0.040099 -0.068487 -0.008773 0.077035 -0.082281 +0.024713 0.084952 0.002319 -0.002792 0.028194 -0.016856 0.036083 0.087381 -0.020743 -0.024386 -0.027268 -0.091654 -0.009025 -0.052124 -0.033407 0.168353 -0.061447 0.244847 0.024587 0.064221 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b/OpenFace/model/detection_validation/validator_cnn_68.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e422e6bf1659dfd83231e988328635e62eb0c4f9ae6b3979052ebc7ee4b287a +size 2328608 diff --git a/OpenFace/model/detection_validation/validator_general_68.txt b/OpenFace/model/detection_validation/validator_general_68.txt new file mode 100644 index 0000000000000000000000000000000000000000..fbbde4777318384411d12c2f479da3b4a9a8cf44 Binary files /dev/null and b/OpenFace/model/detection_validation/validator_general_68.txt differ diff --git a/OpenFace/model/early_term_cen_of.txt b/OpenFace/model/early_term_cen_of.txt new file mode 100644 index 0000000000000000000000000000000000000000..810a21db465a44bdce0f230fa658029b9587f442 --- /dev/null +++ b/OpenFace/model/early_term_cen_of.txt @@ -0,0 +1 @@ +1.000 1.005 1.054 0.976 0.976 1.054 1.005 0.000 0.049 0.121 -0.370 -0.370 0.121 0.049 -0.230 -0.200 -0.140 0.260 0.260 -0.140 -0.200 \ No newline at end of file diff --git a/OpenFace/model/haarAlign.txt b/OpenFace/model/haarAlign.txt new file mode 100644 index 0000000000000000000000000000000000000000..ae96eb317f664f21039edcd38939f6077e75db91 --- /dev/null +++ b/OpenFace/model/haarAlign.txt @@ -0,0 +1,6 @@ +3 +4 +6 +1.000000 0.000000 0.000000 0.000000 +0.000000 1.000000 0.000000 0.000000 +0.055858 0.229781 0.871248 0.892778 diff --git a/OpenFace/model/main_ceclm_general.txt b/OpenFace/model/main_ceclm_general.txt new file mode 100644 index 0000000000000000000000000000000000000000..2319f5e52b21ff7e8ed08852df7051c16bfa7eb3 --- /dev/null +++ b/OpenFace/model/main_ceclm_general.txt @@ -0,0 +1,5 @@ +LandmarkDetector cen_general.txt +LandmarkDetector_part model_eye/main_clnf_synth_left.txt left_eye_28 36 8 37 10 38 12 39 14 40 16 41 18 +LandmarkDetector_part model_eye/main_clnf_synth_right.txt right_eye_28 42 8 43 10 44 12 45 14 46 16 47 18 +FaceDetConversion haarAlign.txt +DetectionValidator detection_validation/validator_cnn_68.txt \ No newline at end of file diff --git a/OpenFace/model/main_clm_general.txt b/OpenFace/model/main_clm_general.txt new file mode 100644 index 0000000000000000000000000000000000000000..cbe921bb8cec49d5a4fcecee3e03abcf007ec4c7 --- /dev/null +++ b/OpenFace/model/main_clm_general.txt @@ -0,0 +1,5 @@ +LandmarkDetector clm_general.txt +LandmarkDetector_part model_eye/main_clnf_synth_left.txt left_eye_28 36 8 37 10 38 12 39 14 40 16 41 18 +LandmarkDetector_part model_eye/main_clnf_synth_right.txt right_eye_28 42 8 43 10 44 12 45 14 46 16 47 18 +FaceDetConversion haarAlign.txt +DetectionValidator detection_validation/validator_cnn.txt \ No newline at end of file diff --git a/OpenFace/model/main_clm_wild.txt b/OpenFace/model/main_clm_wild.txt new file mode 100644 index 0000000000000000000000000000000000000000..b706039fdfeb74b3dd4a8fef598266113f5a3a0c --- /dev/null +++ b/OpenFace/model/main_clm_wild.txt @@ -0,0 +1,5 @@ +LandmarkDetector clm_wild.txt +LandmarkDetector_part model_eye/main_clnf_synth_left.txt left_eye_28 36 8 37 10 38 12 39 14 40 16 41 18 +LandmarkDetector_part model_eye/main_clnf_synth_right.txt right_eye_28 42 8 43 10 44 12 45 14 46 16 47 18 +FaceDetConversion haarAlign.txt +DetectionValidator detection_validation/validator_cnn.txt \ No newline at end of file diff --git a/OpenFace/model/main_clnf_demos.txt b/OpenFace/model/main_clnf_demos.txt new file mode 100644 index 0000000000000000000000000000000000000000..87a5f7bdc0b3b3937b061029a9148fb298644fe8 --- /dev/null +++ b/OpenFace/model/main_clnf_demos.txt @@ -0,0 +1,5 @@ +LandmarkDetector AU_all_best.txt +LandmarkDetector_part model_eye/main_clnf_synth_left.txt left_eye_28 36 8 37 10 38 12 39 14 40 16 41 18 +LandmarkDetector_part model_eye/main_clnf_synth_right.txt right_eye_28 42 8 43 10 44 12 45 14 46 16 47 18 +DetectionValidator detection_validation/validator_general_68.txt +DetectorConstaints placeholder 700 0.05 0.05 0.9 0.9 \ No newline at end of file diff --git a/OpenFace/model/main_clnf_general.txt b/OpenFace/model/main_clnf_general.txt new file mode 100644 index 0000000000000000000000000000000000000000..0e5192b17b594f1b1130875494b36e31309f281c --- /dev/null +++ b/OpenFace/model/main_clnf_general.txt @@ -0,0 +1,6 @@ +LandmarkDetector clnf_general.txt +LandmarkDetector_part model_inner/main_clnf_inner.txt inner 17 0 18 1 19 2 20 3 21 4 22 5 23 6 24 7 25 8 26 9 27 10 28 11 29 12 30 13 31 14 32 15 33 16 34 17 35 18 36 19 37 20 38 21 39 22 40 23 41 24 42 25 43 26 44 27 45 28 46 29 47 30 48 31 49 32 50 33 51 34 52 35 53 36 54 37 55 38 56 39 57 40 58 41 59 42 60 43 61 44 62 45 63 46 64 47 65 48 66 49 67 50 +LandmarkDetector_part model_eye/main_clnf_synth_left.txt left_eye_28 36 8 37 10 38 12 39 14 40 16 41 18 +LandmarkDetector_part model_eye/main_clnf_synth_right.txt right_eye_28 42 8 43 10 44 12 45 14 46 16 47 18 +FaceDetConversion haarAlign.txt +DetectionValidator detection_validation/validator_cnn.txt \ No newline at end of file diff --git a/OpenFace/model/main_clnf_multi_pie.txt b/OpenFace/model/main_clnf_multi_pie.txt new file mode 100644 index 0000000000000000000000000000000000000000..3447f3865c9b597f667e3c837894f85902276bf5 --- /dev/null +++ b/OpenFace/model/main_clnf_multi_pie.txt @@ -0,0 +1,5 @@ +LandmarkDetector clnf_multi_pie.txt +LandmarkDetector_part model_eye/main_clnf_synth_left.txt left_eye_28 36 8 37 10 38 12 39 14 40 16 41 18 +LandmarkDetector_part model_eye/main_clnf_synth_right.txt right_eye_28 42 8 43 10 44 12 45 14 46 16 47 18 +FaceDetConversion haarAlign.txt +DetectionValidator detection_validation/validator_cnn.txt \ No newline at end of file diff --git a/OpenFace/model/main_clnf_wild.txt b/OpenFace/model/main_clnf_wild.txt new file mode 100644 index 0000000000000000000000000000000000000000..f0172622b193ceb6f8b28a83d74afb1c9e84d59f --- /dev/null +++ b/OpenFace/model/main_clnf_wild.txt @@ -0,0 +1,5 @@ +LandmarkDetector clnf_wild.txt +LandmarkDetector_part model_eye/main_clnf_synth_left.txt left_eye_28 36 8 37 10 38 12 39 14 40 16 41 18 +LandmarkDetector_part model_eye/main_clnf_synth_right.txt right_eye_28 42 8 43 10 44 12 45 14 46 16 47 18 +FaceDetConversion haarAlign.txt +DetectionValidator detection_validation/validator_cnn.txt \ No newline at end of file diff --git a/OpenFace/model/model_eye/clnf_left_synth.txt b/OpenFace/model/model_eye/clnf_left_synth.txt new file mode 100644 index 0000000000000000000000000000000000000000..ebeb9bc15e79215d9c57cc30ad2431e325a92c52 --- /dev/null +++ b/OpenFace/model/model_eye/clnf_left_synth.txt @@ -0,0 +1,3 @@ +PDM pdms/pdm_28_l_eye_3D_closed.txt +PatchesCCNF patch_experts/left_ccnf_patches_1.00_synth_lid_.txt +PatchesCCNF patch_experts/left_ccnf_patches_1.50_synth_lid_.txt diff --git a/OpenFace/model/model_eye/clnf_right_synth.txt b/OpenFace/model/model_eye/clnf_right_synth.txt new file mode 100644 index 0000000000000000000000000000000000000000..74205159d9734879b753ccdbc4ceed244a3632fd --- /dev/null +++ b/OpenFace/model/model_eye/clnf_right_synth.txt @@ -0,0 +1,3 @@ +PDM pdms/pdm_28_eye_3D_closed.txt +PatchesCCNF patch_experts/ccnf_patches_1.00_synth_lid_.txt +PatchesCCNF patch_experts/ccnf_patches_1.50_synth_lid_.txt \ No newline at end of file diff --git a/OpenFace/model/model_eye/main_clnf_synth_left.txt b/OpenFace/model/model_eye/main_clnf_synth_left.txt new file mode 100644 index 0000000000000000000000000000000000000000..a20b9ed6286dcd665a047550a808db53e650b015 --- /dev/null +++ b/OpenFace/model/model_eye/main_clnf_synth_left.txt @@ -0,0 +1 @@ +LandmarkDetector clnf_left_synth.txt \ No newline at end of file diff --git a/OpenFace/model/model_eye/main_clnf_synth_right.txt b/OpenFace/model/model_eye/main_clnf_synth_right.txt new file mode 100644 index 0000000000000000000000000000000000000000..b45e0e34770d2426ee3814c64288d5266c8a2009 --- /dev/null +++ b/OpenFace/model/model_eye/main_clnf_synth_right.txt @@ -0,0 +1 @@ +LandmarkDetector clnf_right_synth.txt \ No newline at end of file diff --git a/OpenFace/model/model_eye/patch_experts/ccnf_patches_1.00_synth_lid_.txt b/OpenFace/model/model_eye/patch_experts/ccnf_patches_1.00_synth_lid_.txt new file mode 100644 index 0000000000000000000000000000000000000000..75e401f470fc3db378b9e8467d71d3b5e8e8cf73 Binary files /dev/null and b/OpenFace/model/model_eye/patch_experts/ccnf_patches_1.00_synth_lid_.txt differ diff --git a/OpenFace/model/model_eye/patch_experts/ccnf_patches_1.50_synth_lid_.txt b/OpenFace/model/model_eye/patch_experts/ccnf_patches_1.50_synth_lid_.txt new file mode 100644 index 0000000000000000000000000000000000000000..570e5352d3125118c92dec241ffd4e12f1da072a Binary files /dev/null and b/OpenFace/model/model_eye/patch_experts/ccnf_patches_1.50_synth_lid_.txt differ diff --git a/OpenFace/model/model_eye/patch_experts/left_ccnf_patches_1.00_synth_lid_.txt b/OpenFace/model/model_eye/patch_experts/left_ccnf_patches_1.00_synth_lid_.txt new file mode 100644 index 0000000000000000000000000000000000000000..a853d05a90fab96a83a49ce62ef5cae51ab374bf Binary files /dev/null and b/OpenFace/model/model_eye/patch_experts/left_ccnf_patches_1.00_synth_lid_.txt differ diff --git a/OpenFace/model/model_eye/patch_experts/left_ccnf_patches_1.50_synth_lid_.txt b/OpenFace/model/model_eye/patch_experts/left_ccnf_patches_1.50_synth_lid_.txt new file mode 100644 index 0000000000000000000000000000000000000000..0d57e2dd299571f62229da975ac34d03ccfb99d3 Binary files /dev/null and b/OpenFace/model/model_eye/patch_experts/left_ccnf_patches_1.50_synth_lid_.txt differ diff --git a/OpenFace/model/model_eye/pdms/pdm_28_eye_3D_closed.txt b/OpenFace/model/model_eye/pdms/pdm_28_eye_3D_closed.txt new file mode 100644 index 0000000000000000000000000000000000000000..a5c91d4e46b1f915df036115e5ad406e949663a9 --- /dev/null +++ b/OpenFace/model/model_eye/pdms/pdm_28_eye_3D_closed.txt @@ -0,0 +1,181 @@ +# The mean values of the components (in mm) +84 +1 +6 +-6.346689 +-4.802645 +-0.689206 +3.584030 +5.513861 +3.969817 +-0.143620 +-4.705906 +-10.151100 +-7.784450 +-4.315176 +0.286655 +4.895108 +8.614140 +11.209605 +8.705321 +5.165718 +1.065652 +-3.271212 +-7.250731 +-2.195097 +-0.324358 +1.476467 +2.152288 +1.307482 +-0.562884 +-2.363439 +-3.039631 +-0.630295 +-4.947588 +-6.865877 +-5.261457 +-1.074175 +3.243115 +5.161403 +3.911768 +2.351440 +-0.095726 +-1.959374 +-2.862675 +-2.316547 +-1.049970 +0.957222 +2.555497 +3.410716 +3.797180 +3.755154 +3.510297 +1.231211 +1.933679 +1.093865 +-0.796012 +-2.628691 +-3.330879 +-2.491440 +-0.601842 +-0.509775 +0.210152 +0.641715 +0.532111 +-0.054457 +-0.774384 +-1.205947 +-0.879208 +1.060595 +-0.440952 +-1.721653 +-2.100358 +-0.796095 +1.616596 +4.283196 +1.517115 +-0.872711 +-2.176368 +-2.010491 +-0.559696 +0.173887 +0.125369 +0.313958 +0.629133 +0.886248 +0.934736 +0.746213 +0.431068 +# The principal components (eigenvectors) of identity or combined identity and expression model +84 +10 +6 +-0.153386 0.025177 -0.054841 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0.094112 0.093425 0.004704 0.060292 -0.113458 -0.212440 +0.099074 -0.043950 -0.000509 -0.171174 0.086675 0.069487 0.039093 0.061946 -0.059545 -0.184463 +-0.054913 -0.004523 0.015086 0.231138 -0.115127 0.082549 0.124313 -0.000052 0.180789 0.076509 +-0.038477 -0.010328 -0.025919 0.225952 -0.098722 -0.012521 -0.024397 -0.078886 -0.051188 -0.094188 +-0.015599 -0.034327 -0.050684 0.208434 -0.145282 -0.134450 -0.225097 -0.057805 -0.253738 -0.159062 +0.013759 -0.046657 -0.031453 0.191733 -0.202303 -0.154083 -0.244663 -0.006407 -0.264460 -0.184882 +0.039393 -0.033167 -0.026588 0.194395 -0.163302 -0.053331 -0.095733 0.027393 -0.169484 -0.254320 +0.055992 -0.018520 -0.024499 0.236434 -0.057326 -0.072106 -0.011848 0.151259 -0.049264 -0.117501 +0.068631 -0.013724 0.027292 0.231437 -0.037554 0.034751 0.337456 0.134888 0.312055 -0.132196 +0.056256 0.022936 -0.019524 0.146331 0.032207 -0.007908 -0.035410 -0.090636 -0.012725 0.205397 +0.042182 0.043848 -0.041760 0.116811 0.078314 0.051616 -0.106453 -0.228687 0.036561 0.260986 +0.019928 0.055883 -0.059556 0.147688 0.109694 0.020391 -0.066574 -0.102694 0.128555 0.213173 +-0.009409 0.051946 -0.050194 0.162935 0.085636 -0.028224 -0.126599 0.046702 0.099383 0.240695 +-0.036452 0.030786 -0.028097 0.176770 -0.015768 0.029793 -0.046117 0.020525 0.047330 0.165226 +0.034441 -0.022881 0.015629 -0.156918 0.055237 0.036211 0.045403 0.038219 -0.063438 -0.053697 +-0.007396 -0.031398 0.017754 -0.172866 0.059650 0.047306 0.028721 0.038858 -0.090778 -0.069213 +-0.050587 -0.024716 0.023247 -0.172154 0.048419 0.037544 0.009258 0.022863 -0.065299 -0.050654 +-0.069827 -0.006750 0.028891 -0.155196 0.028124 0.012648 -0.001577 -0.000397 -0.001922 -0.008919 +-0.053849 0.011973 0.031379 -0.131930 0.010655 -0.012793 0.002562 -0.017294 0.062223 0.031533 +-0.012020 0.020487 0.029254 -0.115987 0.006243 -0.023880 0.019242 -0.017932 0.089559 0.047033 +0.031165 0.013807 0.023762 -0.116702 0.017473 -0.014122 0.038694 -0.001937 0.064074 0.028511 +0.050412 -0.004156 0.018118 -0.133655 0.037766 0.010766 0.049531 0.021320 0.000702 -0.013208 +# The variances of the components (eigenvalues) of identity or combined identity and expression model +1 +10 +6 +111.570907 32.910130 22.463857 3.183176 1.531432 1.381999 0.770569 0.635129 0.500532 0.422800 diff --git a/OpenFace/model/model_eye/pdms/pdm_28_l_eye_3D_closed.txt b/OpenFace/model/model_eye/pdms/pdm_28_l_eye_3D_closed.txt new file mode 100644 index 0000000000000000000000000000000000000000..86525b737885ecfc908a8f63b4933b01e302838a --- /dev/null +++ b/OpenFace/model/model_eye/pdms/pdm_28_l_eye_3D_closed.txt @@ -0,0 +1,181 @@ +# The mean values of the components (in mm) +84 +1 +6 +-5.513861 +-3.584030 +0.689206 +4.802645 +6.346689 +4.705906 +0.143620 +-3.969817 +-11.209605 +-8.614140 +-4.895108 +-0.286655 +4.315176 +7.784450 +10.151100 +7.250731 +3.271212 +-1.065652 +-5.165718 +-8.705321 +-1.476467 +0.324358 +2.195097 +3.039631 +2.363439 +0.562884 +-1.307482 +-2.152288 +-1.074175 +-5.261457 +-6.865877 +-4.947588 +-0.630295 +3.911768 +5.161403 +3.243115 +0.957222 +-1.049970 +-2.316547 +-2.862675 +-1.959374 +-0.095726 +2.351440 +3.510297 +3.755154 +3.797180 +3.410716 +2.555497 +1.093865 +1.933679 +1.231211 +-0.601842 +-2.491440 +-3.330879 +-2.628691 +-0.796012 +-0.054457 +0.532111 +0.641715 +0.210152 +-0.509775 +-0.879208 +-1.205947 +-0.774384 +4.283196 +1.616596 +-0.796095 +-2.100358 +-1.721653 +-0.440952 +1.060595 +-0.559696 +-2.010491 +-2.176368 +-0.872711 +1.517115 +0.313958 +0.125369 +0.173887 +0.431068 +0.746213 +0.934736 +0.886248 +0.629133 +# The principal components (eigenvectors) of identity or combined identity and expression model +84 +10 +6 +0.167881 0.037515 -0.058646 0.033467 0.007013 0.083344 0.140162 0.014855 0.064846 -0.170941 +0.144747 0.025369 -0.029522 0.011262 -0.071090 0.203042 0.058466 0.020071 0.048015 -0.098735 +0.130919 0.001976 0.015781 -0.021780 -0.107202 0.203982 -0.058147 0.013939 0.001668 0.032456 +0.134498 -0.018961 0.050725 -0.046303 -0.080169 0.085614 -0.141365 0.000051 -0.047046 0.145781 +0.153386 -0.025177 0.054841 -0.047942 -0.005828 -0.082724 -0.142441 -0.013459 -0.069592 0.174858 +0.171298 -0.014498 0.028042 -0.026941 0.077441 -0.217018 -0.062715 -0.021341 -0.053763 0.101612 +0.190348 0.010362 -0.019585 0.007306 0.108386 -0.203362 0.055867 -0.012543 -0.006414 -0.028538 +0.186769 0.031299 -0.054529 0.031829 0.081354 -0.084994 0.139085 0.001345 0.042300 -0.141864 +-0.152472 0.045932 -0.082773 -0.055658 -0.007529 -0.054010 -0.199419 -0.134126 -0.101864 0.104416 +-0.184330 -0.002781 -0.040384 -0.080832 -0.080460 -0.115659 -0.055044 -0.080257 0.058662 0.101912 +-0.213285 -0.008939 -0.034917 -0.022841 -0.034538 -0.123140 0.030932 -0.037313 0.101512 0.081439 +-0.232555 0.003035 -0.055702 0.033152 0.076396 0.011744 0.117545 -0.262249 0.015413 -0.155968 +-0.234134 0.004997 -0.053270 0.057837 0.186495 0.082705 0.126911 -0.442421 -0.063530 -0.192235 +-0.225329 -0.015439 0.023166 0.064446 0.087217 0.069482 0.099691 -0.249926 -0.080107 -0.028214 +-0.207372 -0.062591 0.119437 0.054907 -0.076820 0.048100 0.135908 0.057742 0.004103 0.009908 +-0.207309 -0.052291 0.099881 0.114814 -0.132728 0.038736 0.063320 0.127790 -0.094419 0.019914 +-0.207618 -0.029961 0.076210 0.091900 -0.049907 0.030279 -0.028983 0.273244 -0.042669 -0.005952 +-0.200832 -0.002349 0.027929 0.031359 -0.014172 0.005684 -0.042899 0.435980 0.098097 0.013502 +-0.184402 0.009464 -0.017039 -0.048177 0.004151 0.026121 -0.058695 0.257197 0.110822 0.032434 +-0.165398 0.018683 -0.044018 -0.098351 0.012939 0.024937 -0.134807 0.069897 0.073109 -0.022702 +0.153118 0.016465 -0.024583 0.006636 0.037945 -0.028369 0.160085 0.033318 -0.184813 0.078178 +0.154866 0.007385 -0.008461 -0.004109 0.049639 -0.092397 0.026746 -0.005949 -0.022710 -0.000382 +0.149017 -0.002774 0.012207 -0.018561 0.033647 -0.104700 -0.125446 -0.043084 0.148359 -0.076732 +0.138999 -0.008057 0.025308 -0.028236 -0.000666 -0.058065 -0.207285 -0.056337 0.228233 -0.106257 +0.130680 -0.005372 0.023172 -0.027479 -0.033190 0.020171 -0.170867 -0.037942 0.170086 -0.071581 +0.128933 0.003706 0.007052 -0.016750 -0.044871 0.084172 -0.037576 0.001329 0.007933 0.007089 +0.134779 0.013860 -0.013609 -0.002322 -0.028876 0.096467 0.114542 0.038469 -0.163204 0.083593 +0.144797 0.019145 -0.026714 0.007369 0.005424 0.049859 0.196430 0.051719 -0.243027 0.113007 +0.016389 -0.154283 -0.012634 0.011202 0.127838 -0.233231 0.083422 -0.020541 0.004212 -0.018229 +0.021618 -0.125432 -0.048000 0.070337 0.075609 -0.129863 0.130471 -0.029065 0.084827 -0.065960 +0.017817 -0.117497 -0.053283 0.089178 -0.015735 0.045359 0.106064 -0.023203 0.119076 -0.070549 +0.007213 -0.135126 -0.025388 0.056689 -0.092686 0.189792 0.024499 -0.006388 0.086896 -0.029308 +-0.003984 -0.167992 0.019345 -0.008099 -0.110167 0.218829 -0.066444 0.011530 0.007137 0.033605 +-0.010850 -0.196699 0.059026 -0.068250 -0.066030 0.122601 -0.115358 0.019806 -0.069346 0.083334 +-0.005412 -0.204777 0.059994 -0.086075 0.033407 -0.059761 -0.089086 0.014191 -0.107727 0.085926 +0.005192 -0.187148 0.032099 -0.053586 0.110358 -0.204194 -0.007521 -0.002624 -0.075546 0.044685 +0.051597 0.123490 -0.100835 0.113633 0.277072 0.404462 -0.158693 -0.089593 -0.059518 -0.031557 +0.046520 0.191125 0.186443 0.028729 0.050283 0.218925 -0.101211 0.048101 -0.011525 -0.043765 +0.031496 0.229386 0.360031 -0.050497 -0.104395 0.079803 -0.054671 -0.073899 -0.062985 -0.023917 +0.008466 0.249892 0.434476 -0.046985 -0.170644 -0.092034 0.008838 -0.087026 -0.030030 0.019115 +-0.020457 0.260307 0.347209 0.044215 -0.051959 -0.147993 0.103852 -0.081857 -0.000604 -0.087831 +-0.049636 0.264882 0.162264 0.120013 0.170250 -0.115936 0.057634 0.079663 0.011493 -0.053341 +-0.074908 0.222322 -0.128680 0.106279 0.515992 -0.037382 -0.166184 0.282250 -0.092724 -0.029211 +-0.061014 0.208181 -0.221481 -0.003480 0.048193 -0.126544 0.030670 0.093612 -0.016308 -0.019164 +-0.039475 0.208053 -0.288236 -0.127299 -0.178571 -0.148602 0.040236 0.004113 -0.004457 -0.016551 +-0.015936 0.203274 -0.317303 -0.176584 -0.301025 -0.090120 0.038670 -0.012354 -0.000247 -0.036027 +0.007963 0.191505 -0.289781 -0.098710 -0.251918 0.026406 0.025857 -0.011773 0.026086 0.020397 +0.031390 0.166293 -0.221317 0.010778 -0.065560 0.197405 -0.013358 -0.038202 0.028971 0.015787 +0.004235 -0.165003 0.019162 -0.015569 0.044130 -0.113124 -0.088548 -0.042014 0.167473 -0.083327 +-0.000300 -0.172691 0.031731 -0.029747 0.010395 -0.055596 -0.169946 -0.050038 0.239789 -0.125691 +-0.002022 -0.169263 0.029080 -0.021466 -0.029459 0.025877 -0.142794 -0.034375 0.183570 -0.078222 +0.000078 -0.156729 0.012766 0.004408 -0.052075 0.083550 -0.023030 -0.004196 0.031710 0.031351 +0.004768 -0.142435 -0.007651 0.032701 -0.044203 0.083633 0.119139 0.022826 -0.126885 0.138947 +0.009301 -0.134752 -0.020213 0.046851 -0.010464 0.026098 0.200464 0.030859 -0.199272 0.181460 +0.011023 -0.138177 -0.017566 0.038590 0.029375 -0.055348 0.173359 0.015191 -0.142999 0.133883 +0.008925 -0.150706 -0.001258 0.012743 0.051988 -0.113014 0.053667 -0.014997 0.008933 0.024161 +-0.146414 -0.002516 0.028155 -0.163578 0.022690 0.023539 0.002211 -0.006223 -0.035002 0.012582 +-0.109902 0.040269 0.033462 -0.110156 -0.017675 -0.030878 0.039946 -0.034778 0.056210 0.143990 +-0.014166 0.059774 0.028433 -0.073564 -0.028456 -0.056347 0.078713 -0.035782 0.114925 0.182190 +0.084713 0.044573 0.016014 -0.075237 -0.003338 -0.037950 0.095804 -0.008646 0.106748 0.104806 +0.128814 0.003571 0.003480 -0.114195 0.042965 0.013538 0.081206 0.030733 0.036469 -0.042832 +0.099074 -0.043950 -0.000509 -0.171174 0.086675 0.069487 0.039093 0.061946 -0.059545 -0.184463 +-0.003434 -0.058719 0.003203 -0.204209 0.094112 0.093425 0.004704 0.060292 -0.113458 -0.212440 +-0.102313 -0.043518 0.015622 -0.202536 0.068994 0.075027 -0.012387 0.033157 -0.105281 -0.135056 +0.068631 -0.013724 0.027292 0.231437 -0.037554 0.034751 0.337456 0.134888 0.312055 -0.132196 +0.055992 -0.018520 -0.024499 0.236434 -0.057326 -0.072106 -0.011848 0.151259 -0.049264 -0.117501 +0.039393 -0.033167 -0.026588 0.194395 -0.163302 -0.053331 -0.095733 0.027393 -0.169484 -0.254320 +0.013759 -0.046657 -0.031453 0.191733 -0.202303 -0.154083 -0.244663 -0.006407 -0.264460 -0.184882 +-0.015599 -0.034327 -0.050684 0.208434 -0.145282 -0.134450 -0.225097 -0.057805 -0.253738 -0.159062 +-0.038477 -0.010328 -0.025919 0.225952 -0.098722 -0.012521 -0.024397 -0.078886 -0.051188 -0.094188 +-0.054913 -0.004523 0.015086 0.231138 -0.115127 0.082549 0.124313 -0.000052 0.180789 0.076509 +-0.036452 0.030786 -0.028097 0.176770 -0.015768 0.029793 -0.046117 0.020525 0.047330 0.165226 +-0.009409 0.051946 -0.050194 0.162935 0.085636 -0.028224 -0.126599 0.046702 0.099383 0.240695 +0.019928 0.055883 -0.059556 0.147688 0.109694 0.020391 -0.066574 -0.102694 0.128555 0.213173 +0.042182 0.043848 -0.041760 0.116811 0.078314 0.051616 -0.106453 -0.228687 0.036561 0.260986 +0.056256 0.022936 -0.019524 0.146331 0.032207 -0.007908 -0.035410 -0.090636 -0.012725 0.205397 +-0.050587 -0.024716 0.023247 -0.172154 0.048419 0.037544 0.009258 0.022863 -0.065299 -0.050654 +-0.007396 -0.031398 0.017754 -0.172866 0.059650 0.047306 0.028721 0.038858 -0.090778 -0.069213 +0.034441 -0.022881 0.015629 -0.156918 0.055237 0.036211 0.045403 0.038219 -0.063438 -0.053697 +0.050412 -0.004156 0.018118 -0.133655 0.037766 0.010766 0.049531 0.021320 0.000702 -0.013208 +0.031165 0.013807 0.023762 -0.116702 0.017473 -0.014122 0.038694 -0.001937 0.064074 0.028511 +-0.012020 0.020487 0.029254 -0.115987 0.006243 -0.023880 0.019242 -0.017932 0.089559 0.047033 +-0.053849 0.011973 0.031379 -0.131930 0.010655 -0.012793 0.002562 -0.017294 0.062223 0.031533 +-0.069827 -0.006750 0.028891 -0.155196 0.028124 0.012648 -0.001577 -0.000397 -0.001922 -0.008919 +# The variances of the components (eigenvalues) of identity or combined identity and expression model +1 +10 +6 +111.570907 32.910130 22.463857 3.183176 1.531432 1.381999 0.770569 0.635129 0.500532 0.422800 diff --git a/OpenFace/model/model_inner/clnf_inner.txt b/OpenFace/model/model_inner/clnf_inner.txt new file mode 100644 index 0000000000000000000000000000000000000000..63a27e5f97d2d55d39a5324d8067e53d860e0961 --- /dev/null +++ b/OpenFace/model/model_inner/clnf_inner.txt @@ -0,0 +1,2 @@ +PDM pdms/pdm_51_inner.txt +PatchesCCNF patch_experts/ccnf_patches_1.00_inner.txt \ No newline at end of file diff --git a/OpenFace/model/model_inner/main_clnf_inner.txt b/OpenFace/model/model_inner/main_clnf_inner.txt new file mode 100644 index 0000000000000000000000000000000000000000..f71c9c36d7b7c836d905d06a845e216d94f861e0 --- /dev/null +++ b/OpenFace/model/model_inner/main_clnf_inner.txt @@ -0,0 +1 @@ +LandmarkDetector clnf_inner.txt \ No newline at end of file diff --git a/OpenFace/model/model_inner/patch_experts/ccnf_patches_1.00_inner.txt b/OpenFace/model/model_inner/patch_experts/ccnf_patches_1.00_inner.txt new file mode 100644 index 0000000000000000000000000000000000000000..3afc9b5abe22bb9e2c47f5f4e2e1523c22b3dcf9 --- /dev/null +++ b/OpenFace/model/model_inner/patch_experts/ccnf_patches_1.00_inner.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:79eb8ab716abe420a1604c8db8c107e0b9774bedfd91f595875e6bad0d37156c +size 2043100 diff --git a/OpenFace/model/model_inner/pdms/pdm_51_inner.txt b/OpenFace/model/model_inner/pdms/pdm_51_inner.txt new file mode 100644 index 0000000000000000000000000000000000000000..0cc10db39c69f4202c40b616c8b3c2727d4660d6 --- /dev/null +++ b/OpenFace/model/model_inner/pdms/pdm_51_inner.txt @@ -0,0 +1,319 @@ +# The mean values of the components (in mm) +153 +1 +6 +-61.487065 +-51.509332 +-37.921431 +-24.057516 +-11.630855 +12.064575 +25.080979 +38.237788 +50.986671 +59.712693 +0.585413 +0.765027 +0.986699 +1.246623 +-15.032333 +-7.411138 +0.343475 +8.042640 +14.960422 +-46.398983 +-37.943690 +-28.132841 +-19.916545 +-28.549994 +-38.380366 +19.002468 +27.657580 +37.180192 +44.841001 +37.909841 +28.497442 +-29.562845 +-17.967543 +-7.015967 +0.052801 +8.043929 +18.433173 +28.157774 +18.519833 +8.494683 +-0.070888 +-7.892087 +-18.695338 +-24.992703 +-7.290790 +-0.039765 +8.052638 +23.868342 +8.019854 +-0.212622 +-7.631916 +-37.103038 +-46.958179 +-50.611833 +-50.104582 +-46.101873 +-46.640281 +-50.673103 +-50.958467 +-46.902842 +-37.264890 +-31.676015 +-21.117514 +-10.737646 +0.158715 +12.338379 +14.273187 +15.946075 +14.281476 +12.541686 +-25.706010 +-31.203003 +-31.244206 +-25.361289 +-23.810857 +-23.539489 +-25.401771 +-31.558920 +-31.146069 +-25.831501 +-23.695860 +-23.829951 +39.570821 +32.160420 +28.582469 +30.231097 +28.688419 +32.668615 +39.377912 +46.579784 +49.241136 +49.857061 +49.336194 +46.680342 +39.469109 +35.142334 +35.627156 +35.092954 +39.393934 +40.374411 +41.028762 +40.536741 +27.039832 +20.931821 +14.543527 +7.608953 +2.074720 +2.333803 +7.566715 +14.068191 +20.304220 +25.944547 +-0.320469 +-8.941564 +-17.912415 +-26.452684 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1655664 diff --git a/OpenFace/model/patch_experts/cen_patches_0.25_of.dat b/OpenFace/model/patch_experts/cen_patches_0.25_of.dat new file mode 100644 index 0000000000000000000000000000000000000000..47d0edcb56d81700cadc82c0b3f7db2a9724ad5d --- /dev/null +++ b/OpenFace/model/patch_experts/cen_patches_0.25_of.dat @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:99d3df9888115428075de7b8f1bc86176881de640c2fdf47e2ab0cee556661bf +size 60602360 diff --git a/OpenFace/model/patch_experts/cen_patches_0.35_of.dat b/OpenFace/model/patch_experts/cen_patches_0.35_of.dat new file mode 100644 index 0000000000000000000000000000000000000000..bf480743d57a4fabb7f0ab14a01adf35d86a451f --- /dev/null +++ b/OpenFace/model/patch_experts/cen_patches_0.35_of.dat @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1070bff51b077ee6a18ce8e1ebbc6f568e1a7a469f6911a8263413878b3df469 +size 60602360 diff --git a/OpenFace/model/patch_experts/cen_patches_0.50_of.dat b/OpenFace/model/patch_experts/cen_patches_0.50_of.dat new file mode 100644 index 0000000000000000000000000000000000000000..99fc83be4519f9ff7050582528c95ededafa4e68 --- /dev/null +++ b/OpenFace/model/patch_experts/cen_patches_0.50_of.dat @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3f08124067e326e83e3261a55c79493e745b01138c9c1b59713dd9b17424168e +size 154289792 diff --git a/OpenFace/model/patch_experts/cen_patches_1.00_of.dat b/OpenFace/model/patch_experts/cen_patches_1.00_of.dat new file mode 100644 index 0000000000000000000000000000000000000000..6f445788b62e968aafd021561fbc586953178d9a --- /dev/null +++ b/OpenFace/model/patch_experts/cen_patches_1.00_of.dat @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bed961bbfa2cc41a709d44c1d1d8a92b2537bc46f2fd6ab430c137fedd0e21b0 +size 154289792 diff --git a/OpenFace/model/patch_experts/svr_patches_0.25_general.txt b/OpenFace/model/patch_experts/svr_patches_0.25_general.txt new file mode 100644 index 0000000000000000000000000000000000000000..aea5f406a9c02380d3ced68995081fb404624cf1 --- /dev/null +++ b/OpenFace/model/patch_experts/svr_patches_0.25_general.txt @@ -0,0 +1,1498 @@ +# scaling factor of training +0.250000 +# number of views +7 +# centers of the views +3 +1 +6 +0.000000000 +0.000000000 +0.000000000 +3 +1 +6 +0.000000000 +-20.000000000 +0.000000000 +3 +1 +6 +0.000000000 +-45.000000000 +0.000000000 +3 +1 +6 +0.000000000 +-70.000000000 +0.000000000 +3 +1 +6 +0.000000000 +70.000000000 +-0.000000000 +3 +1 +6 +0.000000000 +45.000000000 +-0.000000000 +3 +1 +6 +0.000000000 +20.000000000 +-0.000000000 +# visibility indices per view +68 +1 +4 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +68 +1 +4 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 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-0.840130 -0.091119 -0.017724 0.028305 0.005535 -0.011481 0.031436 0.067312 0.057552 -0.002448 -0.062550 -0.086011 0.074241 diff --git a/OpenFace/model/patch_experts/svr_patches_0.25_wild.txt b/OpenFace/model/patch_experts/svr_patches_0.25_wild.txt new file mode 100644 index 0000000000000000000000000000000000000000..bdb5e53baab557764c0ad65ab461d6a0e445b3c5 --- /dev/null +++ b/OpenFace/model/patch_experts/svr_patches_0.25_wild.txt @@ -0,0 +1,646 @@ +# scaling factor of training +0.250000 +# number of views +3 +# centers of the views +3 +1 +6 +0.000000000 +0.000000000 +0.000000000 +3 +1 +6 +0.000000000 +-20.000000000 +0.000000000 +3 +1 +6 +0.000000000 +20.000000000 +-0.000000000 +# visibility indices per view +68 +1 +4 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +68 +1 +4 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 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0.102386 -0.006342 0.016524 -0.024687 -0.062322 0.018439 -0.052105 0.019009 -0.056471 -0.031814 0.093739 0.092384 -0.002912 0.035473 -0.036918 -0.029460 0.026625 -0.029055 -0.030783 -0.067922 0.003461 0.094520 0.104472 0.063278 0.048991 -0.022021 -0.373481 -0.733849 -0.118288 -0.090655 -0.027131 0.039373 0.090729 0.077222 0.086153 0.037379 -0.093265 -0.143624 -0.156189 0.124143 diff --git a/OpenFace/model/patch_experts/svr_patches_0.35_general.txt b/OpenFace/model/patch_experts/svr_patches_0.35_general.txt new file mode 100644 index 0000000000000000000000000000000000000000..5832871015bd1c9645787be7aad4485bbd3b891e --- /dev/null +++ b/OpenFace/model/patch_experts/svr_patches_0.35_general.txt @@ -0,0 +1,1498 @@ +# scaling factor of training +0.350000 +# number of views +7 +# centers of the views +3 +1 +6 +0.000000000 +0.000000000 +0.000000000 +3 +1 +6 +0.000000000 +-20.000000000 +0.000000000 +3 +1 +6 +0.000000000 +-45.000000000 +0.000000000 +3 +1 +6 +0.000000000 +-70.000000000 +0.000000000 +3 +1 +6 +0.000000000 +70.000000000 +-0.000000000 +3 +1 +6 +0.000000000 +45.000000000 +-0.000000000 +3 +1 +6 +0.000000000 +20.000000000 +-0.000000000 +# visibility indices per view +68 +1 +4 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +68 +1 +4 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +68 +1 +4 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +68 +1 +4 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +0 +0 +0 +0 +0 +0 +0 +1 +1 +1 +1 +0 +0 +0 +0 +0 +0 +1 +1 +1 +1 +1 +0 +1 +0 +0 +1 +1 +1 +0 +1 +1 +0 +0 +0 +0 +0 +0 +1 +1 +1 +1 +0 +0 +0 +0 +0 +1 +1 +1 +0 +1 +1 +0 +0 +0 +1 +1 +68 +1 +4 +0 +0 +0 +0 +0 +0 +0 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +0 +0 +0 +0 +0 +1 +1 +1 +1 +1 +1 +1 +1 +0 +0 +1 +0 +1 +0 +0 +0 +0 +0 +0 +0 +1 +1 +1 +1 +1 +0 +0 +0 +1 +1 +1 +1 +1 +1 +1 +0 +0 +1 +1 +0 +0 +0 +0 +1 +1 +68 +1 +4 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +68 +1 +4 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +# Patches themselves (1 line patches of a vertex) +3 11 11 2 2 0 0.103912 7.513077 -5.322085 11 11 5 0.032313 0.051820 0.041005 0.087218 0.096889 -0.019871 -0.060682 -0.024765 -0.037056 -0.092696 0.074027 -0.077658 -0.064983 -0.050620 0.019988 0.063661 0.003808 0.010970 0.057996 0.043381 -0.040819 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0.030817 -0.020478 0.099940 -0.030995 0.036072 0.016325 0.007279 0.020675 0.006594 0.025111 0.017857 0.017013 0.008094 0.074680 0.030731 0.031938 0.008707 0.005697 0.013647 -0.010607 0.003125 -0.007425 -0.014566 -0.018532 -0.176582 0.020024 0.058417 0.050587 0.060041 0.053249 0.038734 0.057470 0.056437 0.060145 0.030590 +2 1 0.031413 4.804261 -4.403501 11 11 5 -0.099277 0.003950 -0.063088 -0.004269 -0.001814 0.032926 -0.018496 0.012239 -0.007398 0.088002 -0.034963 0.004495 0.011497 -0.038847 0.021667 0.014805 0.026365 -0.020307 0.012957 0.052649 0.124740 0.009344 -0.051220 0.030411 -0.059940 0.019556 -0.005242 -0.023187 -0.036130 0.004251 0.030174 0.104782 -0.015089 -0.092274 0.132469 -0.032871 0.029678 0.010752 -0.009585 -0.011043 0.029190 0.026669 0.078245 0.022297 -0.156056 0.160359 -0.011434 0.031481 0.016946 0.022676 0.003328 0.000709 -0.014135 0.064044 0.002000 0.096574 0.268762 0.160516 0.153551 0.157049 0.178413 0.122659 0.059948 0.003846 0.082209 -0.067215 0.259366 0.184101 0.129472 0.122086 0.118188 0.094814 0.046869 0.042913 0.057877 0.071873 -0.032886 0.015062 -0.023828 -0.016842 -0.040868 -0.060128 -0.054633 -0.034984 0.003192 0.031198 -0.037842 -0.035600 -0.082053 -0.144896 -0.071339 -0.057279 -0.044872 -0.032375 -0.011755 -0.011844 -0.015908 -0.181597 -0.039241 -0.020196 -0.164770 -0.108400 -0.159960 -0.160151 -0.157125 -0.166778 -0.184914 -0.169729 -0.319261 0.016551 0.005436 -0.053206 0.042520 -0.011316 -0.025690 -0.001858 0.007579 -0.019988 0.013949 -0.214300 0.060067 +3 11 11 2 2 0 0.056400 9.959532 -4.957429 11 11 5 0.028569 0.015085 0.032969 -0.023620 -0.058051 -0.006549 -0.002315 -0.050608 -0.055182 -0.009161 0.088863 -0.061672 -0.051318 -0.058225 -0.020403 0.027125 0.043908 0.033941 0.016440 -0.018291 -0.031369 -0.052402 -0.074120 0.069057 -0.012976 0.037573 0.079201 0.052611 0.025959 0.020289 -0.011201 -0.034884 -0.057363 -0.284180 0.092222 -0.014062 0.095561 0.167848 0.120962 0.089075 0.115771 0.076869 0.019176 -0.071499 -0.352443 0.002588 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/dev/null +++ b/OpenFace/model/patch_experts/svr_patches_0.35_wild.txt @@ -0,0 +1,646 @@ +# scaling factor of training +0.350000 +# number of views +3 +# centers of the views +3 +1 +6 +0.000000000 +0.000000000 +0.000000000 +3 +1 +6 +0.000000000 +-20.000000000 +0.000000000 +3 +1 +6 +0.000000000 +20.000000000 +-0.000000000 +# visibility indices per view +68 +1 +4 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +68 +1 +4 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +68 +1 +4 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +# 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0.107608 0.070293 0.014953 -0.180780 -0.174373 0.130089 0.120471 -0.024801 -0.072779 0.045837 -0.082714 0.045396 0.081581 0.144160 -0.111140 -0.166180 0.129560 0.202026 -0.021913 -0.038743 -0.007919 -0.094721 -0.054221 -0.028532 0.203905 0.040687 -0.098627 0.069071 0.215341 -0.005694 -0.079143 -0.037400 -0.022729 0.000726 -0.037811 0.090998 -0.026996 -0.167884 -0.123935 0.049311 -0.000099 -0.068936 -0.001677 -0.060442 0.016237 0.026108 0.061108 -0.011819 -0.042602 -0.001332 0.056231 0.059960 -0.045076 -0.071865 -0.067231 0.196364 0.117141 -0.007535 -0.169529 -0.170726 0.067572 0.119915 0.011208 -0.063862 0.142393 +2 1 0.069046 6.570457 -4.205848 11 11 5 -0.046283 -0.015929 -0.058782 -0.081105 -0.026608 0.016828 -0.018337 -0.010387 0.037571 -0.007393 0.029321 -0.111542 -0.208533 -0.137601 -0.019890 0.095832 0.109872 0.097670 0.065059 -0.006001 -0.054271 -0.045027 0.014524 -0.382646 -0.080568 0.123091 0.151593 0.088017 0.069902 0.037809 -0.042116 -0.091872 -0.011479 -0.014528 -0.415416 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0.024425 -0.036785 -0.102954 0.034814 0.173689 0.132141 0.104015 0.067663 -0.055213 -0.112296 -0.089102 -0.072431 -0.124707 -0.151151 0.098803 0.256858 0.090555 -0.029385 0.012516 -0.067469 -0.165792 -0.188922 -0.087656 +3 11 11 2 2 0 0.122347 8.374780 -4.260668 11 11 5 0.038500 0.013076 -0.009985 -0.126031 -0.152666 -0.056908 -0.028371 -0.087040 0.028399 0.049320 0.132217 -0.002292 0.041715 0.047026 0.037842 0.014397 0.027588 0.108620 0.056000 0.007003 -0.060058 0.035856 -0.043015 0.000533 -0.010168 0.022156 -0.043654 -0.127083 0.038707 0.084056 -0.022542 -0.103724 0.075801 -0.107970 -0.010167 0.018006 0.132609 0.062329 -0.139610 0.020098 0.137398 -0.034163 -0.116567 0.035841 -0.033686 0.015577 -0.003287 0.099198 0.032815 -0.217882 0.019665 0.160739 -0.065143 -0.105942 0.006462 0.012340 0.058260 0.011806 0.056603 -0.059029 -0.231648 0.142950 0.160268 -0.091073 -0.103127 0.016592 -0.033209 0.006346 0.051992 0.076442 -0.098881 -0.144422 0.243018 0.064112 -0.076787 -0.084138 0.051785 -0.000330 -0.011734 0.073335 0.015186 -0.154302 -0.071324 0.189186 -0.033803 0.000738 -0.082494 0.053149 0.007841 -0.010161 0.053022 -0.036620 -0.150960 -0.057855 0.049736 -0.043665 0.088976 -0.055547 0.100105 0.108568 0.058067 0.128640 0.079483 0.020173 -0.022289 -0.056601 -0.083126 0.037507 -0.129775 0.066361 0.099203 -0.060884 -0.079086 -0.180454 -0.166912 -0.147040 -0.041998 0.095866 0.199887 -0.003133 0.233278 +2 1 0.071703 7.481243 -4.463337 11 11 5 -0.068000 -0.037323 0.037977 0.061119 0.017056 0.000631 0.020528 0.001321 -0.004748 -0.056193 -0.048591 0.066777 0.041117 0.076432 0.109591 0.112417 0.059053 0.031348 -0.014094 -0.000873 -0.021444 0.030974 -0.008215 -0.036496 0.013229 0.096727 0.046297 -0.003539 -0.024694 -0.065336 -0.053983 -0.036758 -0.018850 -0.010149 -0.024682 0.031507 0.094624 0.046167 0.014395 0.008519 -0.095070 -0.062291 -0.023680 0.001294 0.033799 0.024055 0.064010 0.086202 0.004152 0.031869 0.048384 -0.099421 -0.056342 -0.015560 0.010569 0.035889 0.016062 0.059502 0.025435 -0.033700 0.080129 0.142115 -0.068162 -0.028155 -0.019149 0.021854 -0.040134 -0.053831 0.023150 0.067076 0.023715 0.072845 0.112987 -0.052336 -0.027866 -0.044518 0.032144 -0.048159 -0.105556 0.030943 0.150433 0.090314 0.058532 0.084402 -0.030499 -0.076044 -0.099016 0.005821 -0.038187 -0.086195 0.027686 0.153655 0.077823 -0.021960 0.015343 -0.009373 0.005429 -0.031999 0.067455 -0.007121 -0.107292 0.029894 0.177775 0.123192 0.002484 -0.019301 -0.072880 -0.214249 -0.506393 -0.255831 -0.051589 -0.093557 0.071309 0.150523 0.088213 0.053708 0.116676 0.012287 -0.131685 -0.392299 0.024426 diff --git a/OpenFace/model/patch_experts/svr_patches_0.5_general.txt b/OpenFace/model/patch_experts/svr_patches_0.5_general.txt new file mode 100644 index 0000000000000000000000000000000000000000..ba31ae683ec039dbdfed9b72b6236f858226f2bd --- /dev/null +++ b/OpenFace/model/patch_experts/svr_patches_0.5_general.txt @@ -0,0 +1,1498 @@ +# scaling factor of training +0.500000 +# number of views +7 +# centers of the views +3 +1 +6 +0.000000000 +0.000000000 +0.000000000 +3 +1 +6 +0.000000000 +-20.000000000 +0.000000000 +3 +1 +6 +0.000000000 +-45.000000000 +0.000000000 +3 +1 +6 +0.000000000 +-70.000000000 +0.000000000 +3 +1 +6 +0.000000000 +70.000000000 +-0.000000000 +3 +1 +6 +0.000000000 +45.000000000 +-0.000000000 +3 +1 +6 +0.000000000 +20.000000000 +-0.000000000 +# visibility indices per view +68 +1 +4 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +68 +1 +4 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +68 +1 +4 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +68 +1 +4 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +0 +0 +0 +0 +0 +0 +0 +1 +1 +1 +1 +0 +0 +0 +0 +0 +0 +1 +1 +1 +1 +1 +0 +1 +0 +0 +1 +1 +1 +0 +1 +1 +0 +0 +0 +0 +0 +0 +1 +1 +1 +1 +0 +0 +0 +0 +0 +1 +1 +1 +0 +1 +1 +0 +0 +0 +1 +1 +68 +1 +4 +0 +0 +0 +0 +0 +0 +0 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +0 +0 +0 +0 +0 +1 +1 +1 +1 +1 +1 +1 +1 +0 +0 +1 +0 +1 +0 +0 +0 +0 +0 +0 +0 +1 +1 +1 +1 +1 +0 +0 +0 +1 +1 +1 +1 +1 +1 +1 +0 +0 +1 +1 +0 +0 +0 +0 +1 +1 +68 +1 +4 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +68 +1 +4 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +# Patches themselves (1 line patches of a vertex) +3 11 11 2 2 0 0.084063 6.530603 -5.284854 11 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-0.018512 -0.049586 -0.025472 -0.031359 0.079322 0.195036 -0.031175 -0.093651 -0.064326 -0.006777 -0.030354 -0.035906 0.012207 0.041464 -0.011881 0.053076 0.170590 -0.018081 -0.064873 -0.095171 0.014577 -0.020092 -0.008952 0.101087 0.148144 0.031856 0.068437 0.143001 -0.047273 -0.082063 -0.175549 -0.004125 -0.009826 -0.045209 0.077428 0.110884 -0.024615 -0.044990 0.072222 0.003729 -0.008065 -0.167573 0.081933 -0.072704 -0.073103 0.109607 0.201931 0.163871 0.093054 0.070415 -0.070578 -0.130781 -0.450688 -0.158590 0.003399 -0.080544 0.037275 0.111290 0.086898 -0.062529 -0.055490 0.076391 0.117671 -0.260468 0.042630 diff --git a/OpenFace/model/patch_experts/svr_patches_0.5_wild.txt b/OpenFace/model/patch_experts/svr_patches_0.5_wild.txt new file mode 100644 index 0000000000000000000000000000000000000000..60eb0982858ccc18aaf291e7606b71b5befa49f9 --- /dev/null +++ b/OpenFace/model/patch_experts/svr_patches_0.5_wild.txt @@ -0,0 +1,646 @@ +# scaling factor of training +0.500000 +# number of views +3 +# centers of the views +3 +1 +6 +0.000000000 +0.000000000 +0.000000000 +3 +1 +6 +0.000000000 +-20.000000000 +0.000000000 +3 +1 +6 +0.000000000 +20.000000000 +-0.000000000 +# visibility indices per view +68 +1 +4 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +68 +1 +4 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +68 +1 +4 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +# Patches themselves (1 line patches of a vertex) +3 11 11 2 2 0 0.055415 4.408117 -4.411793 11 11 5 0.079518 0.070395 0.052210 0.056709 0.054742 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a/OpenFace/model/pdms/In-the-wild_aligned_PDM_68.txt b/OpenFace/model/pdms/In-the-wild_aligned_PDM_68.txt new file mode 100644 index 0000000000000000000000000000000000000000..d538397cde6a6ee0dc172787b37a377f002f3630 --- /dev/null +++ b/OpenFace/model/pdms/In-the-wild_aligned_PDM_68.txt @@ -0,0 +1,421 @@ +# The mean values of the components (in mm) +204 +1 +6 +-73.393523 +-72.775014 +-70.533638 +-66.850058 +-59.790187 +-48.368973 +-34.121101 +-17.875411 +0.098749 +17.477031 +32.648966 +46.372358 +57.343480 +64.388482 +68.212038 +70.486405 +71.375822 +-61.119406 +-51.287588 +-37.804800 +-24.022754 +-11.635713 +12.056636 +25.106256 +38.338588 +51.191007 +60.053851 +0.653940 +0.804809 +0.992204 +1.226783 +-14.772472 +-7.180239 +0.555920 +8.272499 +15.214351 +-46.047290 +-37.674688 +-27.883856 +-19.648268 +-28.272965 +-38.082418 +19.265868 +27.894191 +37.437529 +45.170805 +38.196454 +28.764989 +-28.916267 +-17.533194 +-6.684590 +0.381001 +8.375443 +18.876618 +28.794412 +19.057574 +8.956375 +0.381549 +-7.428895 +-18.160634 +-24.377490 +-6.897633 +0.340663 +8.444722 +24.474473 +8.449166 +0.205322 +-7.198266 +-29.801432 +-10.949766 +7.929818 +26.074280 +42.564390 +56.481080 +67.246992 +75.056892 +77.061286 +74.758448 +66.929021 +56.311389 +42.419126 +25.455880 +6.990805 +-11.666193 +-30.365191 +-49.361602 +-58.769795 +-61.996155 +-61.033399 +-56.686759 +-57.391033 +-61.902186 +-62.777713 +-59.302347 +-50.190255 +-42.193790 +-30.993721 +-19.944596 +-8.414541 +2.598255 +4.751589 +6.562900 +4.661005 +2.643046 +-37.471411 +-42.730510 +-42.711517 +-36.754742 +-35.134493 +-34.919043 +-37.032306 +-43.342445 +-43.110822 +-38.086515 +-35.532024 +-35.484289 +28.612716 +22.172187 +19.029051 +20.721118 +19.035460 +22.394109 +28.079924 +36.298248 +39.634575 +40.395647 +39.836405 +36.677899 +28.677771 +25.475976 +26.014269 +25.326198 +28.323008 +30.596216 +31.408738 +30.844876 +47.667532 +45.909403 +44.842580 +43.141114 +38.635298 +30.750622 +18.456453 +3.609035 +-0.881698 +5.181201 +19.176563 +30.770570 +37.628629 +40.886309 +42.281449 +44.142567 +47.140426 +14.254422 +7.268147 +0.442051 +-6.606501 +-11.967398 +-12.051204 +-7.315098 +-1.022953 +5.349435 +11.615746 +-13.380835 +-21.150853 +-29.284036 +-36.948060 +-20.132003 +-23.536684 +-25.944448 +-23.695741 +-20.858157 +7.037989 +3.021217 +1.353629 +-0.111088 +-0.147273 +1.476612 +-0.665746 +0.247660 +1.696435 +4.894163 +0.282961 +-1.172675 +-2.240310 +-15.934335 +-22.611355 +-23.748437 +-22.721995 +-15.610679 +-3.217393 +-14.987997 +-22.554245 +-23.591626 +-22.406106 +-15.121907 +-4.785684 +-20.893742 +-22.220479 +-21.025520 +-5.712776 +-20.671489 +-21.903670 +-20.328022 +# The principal components (eigenvectors) of identity or combined identity and expression model +204 +34 +6 +-0.007395 -0.093690 0.039362 0.204443 -0.104698 0.033568 -0.090173 0.010170 -0.042341 0.104375 0.032695 0.038750 0.064385 -0.037025 0.058377 0.032457 -0.100005 -0.082042 -0.053440 0.008782 -0.027164 -0.000368 -0.129614 0.035436 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+-63.884164 +-57.408209 +-43.494093 +-32.323206 +-21.219201 +-10.154099 +1.865699 +3.812307 +4.550229 +3.795547 +1.833301 +-39.575745 +-44.012326 +-44.271766 +-40.257729 +-37.775364 +-37.398870 +-40.324420 +-44.356723 +-44.121200 +-39.703605 +-37.507475 +-37.862166 +30.198595 +23.732715 +19.281161 +19.985394 +19.255417 +23.681657 +30.124395 +37.039208 +41.464305 +42.768918 +41.494874 +37.095259 +29.677178 +26.828533 +26.647927 +26.799121 +29.618278 +31.751934 +32.973459 +31.783409 +64.083586 +59.604237 +55.630937 +51.270408 +45.264067 +38.374113 +30.298802 +21.248638 +16.868691 +21.260056 +30.328419 +38.423826 +45.328428 +51.345204 +55.721454 +59.708635 +64.196230 +14.422033 +8.686064 +2.801790 +-2.729174 +-7.926259 +-7.862783 +-2.662158 +2.869104 +8.753808 +14.496714 +-20.448168 +-30.505334 +-40.604505 +-50.582207 +-32.451571 +-35.110317 +-36.937315 +-35.089217 +-32.412092 +5.389048 +2.570176 +0.218247 +-2.269159 +-0.023830 +2.115026 +-2.270098 +0.239954 +2.611755 +5.453688 +2.158551 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0.031114 -0.000590 0.005485 -0.070567 -0.026852 -0.193679 -0.213317 0.003024 +0.117592 -0.015042 0.098196 -0.027487 -0.036629 -0.025410 -0.002671 0.042636 -0.029774 0.076182 -0.010119 0.064261 0.095826 0.070074 -0.022651 -0.055786 0.090197 0.029593 0.033172 0.012973 -0.109351 -0.104770 -0.034005 +# The variances of the components (eigenvalues) of identity or combined identity and expression model +1 +23 +6 +795.358907 526.466957 323.962380 149.332995 108.218528 86.462266 78.978172 71.817921 63.124413 56.631775 49.840502 37.133586 32.292271 27.991391 25.613033 20.062292 16.082399 14.009888 12.585536 4.127530 1.562736 1.104694 0.909219 diff --git a/OpenFace/model/pdms/pdm_68_aligned_menpo.txt b/OpenFace/model/pdms/pdm_68_aligned_menpo.txt new file mode 100644 index 0000000000000000000000000000000000000000..fc8ce63f7be416764041e2543e4ac1a71761b9d5 --- /dev/null +++ b/OpenFace/model/pdms/pdm_68_aligned_menpo.txt @@ -0,0 +1,421 @@ +# The mean values of the components (in mm) +204 +1 +6 +-74.409969 +-74.137773 +-72.056496 +-67.915820 +-60.492143 +-48.932581 +-34.280137 +-17.760628 +0.514393 +18.358848 +34.248759 +48.283175 +59.483187 +66.851931 +71.085235 +73.234417 +73.635334 +-57.908717 +-49.068078 +-37.656400 +-25.729901 +-14.795319 +14.837927 +25.725682 +37.468905 +48.751250 +57.344747 +0.388587 +0.295350 +0.206926 +0.135388 +-15.498362 +-8.047687 +0.076176 +8.251717 +15.588112 +-45.515042 +-37.079098 +-27.956753 +-19.734098 +-28.329816 +-37.307204 +19.581417 +27.839109 +36.880288 +45.145696 +37.224189 +28.382643 +-27.977066 +-17.476087 +-6.663955 +0.244789 +7.291085 +17.947928 +28.125385 +18.687933 +8.539212 +0.242857 +-7.847825 +-18.237373 +-24.528857 +-7.104831 +0.273636 +7.739429 +24.736241 +7.968382 +0.229350 +-7.397604 +-30.248138 +-11.542661 +7.567939 +26.259376 +42.866270 +56.586917 +67.359002 +75.143546 +77.529037 +75.071547 +67.393621 +56.803934 +43.251437 +26.710196 +8.051821 +-10.968992 +-29.588592 +-52.172718 +-59.222312 +-61.949207 +-61.037213 +-57.532329 +-57.980133 +-61.715278 +-62.824686 +-60.232398 +-53.273185 +-40.542060 +-30.162960 +-20.232369 +-9.953997 +3.145708 +4.856711 +5.687939 +4.792678 +2.997034 +-37.288250 +-42.119954 +-42.362645 +-36.532986 +-34.532435 +-34.563887 +-36.758354 +-42.734934 +-42.649721 +-37.900147 +-35.115277 +-34.873489 +27.684553 +22.191257 +18.679379 +20.310742 +18.619478 +22.094260 +27.477987 +35.483072 +39.911465 +40.897777 +40.075424 +35.645402 +27.883771 +25.627237 +26.352545 +25.545734 +27.760907 +31.094250 +32.006269 +31.195088 +55.141955 +51.629462 +48.507238 +44.913190 +38.574081 +29.347878 +17.493884 +5.304010 +-1.371218 +5.342776 +17.851699 +30.323049 +40.570487 +47.890182 +52.297654 +55.916658 +60.074025 +13.228242 +6.263352 +-0.547326 +-6.314237 +-10.909358 +-13.043499 +-9.974582 +-5.801725 +-0.545529 +5.625345 +-13.691691 +-22.845392 +-32.311712 +-41.584645 +-21.080547 +-26.294217 +-29.003103 +-26.743598 +-22.061192 +9.230938 +3.879971 +1.219552 +1.017579 +0.985420 +3.658622 +-0.023947 +-0.773850 +0.704347 +5.283591 +0.421741 +-1.190907 +-7.844583 +-17.491041 +-24.300421 +-25.186808 +-24.409816 +-17.587587 +-7.690400 +-15.385836 +-21.456027 +-22.469566 +-21.062494 +-15.340380 +-9.728696 +-21.188579 +-22.512963 +-21.275175 +-9.282340 +-20.462720 +-21.609047 +-20.300170 +# The principal components (eigenvectors) of identity or combined identity and expression model +204 +30 +6 +-0.190469 -0.071184 -0.054685 0.088181 -0.005885 0.117801 -0.017233 -0.027212 -0.081376 -0.065794 0.104253 -0.043337 0.060539 0.027374 -0.030921 0.019905 -0.032263 0.073285 -0.027532 0.067513 -0.106417 -0.052799 0.090934 0.071815 -0.103673 -0.018281 -0.142612 -0.136544 0.104777 -0.059287 +-0.181987 -0.077301 -0.040768 0.081699 -0.010115 0.074060 -0.030833 -0.002966 -0.045981 -0.047412 0.056452 -0.056207 0.046458 0.008748 -0.018044 0.054030 -0.035939 0.107036 0.006886 0.061526 -0.048319 -0.066964 0.074145 0.045579 -0.068658 0.034547 -0.074267 -0.093346 -0.011087 -0.057005 +-0.173515 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+# The variances of the components (eigenvalues) of identity or combined identity and expression model +1 +30 +6 +1260.970668 622.171792 514.701969 368.231621 178.288803 141.373501 123.600203 108.077802 95.536656 70.169455 60.351993 56.235863 49.519216 44.933492 42.272445 39.564849 34.747298 28.830661 26.147964 25.285930 21.833110 18.995127 18.134667 16.329230 13.965430 12.933174 11.705294 11.301406 9.624030 8.423463 diff --git a/OpenFace/model/tris_68.txt b/OpenFace/model/tris_68.txt new file mode 100644 index 0000000000000000000000000000000000000000..a7e3a0c7fc8f33caf3d2ede280aeda0f38b2fae2 --- /dev/null +++ b/OpenFace/model/tris_68.txt @@ -0,0 +1,554 @@ +# Number of triangulations +7 +# Triangulation 1 +# triangulation +97 +3 +4 +58 67 59 +60 49 48 +58 59 6 +34 52 35 +44 45 25 +39 40 29 +37 18 36 +27 42 22 +23 44 24 +41 36 1 +50 62 51 +56 65 66 +57 58 7 +64 53 63 +28 27 39 +52 34 51 +54 14 35 +29 42 28 +19 20 24 +35 15 46 +37 19 18 +36 0 1 +18 17 36 +37 20 19 +38 20 37 +21 20 38 +21 38 39 +24 44 25 +30 34 35 +21 39 27 +28 42 27 +39 29 28 +29 30 35 +31 30 29 +30 33 34 +31 29 40 +36 17 0 +41 31 40 +31 32 30 +31 41 1 +49 31 48 +48 2 3 +67 60 59 +4 48 3 +5 48 4 +6 59 5 +59 48 5 +60 48 59 +7 58 6 +61 49 60 +58 66 67 +31 2 48 +31 50 32 +1 2 31 +61 50 49 +52 62 63 +50 31 49 +34 33 51 +51 62 52 +32 50 51 +50 61 62 +63 53 52 +54 55 11 +57 8 9 +66 58 57 +8 57 7 +56 57 9 +66 57 56 +10 56 9 +55 56 10 +53 54 35 +53 35 52 +12 54 11 +55 10 11 +65 56 55 +64 55 54 +65 55 64 +54 53 64 +12 13 54 +14 54 13 +15 35 14 +47 35 46 +33 32 51 +30 32 33 +29 35 47 +15 45 46 +22 21 27 +20 21 23 +43 23 22 +29 47 42 +23 21 22 +24 20 23 +22 42 43 +23 43 44 +45 16 26 +15 16 45 +25 45 26 +# Triangulation 2 +# triangulation +97 +3 +4 +58 67 59 +60 49 48 +58 59 6 +34 52 35 +44 45 25 +39 40 29 +37 18 36 +27 42 22 +23 44 24 +41 36 1 +50 62 51 +56 65 66 +57 58 7 +64 53 63 +28 27 39 +52 34 51 +54 14 35 +29 42 28 +19 20 24 +35 15 46 +37 19 18 +36 0 1 +18 17 36 +37 20 19 +38 20 37 +21 20 38 +21 38 39 +24 44 25 +30 34 35 +21 39 27 +28 42 27 +39 29 28 +29 30 35 +31 30 29 +30 33 34 +31 29 40 +36 17 0 +41 31 40 +31 32 30 +31 41 1 +49 31 48 +48 2 3 +67 60 59 +4 48 3 +5 48 4 +6 59 5 +59 48 5 +60 48 59 +7 58 6 +61 49 60 +58 66 67 +31 2 48 +31 50 32 +1 2 31 +61 50 49 +52 62 63 +50 31 49 +34 33 51 +51 62 52 +32 50 51 +50 61 62 +63 53 52 +54 55 11 +57 8 9 +66 58 57 +8 57 7 +56 57 9 +66 57 56 +10 56 9 +55 56 10 +53 54 35 +53 35 52 +12 54 11 +55 10 11 +65 56 55 +64 55 54 +65 55 64 +54 53 64 +12 13 54 +14 54 13 +15 35 14 +47 35 46 +33 32 51 +30 32 33 +29 35 47 +15 45 46 +22 21 27 +20 21 23 +43 23 22 +29 47 42 +23 21 22 +24 20 23 +22 42 43 +23 43 44 +45 16 26 +15 16 45 +25 45 26 +# Triangulation 3 +# triangulation +82 +3 +4 +58 67 59 +60 49 48 +58 59 6 +34 52 35 +44 45 25 +39 40 29 +37 18 36 +27 42 22 +23 44 24 +41 36 1 +50 62 51 +56 65 66 +57 58 7 +64 53 63 +28 27 39 +52 34 51 +29 42 28 +19 20 24 +37 19 18 +36 0 1 +18 17 36 +37 20 19 +38 20 37 +21 20 38 +21 38 39 +24 44 25 +30 34 35 +21 39 27 +28 42 27 +39 29 28 +31 30 29 +30 33 34 +31 29 40 +36 17 0 +41 31 40 +31 32 30 +31 41 1 +49 31 48 +48 2 3 +67 60 59 +4 48 3 +5 48 4 +6 59 5 +59 48 5 +60 48 59 +7 58 6 +61 49 60 +58 66 67 +31 2 48 +31 50 32 +1 2 31 +61 50 49 +52 62 63 +50 31 49 +34 33 51 +51 62 52 +32 50 51 +50 61 62 +63 53 52 +57 8 9 +66 58 57 +8 57 7 +56 57 9 +66 57 56 +53 35 52 +65 56 55 +64 55 54 +65 55 64 +54 53 64 +47 35 46 +33 32 51 +30 32 33 +15 45 46 +22 21 27 +20 21 23 +43 23 22 +29 47 42 +23 21 22 +24 20 23 +22 42 43 +23 43 44 +25 45 26 +# Triangulation 4 +# triangulation +31 +3 +4 +58 67 59 +58 59 6 +37 18 36 +41 36 1 +50 62 51 +57 58 7 +37 19 18 +36 0 1 +18 17 36 +37 20 19 +38 20 37 +31 30 29 +31 29 40 +36 17 0 +41 31 40 +31 41 1 +49 31 48 +48 2 3 +4 48 3 +5 48 4 +6 59 5 +59 48 5 +7 58 6 +58 66 67 +31 2 48 +1 2 31 +61 50 49 +50 31 49 +50 61 62 +66 58 57 +8 57 7 +# Triangulation 5 +# triangulation +97 +3 +4 +58 67 59 +60 49 48 +58 59 6 +34 52 35 +44 45 25 +39 40 29 +37 18 36 +27 42 22 +23 44 24 +41 36 1 +50 62 51 +56 65 66 +57 58 7 +64 53 63 +28 27 39 +52 34 51 +54 14 35 +29 42 28 +19 20 24 +35 15 46 +37 19 18 +36 0 1 +18 17 36 +37 20 19 +38 20 37 +21 20 38 +21 38 39 +24 44 25 +30 34 35 +21 39 27 +28 42 27 +39 29 28 +29 30 35 +31 30 29 +30 33 34 +31 29 40 +36 17 0 +41 31 40 +31 32 30 +31 41 1 +49 31 48 +48 2 3 +67 60 59 +4 48 3 +5 48 4 +6 59 5 +59 48 5 +60 48 59 +7 58 6 +61 49 60 +58 66 67 +31 2 48 +31 50 32 +1 2 31 +61 50 49 +52 62 63 +50 31 49 +34 33 51 +51 62 52 +32 50 51 +50 61 62 +63 53 52 +54 55 11 +57 8 9 +66 58 57 +8 57 7 +56 57 9 +66 57 56 +10 56 9 +55 56 10 +53 54 35 +53 35 52 +12 54 11 +55 10 11 +65 56 55 +64 55 54 +65 55 64 +54 53 64 +12 13 54 +14 54 13 +15 35 14 +47 35 46 +33 32 51 +30 32 33 +29 35 47 +15 45 46 +22 21 27 +20 21 23 +43 23 22 +29 47 42 +23 21 22 +24 20 23 +22 42 43 +23 43 44 +45 16 26 +15 16 45 +25 45 26 +# Triangulation 6 +# triangulation +82 +3 +4 +58 67 59 +60 49 48 +34 52 35 +44 45 25 +39 40 29 +37 18 36 +27 42 22 +23 44 24 +41 36 1 +50 62 51 +56 65 66 +57 58 7 +64 53 63 +28 27 39 +52 34 51 +54 14 35 +29 42 28 +19 20 24 +35 15 46 +37 19 18 +18 17 36 +37 20 19 +38 20 37 +21 20 38 +21 38 39 +24 44 25 +30 34 35 +21 39 27 +28 42 27 +39 29 28 +29 30 35 +30 33 34 +41 31 40 +31 32 30 +67 60 59 +60 48 59 +61 49 60 +58 66 67 +31 50 32 +61 50 49 +52 62 63 +50 31 49 +34 33 51 +51 62 52 +32 50 51 +50 61 62 +63 53 52 +54 55 11 +57 8 9 +66 58 57 +8 57 7 +56 57 9 +66 57 56 +10 56 9 +55 56 10 +53 54 35 +53 35 52 +12 54 11 +55 10 11 +65 56 55 +64 55 54 +65 55 64 +54 53 64 +12 13 54 +14 54 13 +15 35 14 +47 35 46 +33 32 51 +30 32 33 +29 35 47 +15 45 46 +22 21 27 +20 21 23 +43 23 22 +29 47 42 +23 21 22 +24 20 23 +22 42 43 +23 43 44 +45 16 26 +15 16 45 +25 45 26 +# Triangulation 7 +# triangulation +31 +3 +4 +44 45 25 +23 44 24 +56 65 66 +54 14 35 +35 15 46 +24 44 25 +29 30 35 +52 62 63 +51 62 52 +63 53 52 +54 55 11 +57 8 9 +56 57 9 +66 57 56 +10 56 9 +55 56 10 +53 54 35 +53 35 52 +12 54 11 +55 10 11 +65 56 55 +12 13 54 +14 54 13 +15 35 14 +47 35 46 +29 35 47 +15 45 46 +23 43 44 +45 16 26 +15 16 45 +25 45 26 diff --git a/OpenFace/model/tris_68_full.txt b/OpenFace/model/tris_68_full.txt new file mode 100644 index 0000000000000000000000000000000000000000..25ee5c0e473f4758eb07b465c611a377d5af242a --- /dev/null +++ b/OpenFace/model/tris_68_full.txt @@ -0,0 +1,114 @@ +111 +3 +4 +58 67 59 +60 49 48 +58 59 6 +34 52 35 +44 45 25 +39 40 29 +37 18 36 +27 42 22 +23 44 24 +41 36 1 +50 62 51 +56 65 66 +57 58 7 +64 53 63 +28 27 39 +52 34 51 +54 14 35 +29 42 28 +19 20 24 +35 15 46 +37 19 18 +36 0 1 +18 17 36 +37 20 19 +38 20 37 +21 20 38 +21 38 39 +24 44 25 +30 34 35 +21 39 27 +28 42 27 +39 29 28 +29 30 35 +31 30 29 +30 33 34 +31 29 40 +36 17 0 +41 31 40 +31 32 30 +31 41 1 +49 31 48 +48 2 3 +67 60 59 +4 48 3 +5 48 4 +6 59 5 +59 48 5 +60 48 59 +7 58 6 +61 49 60 +58 66 67 +31 2 48 +31 50 32 +1 2 31 +61 50 49 +52 62 63 +50 31 49 +34 33 51 +51 62 52 +32 50 51 +50 61 62 +63 53 52 +54 55 11 +57 8 9 +66 58 57 +8 57 7 +56 57 9 +66 57 56 +10 56 9 +55 56 10 +53 54 35 +53 35 52 +12 54 11 +55 10 11 +65 56 55 +64 55 54 +65 55 64 +54 53 64 +12 13 54 +14 54 13 +15 35 14 +47 35 46 +33 32 51 +30 32 33 +29 35 47 +15 45 46 +22 21 27 +20 21 23 +43 23 22 +29 47 42 +23 21 22 +24 20 23 +22 42 43 +23 43 44 +45 16 26 +15 16 45 +25 45 26 +41 36 37 +40 41 37 +38 40 37 +39 40 38 +47 42 43 +46 47 43 +46 43 44 +45 46 44 +67 60 61 +66 67 61 +62 66 61 +65 66 62 +63 65 62 +64 65 63 \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..bc79ba8a48bdcff6b195f03ec26da7fda6c11e64 --- /dev/null +++ b/README.md @@ -0,0 +1,59 @@ +# One-shot Talking Face Generation from Single-speaker Audio-Visual Correlation Learning (AAAI 2022) + +#### [Paper](https://arxiv.org/pdf/2112.02749.pdf) | [Demo](https://www.youtube.com/watch?v=HHj-XCXXePY) + +#### Requirements + +- Python >= 3.6 , Pytorch >= 1.8 and ffmpeg +- Set up [OpenFace](https://github.com/TadasBaltrusaitis/OpenFace) + - We use the OpenFace tools to extract the initial pose of the reference image + - Make sure you have installed this tool, and set the `OPENFACE_POSE_EXTRACTOR_PATH` in `config.py`. For example, it should be the absolute path of the "`FeatureExtraction.exe`" for Windows. +- Other requirements are listed in the 'requirements.txt' + + + +#### Pretrained Checkpoint + +Please download the pretrained checkpoint from [google-drive](https://drive.google.com/file/d/1mjFEozPR_2vMaVRMd9Agk_sU1VaiUYMl/view?usp=sharing) and unzip it to the directory (`/checkpoints`). Or manually modify the settings of `GENERATOR_CKPT` and `AUDIO2POSE_CKPT` in the `config.py`. + + + +#### Extract phoneme + +We employ the [CMU phoneset](https://github.com/cmusphinx/cmudict) to represent phonemes, the extra 'SIL' means silence. All the phonesets can be seen in '`phindex.json`'. + +We have extracted the phonemes for the audios in the '`sample/audio`' directory. For other audios, you can extract the phonemes by other ASR tools and then map them to the CMU phoneset. Or email to wangsuzhen@corp.netease.com for help. + + + +#### Generate Demo Results + +``` +python test_script.py --img_path xxx.jpg --audio_path xxx.wav --phoneme_path xxx.json --save_dir "YOUR_DIR" +``` + +Note that the input images must keep the same height and width and the face should be appropriately cropped as in `samples/imgs`. You can also preprocess your images with `image_preprocess.py`. + + + +#### License and Citation + +``` +@InProceedings{wang2021one, +author = Suzhen Wang, Lincheng Li, Yu Ding, Xin Yu +title = {One-shot Talking Face Generation from Single-speaker Audio-Visual Correlation Learning}, +booktitle = {AAAI 2022}, +year = {2022}, +} +``` + + + +#### Acknowledgement + +This codebase is based on [First Order Motion Model](https://github.com/AliaksandrSiarohin/first-order-model) and [imaginaire](https://github.com/NVlabs/imaginaire), thanks for their contributions. + + + + + diff --git a/checkpoints/audio2pose.ckpt b/checkpoints/audio2pose.ckpt new file mode 100644 index 0000000000000000000000000000000000000000..fb08998765cadfffa6d2a029cd582fdbf0a0aadc --- /dev/null +++ b/checkpoints/audio2pose.ckpt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b4babdddf02904082fb8f61aa91c02a13737e6ba69e7d389ff9f778d4b917b77 +size 176834707 diff --git a/checkpoints/generator.ckpt b/checkpoints/generator.ckpt new file mode 100644 index 0000000000000000000000000000000000000000..6af6511272b4c0cd41841a59059e70d4e8382c10 --- /dev/null +++ b/checkpoints/generator.ckpt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:45d3434f01cd3282712d317348c365a447026e4d21719f6763af288897af573c +size 436871987 diff --git a/config.py b/config.py new file mode 100644 index 0000000000000000000000000000000000000000..11fa6424389fdd22d7a5e4a14844b58515905154 --- /dev/null +++ b/config.py @@ -0,0 +1,3 @@ +OPENFACE_POSE_EXTRACTOR_PATH = "/content/one-shot-talking-face/OpenFace/FeatureExtraction" +GENERATOR_CKPT = "/content/one-shot-talking-face/checkpoints/generator.ckpt" +AUDIO2POSE_CKPT = "/content/one-shot-talking-face/checkpoints/audio2pose.ckpt" diff --git a/config_file/audio2kp.yaml b/config_file/audio2kp.yaml new file mode 100644 index 0000000000000000000000000000000000000000..74aaf4fe2a00751b616662be866013c1c9c7c9d1 --- /dev/null +++ b/config_file/audio2kp.yaml @@ -0,0 +1,3 @@ +embedding_dim: 256 +num_kp: 10 +num_w: 5 \ No newline at end of file diff --git a/config_file/vox-256.yaml b/config_file/vox-256.yaml new file mode 100644 index 0000000000000000000000000000000000000000..60749eb918c296e26bc12fc5264cadea31ce7293 --- /dev/null +++ b/config_file/vox-256.yaml @@ -0,0 +1,83 @@ +dataset_params: + root_dir: /root/wsz/data/vex_image + frame_shape: [256, 256, 3] + id_sampling: True + pairs_list: data/vox256.csv + augmentation_params: + flip_param: + horizontal_flip: True + time_flip: True + jitter_param: + brightness: 0.1 + contrast: 0.1 + saturation: 0.1 + hue: 0.1 + + +model_params: + common_params: + num_kp: 10 + num_channels: 3 + estimate_jacobian: True + kp_detector_params: + temperature: 0.1 + block_expansion: 32 + max_features: 1024 + scale_factor: 0.25 + num_blocks: 5 + generator_params: + block_expansion: 64 + max_features: 512 + num_down_blocks: 2 + num_bottleneck_blocks: 6 + estimate_occlusion_map: True + dense_motion_params: + block_expansion: 64 + max_features: 1024 + num_blocks: 5 + scale_factor: 0.25 + discriminator_params: + scales: [1] + block_expansion: 32 + max_features: 512 + num_blocks: 4 + sn: True + +train_params: + num_epochs: 100 + num_repeats: 50 + epoch_milestones: [5, 20, 30] + lr_generator: 2.0e-4 + lr_discriminator: 2.0e-4 + lr_kp_detector: 2.0e-4 + batch_size: 36 + scales: [1, 0.5, 0.25, 0.125] + checkpoint_freq: 10 + transform_params: + sigma_affine: 0.05 + sigma_tps: 0.005 + points_tps: 5 + loss_weights: + generator_gan: 0 + discriminator_gan: 1 + feature_matching: [10, 10, 10, 10] + perceptual: [10, 10, 10, 10, 10] + equivariance_value: 10 + equivariance_jacobian: 10 + +reconstruction_params: + num_videos: 1000 + format: '.mp4' + +animate_params: + num_pairs: 50 + format: '.mp4' + normalization_params: + adapt_movement_scale: False + use_relative_movement: True + use_relative_jacobian: True + +visualizer_params: + kp_size: 5 + draw_border: True + colormap: 'gist_rainbow' diff --git a/image_preprocess.py b/image_preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..4596c8ba2bd72f13e4f6737e2458f1c18a0bb7bd --- /dev/null +++ b/image_preprocess.py @@ -0,0 +1,57 @@ +import dlib +import cv2 +def compute_aspect_preserved_bbox(bbox, increase_area, h, w): + left, top, right, bot = bbox + width = right - left + height = bot - top + + width_increase = max(increase_area, ((1 + 2 * increase_area) * height - width) / (2 * width)) + height_increase = max(increase_area, ((1 + 2 * increase_area) * width - height) / (2 * height)) + + left_t = int(left - width_increase * width) + top_t = int(top - height_increase * height) + right_t = int(right + width_increase * width) + bot_t = int(bot + height_increase * height) + + left_oob = -min(0, left_t) + right_oob = right - min(right_t, w) + top_oob = -min(0, top_t) + bot_oob = bot - min(bot_t, h) + + if max(left_oob, right_oob, top_oob, bot_oob) > 0: + max_w = max(left_oob, right_oob) + max_h = max(top_oob, bot_oob) + if max_w > max_h: + return left_t + max_w, top_t + max_w, right_t - max_w, bot_t - max_w + else: + return left_t + max_h, top_t + max_h, right_t - max_h, bot_t - max_h + + else: + return (left_t, top_t, right_t, bot_t) + +def crop_src_image(src_img,save_img, detector=None): + if detector is None: + detector = dlib.get_frontal_face_detector() + + img = cv2.imread(src_img) + faces = detector(img, 0) + h, width, _ = img.shape + if len(faces) > 0: + bbox = [faces[0].left(), faces[0].top(),faces[0].right(), faces[0].bottom()] + l = bbox[3]-bbox[1] + bbox[1]= bbox[1]-l*0.1 + bbox[3]= bbox[3]-l*0.1 + bbox[1] = max(0,bbox[1]) + bbox[3] = min(h,bbox[3]) + bbox = compute_aspect_preserved_bbox(tuple(bbox), 0.5, img.shape[0], img.shape[1]) + img = img[bbox[1] :bbox[3] , bbox[0]:bbox[2]] + img = cv2.resize(img, (256, 256)) + cv2.imwrite(save_img,img) + else: + img = cv2.resize(img,(256,256)) + cv2.imwrite(save_img, img) + +if __name__ == '__main__': + src_img = "" + out_img = "" + crop_src_image(src_img,out_img) \ No newline at end of file diff --git a/models/audio2pose.py b/models/audio2pose.py new file mode 100644 index 0000000000000000000000000000000000000000..c93ccef67636e748c56c0990e4e3e3aedcf1d577 --- /dev/null +++ b/models/audio2pose.py @@ -0,0 +1,36 @@ +import torch.nn as nn +import torch +from models.util import MyResNet34 + +class audio2poseLSTM(nn.Module): + def __init__(self): + super(audio2poseLSTM,self).__init__() + + self.em_pose = MyResNet34(256, 1) + self.em_audio = MyResNet34(256, 1) + self.lstm = nn.LSTM(512,256,num_layers=2,bias=True,batch_first=True) + + self.output = nn.Linear(256,6) + + + def forward(self,x): + pose_em = self.em_pose(x["img"]) + bs = pose_em.shape[0] + zero_state = torch.zeros((2, bs, 256), requires_grad=True).to(pose_em.device) + cur_state = (zero_state, zero_state) + img_em = pose_em + bs,seqlen,num,dims = x["audio"].shape + + audio = x["audio"].reshape(-1, 1, num, dims) + audio_em = self.em_audio(audio).reshape(bs, seqlen, 256) + + result = [self.output(img_em).unsqueeze(1)] + + for i in range(seqlen): + + img_em,cur_state = self.lstm(torch.cat((audio_em[:,i:i+1],img_em.unsqueeze(1)),dim=2),cur_state) + img_em = img_em.reshape(-1, 256) + + result.append(self.output(img_em).unsqueeze(1)) + res = torch.cat(result,dim=1) + return res diff --git a/models/dense_motion.py b/models/dense_motion.py new file mode 100644 index 0000000000000000000000000000000000000000..65a946bce0839789fa47c9d7fcd387fef6b57f9a --- /dev/null +++ b/models/dense_motion.py @@ -0,0 +1,114 @@ +from torch import nn +import torch.nn.functional as F +import torch +from models.util import Hourglass, AntiAliasInterpolation2d, make_coordinate_grid, kp2gaussian + + +class DenseMotionNetwork(nn.Module): + """ + Module that predicting a dense motion from sparse motion representation given by kp_source and kp_driving + """ + + def __init__(self, block_expansion, num_blocks, max_features, num_kp, num_channels, estimate_occlusion_map=False, + scale_factor=1, kp_variance=0.01): + super(DenseMotionNetwork, self).__init__() + self.hourglass = Hourglass(block_expansion=block_expansion, in_features=(num_kp + 1) * (num_channels + 1), + max_features=max_features, num_blocks=num_blocks) + + self.mask = nn.Conv2d(self.hourglass.out_filters, num_kp + 1, kernel_size=(7, 7), padding=(3, 3)) + + if estimate_occlusion_map: + self.occlusion = nn.Conv2d(self.hourglass.out_filters, 1, kernel_size=(7, 7), padding=(3, 3)) + else: + self.occlusion = None + + self.num_kp = num_kp + self.scale_factor = scale_factor + self.kp_variance = kp_variance + + if self.scale_factor != 1: + self.down = AntiAliasInterpolation2d(num_channels, self.scale_factor) + + def create_heatmap_representations(self, source_image, kp_driving, kp_source): + """ + Eq 6. in the paper H_k(z) + """ + spatial_size = source_image.shape[2:] + gaussian_driving = kp2gaussian(kp_driving, spatial_size=spatial_size, kp_variance=self.kp_variance) + gaussian_source = kp2gaussian(kp_source, spatial_size=spatial_size, kp_variance=self.kp_variance) + heatmap = gaussian_driving - gaussian_source + + #adding background feature + zeros = torch.zeros(heatmap.shape[0], 1, spatial_size[0], spatial_size[1]).type(heatmap.type()) + heatmap = torch.cat([zeros, heatmap], dim=1) + heatmap = heatmap.unsqueeze(2) + return heatmap + + def create_sparse_motions(self, source_image, kp_driving, kp_source): + """ + Eq 4. in the paper T_{s<-d}(z) + """ + bs, _, h, w = source_image.shape + identity_grid = make_coordinate_grid((h, w), type=kp_source['value'].type()) + identity_grid = identity_grid.view(1, 1, h, w, 2) + coordinate_grid = identity_grid - kp_driving['value'].view(bs, self.num_kp, 1, 1, 2) + if 'jacobian' in kp_driving: + jacobian = torch.matmul(kp_source['jacobian'], torch.inverse(kp_driving['jacobian'])) + jacobian = jacobian.unsqueeze(-3).unsqueeze(-3) + jacobian = jacobian.repeat(1, 1, h, w, 1, 1) + coordinate_grid = torch.matmul(jacobian, coordinate_grid.unsqueeze(-1)) + coordinate_grid = coordinate_grid.squeeze(-1) + + driving_to_source = coordinate_grid + kp_source['value'].view(bs, self.num_kp, 1, 1, 2) + + #adding background feature + identity_grid = identity_grid.repeat(bs, 1, 1, 1, 1) + sparse_motions = torch.cat([identity_grid, driving_to_source], dim=1) + return sparse_motions + + def create_deformed_source_image(self, source_image, sparse_motions): + """ + Eq 7. in the paper \hat{T}_{s<-d}(z) + """ + bs, _, h, w = source_image.shape + source_repeat = source_image.unsqueeze(1).unsqueeze(1).repeat(1, self.num_kp + 1, 1, 1, 1, 1) + source_repeat = source_repeat.view(bs * (self.num_kp + 1), -1, h, w) + sparse_motions = sparse_motions.view((bs * (self.num_kp + 1), h, w, -1)) + sparse_deformed = F.grid_sample(source_repeat, sparse_motions) + # sparse_deformed = F.grid_sample(source_repeat, sparse_motions,align_corners = False) + sparse_deformed = sparse_deformed.view((bs, self.num_kp + 1, -1, h, w)) + return sparse_deformed + + def forward(self, source_image, kp_driving, kp_source): + if self.scale_factor != 1: + source_image = self.down(source_image) + + bs, _, h, w = source_image.shape + + out_dict = dict() + heatmap_representation = self.create_heatmap_representations(source_image, kp_driving, kp_source)#bs*(numkp+1)*1*h*w + sparse_motion = self.create_sparse_motions(source_image, kp_driving, kp_source)#bs*(numkp+1)*h*w*2 + deformed_source = self.create_deformed_source_image(source_image, sparse_motion) + out_dict['sparse_deformed'] = deformed_source + + input = torch.cat([heatmap_representation, deformed_source], dim=2)#bs*num+1*4*w*h + input = input.view(bs, -1, h, w) + + prediction = self.hourglass(input) + + mask = self.mask(prediction) + mask = F.softmax(mask, dim=1) + out_dict['mask'] = mask + mask = mask.unsqueeze(2)#bs*numkp+1*1*h*w + sparse_motion = sparse_motion.permute(0, 1, 4, 2, 3) + deformation = (sparse_motion * mask).sum(dim=1)# bs,2,64,64 + deformation = deformation.permute(0, 2, 3, 1)#bs*h*w*2 + + out_dict['deformation'] = deformation + + # Sec. 3.2 in the paper + if self.occlusion: + occlusion_map = torch.sigmoid(self.occlusion(prediction)) + out_dict['occlusion_map'] = occlusion_map + + return out_dict diff --git a/models/generator.py b/models/generator.py new file mode 100644 index 0000000000000000000000000000000000000000..17494e431f21d6d2abac33a33f4c82e00fbb5a78 --- /dev/null +++ b/models/generator.py @@ -0,0 +1,99 @@ +import torch +from torch import nn +import torch.nn.functional as F +from models.util import ResBlock2d, SameBlock2d, UpBlock2d, DownBlock2d +from models.dense_motion import DenseMotionNetwork + + +class OcclusionAwareGenerator(nn.Module): + """ + Generator that given source image and and keypoints try to transform image according to movement trajectories + induced by keypoints. Generator follows Johnson architecture. + """ + + def __init__(self, num_channels, num_kp, block_expansion, max_features, num_down_blocks, + num_bottleneck_blocks, estimate_occlusion_map=False, dense_motion_params=None, estimate_jacobian=False): + super(OcclusionAwareGenerator, self).__init__() + + if dense_motion_params is not None: + self.dense_motion_network = DenseMotionNetwork(num_kp=num_kp, num_channels=num_channels, + estimate_occlusion_map=estimate_occlusion_map, + **dense_motion_params) + else: + self.dense_motion_network = None + + self.first = SameBlock2d(num_channels, block_expansion, kernel_size=(7, 7), padding=(3, 3)) + + down_blocks = [] + for i in range(num_down_blocks): + in_features = min(max_features, block_expansion * (2 ** i)) + out_features = min(max_features, block_expansion * (2 ** (i + 1))) + down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1))) + self.down_blocks = nn.ModuleList(down_blocks) + + up_blocks = [] + for i in range(num_down_blocks): + in_features = min(max_features, block_expansion * (2 ** (num_down_blocks - i))) + out_features = min(max_features, block_expansion * (2 ** (num_down_blocks - i - 1))) + up_blocks.append(UpBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1))) + self.up_blocks = nn.ModuleList(up_blocks) + + self.bottleneck = torch.nn.Sequential() + in_features = min(max_features, block_expansion * (2 ** num_down_blocks)) + for i in range(num_bottleneck_blocks): + self.bottleneck.add_module('r' + str(i), ResBlock2d(in_features, kernel_size=(3, 3), padding=(1, 1))) + + self.final = nn.Conv2d(block_expansion, num_channels, kernel_size=(7, 7), padding=(3, 3)) + self.estimate_occlusion_map = estimate_occlusion_map + self.num_channels = num_channels + + def deform_input(self, inp, deformation): + _, h_old, w_old, _ = deformation.shape + _, _, h, w = inp.shape + if h_old != h or w_old != w: + deformation = deformation.permute(0, 3, 1, 2) + deformation = F.interpolate(deformation, size=(h, w), mode='bilinear') + deformation = deformation.permute(0, 2, 3, 1) + return F.grid_sample(inp, deformation) + # return F.grid_sample(inp, deformation,align_corners = False) + + def forward(self, source_image, kp_driving, kp_source): + # Encoding (downsampling) part + out = self.first(source_image) + for i in range(len(self.down_blocks)): + out = self.down_blocks[i](out) + + # Transforming feature representation according to deformation and occlusion + output_dict = {} + if self.dense_motion_network is not None: + dense_motion = self.dense_motion_network(source_image=source_image, kp_driving=kp_driving, + kp_source=kp_source) + output_dict['mask'] = dense_motion['mask'] + output_dict['sparse_deformed'] = dense_motion['sparse_deformed'] + output_dict['deformation'] = dense_motion['deformation'] + + if 'occlusion_map' in dense_motion: + occlusion_map = dense_motion['occlusion_map'] + output_dict['occlusion_map'] = occlusion_map + else: + occlusion_map = None + deformation = dense_motion['deformation'] + out = self.deform_input(out, deformation) + + if occlusion_map is not None: + if out.shape[2] != occlusion_map.shape[2] or out.shape[3] != occlusion_map.shape[3]: + occlusion_map = F.interpolate(occlusion_map, size=out.shape[2:], mode='bilinear') + out = out * occlusion_map + + output_dict["deformed"] = self.deform_input(source_image, deformation) + + # Decoding part + out = self.bottleneck(out) + for i in range(len(self.up_blocks)): + out = self.up_blocks[i](out) + out = self.final(out) + out = F.sigmoid(out) + + output_dict["prediction"] = out + + return output_dict diff --git a/models/keypoint_detector.py b/models/keypoint_detector.py new file mode 100644 index 0000000000000000000000000000000000000000..0eafc171ba001e76a3a7d56cbbf46f8907366e1f --- /dev/null +++ b/models/keypoint_detector.py @@ -0,0 +1,80 @@ +from torch import nn +import torch +import torch.nn.functional as F +from models.util import Hourglass, make_coordinate_grid, AntiAliasInterpolation2d + + + +class KPDetector(nn.Module): + """ + Detecting a keypoints. Return keypoint position and jacobian near each keypoint. + """ + + def __init__(self, block_expansion, num_kp, num_channels, max_features, + num_blocks, temperature, estimate_jacobian=False, scale_factor=1, + single_jacobian_map=False, pad=0): + super(KPDetector, self).__init__() + + self.predictor = Hourglass(block_expansion, in_features=num_channels, + max_features=max_features, num_blocks=num_blocks) + + self.kp = nn.Conv2d(in_channels=self.predictor.out_filters, out_channels=num_kp, kernel_size=(7, 7), + padding=pad) + + if estimate_jacobian: + self.num_jacobian_maps = 1 if single_jacobian_map else num_kp + self.jacobian = nn.Conv2d(in_channels=self.predictor.out_filters, + out_channels=4 * self.num_jacobian_maps, kernel_size=(7, 7), padding=pad) + self.jacobian.weight.data.zero_() + self.jacobian.bias.data.copy_(torch.tensor([1, 0, 0, 1] * self.num_jacobian_maps, dtype=torch.float)) + else: + self.jacobian = None + + self.temperature = temperature + self.scale_factor = scale_factor + if self.scale_factor != 1: + self.down = AntiAliasInterpolation2d(num_channels, self.scale_factor) + + def gaussian2kp(self, heatmap): + """ + Extract the mean and from a heatmap + """ + shape = heatmap.shape + heatmap = heatmap.unsqueeze(-1) + grid = make_coordinate_grid(shape[2:], heatmap.type()).unsqueeze_(0).unsqueeze_(0) + value = (heatmap * grid).sum(dim=(2, 3)) + kp = {'value': value} + + return kp + + def forward(self, x,with_feature = False): + if self.scale_factor != 1: + x = self.down(x) + + feature_map = self.predictor(x) + prediction = self.kp(feature_map) + final_shape = prediction.shape + heatmap = prediction.view(final_shape[0], final_shape[1], -1) + heatmap = F.softmax(heatmap / self.temperature, dim=2) + heatmap = heatmap.view(*final_shape) + + out = self.gaussian2kp(heatmap) + + if self.jacobian is not None: + jacobian_map = self.jacobian(feature_map) + out["jacobian_map"] = jacobian_map + + jacobian_map = jacobian_map.reshape(final_shape[0], self.num_jacobian_maps, 4, final_shape[2], + final_shape[3]) + + heatmap = heatmap.unsqueeze(2) + + jacobian = heatmap * jacobian_map + jacobian = jacobian.view(final_shape[0], final_shape[1], 4, -1) + jacobian = jacobian.sum(dim=-1) + jacobian = jacobian.view(jacobian.shape[0], jacobian.shape[1], 2, 2) + out['jacobian'] = jacobian + out["pred_feature"] = prediction + if with_feature: + out["feature_map"] = feature_map + return out diff --git a/models/layers/__init__.py b/models/layers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a2345d6adc3981a784e9c2d78e44374831f8e9cb --- /dev/null +++ b/models/layers/__init__.py @@ -0,0 +1,19 @@ +# Copyright (C) 2020 NVIDIA Corporation. All rights reserved. +# +# This work is made available under the Nvidia Source Code License-NC. +# To view a copy of this license, check out LICENSE.md +from .conv import LinearBlock, Conv1dBlock, Conv2dBlock, Conv3dBlock, \ + HyperConv2dBlock, MultiOutConv2dBlock, \ + PartialConv2dBlock, PartialConv3dBlock +from .residual import ResLinearBlock, Res1dBlock, Res2dBlock, Res3dBlock, \ + HyperRes2dBlock, MultiOutRes2dBlock, UpRes2dBlock, DownRes2dBlock, \ + PartialRes2dBlock, PartialRes3dBlock +# from .non_local import NonLocal2dBlock + +__all__ = ['Conv1dBlock', 'Conv2dBlock', 'Conv3dBlock', 'LinearBlock', + 'HyperConv2dBlock', 'MultiOutConv2dBlock', + 'PartialConv2dBlock', 'PartialConv3dBlock', + 'Res1dBlock', 'Res2dBlock', 'Res3dBlock', + 'UpRes2dBlock', 'DownRes2dBlock', + 'ResLinearBlock', 'HyperRes2dBlock', 'MultiOutRes2dBlock', + 'PartialRes2dBlock', 'PartialRes3dBlock',] diff --git a/models/layers/activation_norm.py b/models/layers/activation_norm.py new file mode 100644 index 0000000000000000000000000000000000000000..ce2f194a53d4f5f3ec172a2bcb1f2d38cd5a1db0 --- /dev/null +++ b/models/layers/activation_norm.py @@ -0,0 +1,420 @@ +from types import SimpleNamespace + +import torch + + +try: + # from torch.nn import BatchNorm2d as SyncBatchNorm + from torch.nn import SyncBatchNorm +except ImportError: + from torch.nn import BatchNorm2d as SyncBatchNorm +from torch import nn +from torch.nn import functional as F +from .conv import LinearBlock, Conv2dBlock, HyperConv2d, PartialConv2dBlock +from .misc import PartialSequential +import sync_batchnorm + + +class AdaptiveNorm(nn.Module): + r"""Adaptive normalization layer. The layer first normalizes the input, then + performs an affine transformation using parameters computed from the + conditional inputs. + Args: + num_features (int): Number of channels in the input tensor. + cond_dims (int): Number of channels in the conditional inputs. + weight_norm_type (str): Type of weight normalization. + ``'none'``, ``'spectral'``, ``'weight'``, or ``'weight_demod'``. + projection (bool): If ``True``, project the conditional input to gamma + and beta using a fully connected layer, otherwise directly use + the conditional input as gamma and beta. + separate_projection (bool): If ``True``, we will use two different + layers for gamma and beta. Otherwise, we will use one layer. It + matters only if you apply any weight norms to this layer. + input_dim (int): Number of dimensions of the input tensor. + activation_norm_type (str): + Type of activation normalization. + ``'none'``, ``'instance'``, ``'batch'``, ``'sync_batch'``, + ``'layer'``, ``'layer_2d'``, ``'group'``, ``'adaptive'``, + ``'spatially_adaptive'`` or ``'hyper_spatially_adaptive'``. + activation_norm_params (obj, optional, default=None): + Parameters of activation normalization. + If not ``None``, ``activation_norm_params.__dict__`` will be used as + keyword arguments when initializing activation normalization. + """ + + def __init__(self, num_features, cond_dims, weight_norm_type='', + projection=True, + separate_projection=False, + input_dim=2, + activation_norm_type='instance', + activation_norm_params=None): + super().__init__() + self.projection = projection + self.separate_projection = separate_projection + if activation_norm_params is None: + activation_norm_params = SimpleNamespace(affine=False) + self.norm = get_activation_norm_layer(num_features, + activation_norm_type, + input_dim, + **vars(activation_norm_params)) + if self.projection: + if self.separate_projection: + self.fc_gamma = \ + LinearBlock(cond_dims, num_features, + weight_norm_type=weight_norm_type) + self.fc_beta = \ + LinearBlock(cond_dims, num_features, + weight_norm_type=weight_norm_type) + else: + self.fc = LinearBlock(cond_dims, num_features * 2, + weight_norm_type=weight_norm_type) + + self.conditional = True + + def forward(self, x, y, **kwargs): + r"""Adaptive Normalization forward. + Args: + x (N x C1 x * tensor): Input tensor. + y (N x C2 tensor): Conditional information. + Returns: + out (N x C1 x * tensor): Output tensor. + """ + if self.projection: + if self.separate_projection: + gamma = self.fc_gamma(y) + beta = self.fc_beta(y) + for _ in range(x.dim() - gamma.dim()): + gamma = gamma.unsqueeze(-1) + beta = beta.unsqueeze(-1) + else: + y = self.fc(y) + for _ in range(x.dim() - y.dim()): + y = y.unsqueeze(-1) + gamma, beta = y.chunk(2, 1) + else: + for _ in range(x.dim() - y.dim()): + y = y.unsqueeze(-1) + gamma, beta = y.chunk(2, 1) + x = self.norm(x) if self.norm is not None else x + out = x * (1 + gamma) + beta + return out + + +class SpatiallyAdaptiveNorm(nn.Module): + r"""Spatially Adaptive Normalization (SPADE) initialization. + Args: + num_features (int) : Number of channels in the input tensor. + cond_dims (int or list of int) : List of numbers of channels + in the input. + num_filters (int): Number of filters in SPADE. + kernel_size (int): Kernel size of the convolutional filters in + the SPADE layer. + weight_norm_type (str): Type of weight normalization. + ``'none'``, ``'spectral'``, or ``'weight'``. + separate_projection (bool): If ``True``, we will use two different + layers for gamma and beta. Otherwise, we will use one layer. It + matters only if you apply any weight norms to this layer. + activation_norm_type (str): + Type of activation normalization. + ``'none'``, ``'instance'``, ``'batch'``, ``'sync_batch'``, + ``'layer'``, ``'layer_2d'``, ``'group'``. + activation_norm_params (obj, optional, default=None): + Parameters of activation normalization. + If not ``None``, ``activation_norm_params.__dict__`` will be used as + keyword arguments when initializing activation normalization. + """ + + def __init__(self, + num_features, + cond_dims, + num_filters=128, + kernel_size=3, + weight_norm_type='', + separate_projection=False, + activation_norm_type='sync_batch', + activation_norm_params=None, + partial=False): + super().__init__() + if activation_norm_params is None: + activation_norm_params = SimpleNamespace(affine=False) + padding = kernel_size // 2 + self.separate_projection = separate_projection + self.mlps = nn.ModuleList() + self.gammas = nn.ModuleList() + self.betas = nn.ModuleList() + + # Make cond_dims a list. + if type(cond_dims) != list: + cond_dims = [cond_dims] + + # Make num_filters a list. + if not isinstance(num_filters, list): + num_filters = [num_filters] * len(cond_dims) + else: + assert len(num_filters) >= len(cond_dims) + + # Make partial a list. + if not isinstance(partial, list): + partial = [partial] * len(cond_dims) + else: + assert len(partial) >= len(cond_dims) + + for i, cond_dim in enumerate(cond_dims): + mlp = [] + conv_block = PartialConv2dBlock if partial[i] else Conv2dBlock + sequential = PartialSequential if partial[i] else nn.Sequential + + if num_filters[i] > 0: + mlp += [conv_block(cond_dim, + num_filters[i], + kernel_size, + padding=padding, + weight_norm_type=weight_norm_type, + nonlinearity='relu')] + mlp_ch = cond_dim if num_filters[i] == 0 else num_filters[i] + + if self.separate_projection: + if partial[i]: + raise NotImplementedError( + 'Separate projection not yet implemented for ' + + 'partial conv') + self.mlps.append(nn.Sequential(*mlp)) + self.gammas.append( + conv_block(mlp_ch, num_features, + kernel_size, + padding=padding, + weight_norm_type=weight_norm_type)) + self.betas.append( + conv_block(mlp_ch, num_features, + kernel_size, + padding=padding, + weight_norm_type=weight_norm_type)) + else: + mlp += [conv_block(mlp_ch, num_features * 2, kernel_size, + padding=padding, + weight_norm_type=weight_norm_type)] + self.mlps.append(sequential(*mlp)) + + self.norm = get_activation_norm_layer(num_features, + activation_norm_type, + 2, + **vars(activation_norm_params)) + self.conditional = True + + def forward(self, x, *cond_inputs, **kwargs): + r"""Spatially Adaptive Normalization (SPADE) forward. + Args: + x (N x C1 x H x W tensor) : Input tensor. + cond_inputs (list of tensors) : Conditional maps for SPADE. + Returns: + output (4D tensor) : Output tensor. + """ + output = self.norm(x) if self.norm is not None else x + for i in range(len(cond_inputs)): + if cond_inputs[i] is None: + continue + label_map = F.interpolate(cond_inputs[i], size=x.size()[2:], + mode='nearest') + if self.separate_projection: + hidden = self.mlps[i](label_map) + gamma = self.gammas[i](hidden) + beta = self.betas[i](hidden) + else: + affine_params = self.mlps[i](label_map) + gamma, beta = affine_params.chunk(2, dim=1) + output = output * (1 + gamma) + beta + return output + + +class HyperSpatiallyAdaptiveNorm(nn.Module): + r"""Spatially Adaptive Normalization (SPADE) initialization. + Args: + num_features (int) : Number of channels in the input tensor. + cond_dims (int or list of int) : List of numbers of channels + in the conditional input. + num_filters (int): Number of filters in SPADE. + kernel_size (int): Kernel size of the convolutional filters in + the SPADE layer. + weight_norm_type (str): Type of weight normalization. + ``'none'``, ``'spectral'``, or ``'weight'``. + activation_norm_type (str): + Type of activation normalization. + ``'none'``, ``'instance'``, ``'batch'``, ``'sync_batch'``, + ``'layer'``, ``'layer_2d'``, ``'group'``. + is_hyper (bool): Whether to use hyper SPADE. + """ + + def __init__(self, num_features, cond_dims, + num_filters=0, kernel_size=3, + weight_norm_type='', + activation_norm_type='sync_batch', is_hyper=True): + super().__init__() + padding = kernel_size // 2 + self.mlps = nn.ModuleList() + if type(cond_dims) != list: + cond_dims = [cond_dims] + + for i, cond_dim in enumerate(cond_dims): + mlp = [] + if not is_hyper or (i != 0): + if num_filters > 0: + mlp += [Conv2dBlock(cond_dim, num_filters, kernel_size, + padding=padding, + weight_norm_type=weight_norm_type, + nonlinearity='relu')] + mlp_ch = cond_dim if num_filters == 0 else num_filters + mlp += [Conv2dBlock(mlp_ch, num_features * 2, kernel_size, + padding=padding, + weight_norm_type=weight_norm_type)] + mlp = nn.Sequential(*mlp) + else: + if num_filters > 0: + raise ValueError('Multi hyper layer not supported yet.') + mlp = HyperConv2d(padding=padding) + self.mlps.append(mlp) + + self.norm = get_activation_norm_layer(num_features, + activation_norm_type, + 2, + affine=False) + + self.conditional = True + + def forward(self, x, *cond_inputs, + norm_weights=(None, None), **kwargs): + r"""Spatially Adaptive Normalization (SPADE) forward. + Args: + x (4D tensor) : Input tensor. + cond_inputs (list of tensors) : Conditional maps for SPADE. + norm_weights (5D tensor or list of tensors): conv weights or + [weights, biases]. + Returns: + output (4D tensor) : Output tensor. + """ + output = self.norm(x) + for i in range(len(cond_inputs)): + if cond_inputs[i] is None: + continue + if type(cond_inputs[i]) == list: + cond_input, mask = cond_inputs[i] + mask = F.interpolate(mask, size=x.size()[2:], mode='bilinear', + align_corners=False) + else: + cond_input = cond_inputs[i] + mask = None + label_map = F.interpolate(cond_input, size=x.size()[2:]) + if norm_weights is None or norm_weights[0] is None or i != 0: + affine_params = self.mlps[i](label_map) + else: + affine_params = self.mlps[i](label_map, + conv_weights=norm_weights) + gamma, beta = affine_params.chunk(2, dim=1) + if mask is not None: + gamma = gamma * (1 - mask) + beta = beta * (1 - mask) + output = output * (1 + gamma) + beta + return output + + +class LayerNorm2d(nn.Module): + r"""Layer Normalization as introduced in + https://arxiv.org/abs/1607.06450. + This is the usual way to apply layer normalization in CNNs. + Note that unlike the pytorch implementation which applies per-element + scale and bias, here it applies per-channel scale and bias, similar to + batch/instance normalization. + Args: + num_features (int): Number of channels in the input tensor. + eps (float, optional, default=1e-5): a value added to the + denominator for numerical stability. + affine (bool, optional, default=False): If ``True``, performs + affine transformation after normalization. + """ + + def __init__(self, num_features, eps=1e-5, affine=True): + super(LayerNorm2d, self).__init__() + self.num_features = num_features + self.affine = affine + self.eps = eps + + if self.affine: + self.gamma = nn.Parameter(torch.Tensor(num_features).uniform_()) + self.beta = nn.Parameter(torch.zeros(num_features)) + + def forward(self, x): + r""" + Args: + x (tensor): Input tensor. + """ + shape = [-1] + [1] * (x.dim() - 1) + if x.size(0) == 1: + mean = x.view(-1).mean().view(*shape) + std = x.view(-1).std().view(*shape) + else: + mean = x.view(x.size(0), -1).mean(1).view(*shape) + std = x.view(x.size(0), -1).std(1).view(*shape) + + x = (x - mean) / (std + self.eps) + + if self.affine: + shape = [1, -1] + [1] * (x.dim() - 2) + x = x * self.gamma.view(*shape) + self.beta.view(*shape) + return x + + +def get_activation_norm_layer(num_features, norm_type, + input_dim, **norm_params): + r"""Return an activation normalization layer. + Args: + num_features (int): Number of feature channels. + norm_type (str): + Type of activation normalization. + ``'none'``, ``'instance'``, ``'batch'``, ``'sync_batch'``, + ``'layer'``, ``'layer_2d'``, ``'group'``, ``'adaptive'``, + ``'spatially_adaptive'`` or ``'hyper_spatially_adaptive'``. + input_dim (int): Number of input dimensions. + norm_params: Arbitrary keyword arguments that will be used to + initialize the activation normalization. + """ + input_dim = max(input_dim, 1) # Norm1d works with both 0d and 1d inputs + + if norm_type == 'none' or norm_type == '': + norm_layer = None + elif norm_type == 'batch': + # norm = getattr(nn, 'BatchNorm%dd' % input_dim) + norm = getattr(sync_batchnorm, 'SynchronizedBatchNorm%dd' % input_dim) + norm_layer = norm(num_features, **norm_params) + elif norm_type == 'instance': + affine = norm_params.pop('affine', True) # Use affine=True by default + norm = getattr(nn, 'InstanceNorm%dd' % input_dim) + norm_layer = norm(num_features, affine=affine, **norm_params) + elif norm_type == 'sync_batch': + # There is a bug of using amp O1 with synchronize batch norm. + # The lines below fix it. + affine = norm_params.pop('affine', True) + # Always call SyncBN with affine=True + norm_layer = SyncBatchNorm(num_features, affine=True, **norm_params) + norm_layer.weight.requires_grad = affine + norm_layer.bias.requires_grad = affine + elif norm_type == 'layer': + norm_layer = nn.LayerNorm(num_features, **norm_params) + elif norm_type == 'layer_2d': + norm_layer = LayerNorm2d(num_features, **norm_params) + elif norm_type == 'group': + norm_layer = nn.GroupNorm(num_channels=num_features, **norm_params) + elif norm_type == 'adaptive': + norm_layer = AdaptiveNorm(num_features, **norm_params) + elif norm_type == 'spatially_adaptive': + if input_dim != 2: + raise ValueError('Spatially adaptive normalization layers ' + 'only supports 2D input') + norm_layer = SpatiallyAdaptiveNorm(num_features, **norm_params) + elif norm_type == 'hyper_spatially_adaptive': + if input_dim != 2: + raise ValueError('Spatially adaptive normalization layers ' + 'only supports 2D input') + norm_layer = HyperSpatiallyAdaptiveNorm(num_features, **norm_params) + else: + raise ValueError('Activation norm layer %s ' + 'is not recognized' % norm_type) + return norm_layer \ No newline at end of file diff --git a/models/layers/conv.py b/models/layers/conv.py new file mode 100644 index 0000000000000000000000000000000000000000..59aa38006707a67f7f2a6c400a0c7a2703487ce7 --- /dev/null +++ b/models/layers/conv.py @@ -0,0 +1,1073 @@ +# Copyright (C) 2020 NVIDIA Corporation. All rights reserved. +# +# This work is made available under the Nvidia Source Code License-NC. +# To view a copy of this license, check out LICENSE.md +from types import SimpleNamespace + +import torch +from torch import nn +from torch.nn import functional as F + +from .misc import ApplyNoise + + +class _BaseConvBlock(nn.Module): + r"""An abstract wrapper class that wraps a torch convolution or linear layer + with normalization and nonlinearity. + """ + + def __init__(self, in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + nonlinearity, inplace_nonlinearity, + apply_noise, order, input_dim): + super().__init__() + from .nonlinearity import get_nonlinearity_layer + from .weight_norm import get_weight_norm_layer + from .activation_norm import get_activation_norm_layer + self.weight_norm_type = weight_norm_type + + # Convolutional layer. + if weight_norm_params is None: + weight_norm_params = SimpleNamespace() + weight_norm = get_weight_norm_layer( + weight_norm_type, **vars(weight_norm_params)) + conv_layer = weight_norm(self._get_conv_layer( + in_channels, out_channels, kernel_size, stride, padding, dilation, + groups, bias, padding_mode, input_dim)) + + # Noise injection layer. + noise_layer = ApplyNoise() if apply_noise else None + + # Normalization layer. + conv_before_norm = order.find('C') < order.find('N') + norm_channels = out_channels if conv_before_norm else in_channels + if activation_norm_params is None: + activation_norm_params = SimpleNamespace() + activation_norm_layer = get_activation_norm_layer( + norm_channels, + activation_norm_type, + input_dim, + **vars(activation_norm_params)) + + # Nonlinearity layer. + nonlinearity_layer = get_nonlinearity_layer( + nonlinearity, inplace=inplace_nonlinearity) + + # Mapping from operation names to layers. + mappings = {'C': {'conv': conv_layer}, + 'N': {'norm': activation_norm_layer}, + 'A': {'nonlinearity': nonlinearity_layer}} + + # All layers in order. + self.layers = nn.ModuleDict() + for op in order: + if list(mappings[op].values())[0] is not None: + self.layers.update(mappings[op]) + if op == 'C' and noise_layer is not None: + # Inject noise after convolution. + self.layers.update({'noise': noise_layer}) + + # Whether this block expects conditional inputs. + self.conditional = \ + getattr(conv_layer, 'conditional', False) or \ + getattr(activation_norm_layer, 'conditional', False) + + def forward(self, x, *cond_inputs, **kw_cond_inputs): + r""" + + Args: + x (tensor): Input tensor. + cond_inputs (list of tensors) : Conditional input tensors. + kw_cond_inputs (dict) : Keyword conditional inputs. + """ + for layer in self.layers.values(): + if getattr(layer, 'conditional', False): + # Layers that require conditional inputs. + x = layer(x, *cond_inputs, **kw_cond_inputs) + else: + x = layer(x) + return x + + def _get_conv_layer(self, in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, padding_mode, + input_dim): + # Returns the convolutional layer. + if input_dim == 0: + layer = nn.Linear(in_channels, out_channels, bias) + else: + layer_type = getattr(nn, 'Conv%dd' % input_dim) + + layer = layer_type( + in_channels, out_channels, kernel_size, stride, padding, + dilation, groups, bias) + return layer + + def __repr__(self): + main_str = self._get_name() + '(' + child_lines = [] + for name, layer in self.layers.items(): + mod_str = repr(layer) + if name == 'conv' and self.weight_norm_type != 'none' and \ + self.weight_norm_type != '': + mod_str = mod_str[:-1] + \ + ', weight_norm={}'.format(self.weight_norm_type) + ')' + mod_str = self._addindent(mod_str, 2) + child_lines.append(mod_str) + if len(child_lines) == 1: + main_str += child_lines[0] + else: + main_str += '\n ' + '\n '.join(child_lines) + '\n' + + main_str += ')' + return main_str + + @staticmethod + def _addindent(s_, numSpaces): + s = s_.split('\n') + # don't do anything for single-line stuff + if len(s) == 1: + return s_ + first = s.pop(0) + s = [(numSpaces * ' ') + line for line in s] + s = '\n'.join(s) + s = first + '\n' + s + return s + + +class LinearBlock(_BaseConvBlock): + r"""A Wrapper class that wraps ``torch.nn.Linear`` with normalization and + nonlinearity. + + Args: + in_features (int): Number of channels in the input tensor. + out_features (int): Number of channels in the output tensor. + bias (bool, optional, default=True): + If ``True``, adds a learnable bias to the output. + weight_norm_type (str, optional, default='none'): + Type of weight normalization. + ``'none'``, ``'spectral'``, ``'weight'`` + or ``'weight_demod'``. + weight_norm_params (obj, optional, default=None): + Parameters of weight normalization. + If not ``None``, ``weight_norm_params.__dict__`` will be used as + keyword arguments when initializing weight normalization. + activation_norm_type (str, optional, default='none'): + Type of activation normalization. + ``'none'``, ``'instance'``, ``'batch'``, ``'sync_batch'``, + ``'layer'``, ``'layer_2d'``, ``'group'``, ``'adaptive'``, + ``'spatially_adaptive'`` or ``'hyper_spatially_adaptive'``. + activation_norm_params (obj, optional, default=None): + Parameters of activation normalization. + If not ``None``, ``activation_norm_params.__dict__`` will be used as + keyword arguments when initializing activation normalization. + nonlinearity (str, optional, default='none'): + Type of nonlinear activation function. + ``'none'``, ``'relu'``, ``'leakyrelu'``, ``'prelu'``, + ``'tanh'`` , ``'sigmoid'`` or ``'softmax'``. + inplace_nonlinearity (bool, optional, default=False): If ``True``, + set ``inplace=True`` when initializing the nonlinearity layer. + apply_noise (bool, optional, default=False): If ``True``, add + Gaussian noise with learnable magnitude after the + fully-connected layer. + order (str, optional, default='CNA'): Order of operations. + ``'C'``: fully-connected, + ``'N'``: normalization, + ``'A'``: nonlinear activation. + For example, a block initialized with ``order='CNA'`` will + do convolution first, then normalization, then nonlinearity. + """ + + def __init__(self, in_features, out_features, bias=True, + weight_norm_type='none', weight_norm_params=None, + activation_norm_type='none', activation_norm_params=None, + nonlinearity='none', inplace_nonlinearity=False, + apply_noise=False, order='CNA'): + super().__init__(in_features, out_features, None, None, + None, None, None, bias, + None, weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + nonlinearity, inplace_nonlinearity, apply_noise, + order, 0) + + +class Conv1dBlock(_BaseConvBlock): + r"""A Wrapper class that wraps ``torch.nn.Conv1d`` with normalization and + nonlinearity. + + Args: + in_channels (int): Number of channels in the input tensor. + out_channels (int): Number of channels in the output tensor. + kernel_size (int or tuple): Size of the convolving kernel. + stride (int or tuple, optional, default=1): + Stride of the convolution. + padding (int or tuple, optional, default=0): + Zero-padding added to both sides of the input. + dilation (int or tuple, optional, default=1): + Spacing between kernel elements. + groups (int, optional, default=1): Number of blocked connections + from input channels to output channels. + bias (bool, optional, default=True): + If ``True``, adds a learnable bias to the output. + padding_mode (string, optional, default='zeros'): Type of padding: + ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. + weight_norm_type (str, optional, default='none'): + Type of weight normalization. + ``'none'``, ``'spectral'``, ``'weight'`` + or ``'weight_demod'``. + weight_norm_params (obj, optional, default=None): + Parameters of weight normalization. + If not ``None``, ``weight_norm_params.__dict__`` will be used as + keyword arguments when initializing weight normalization. + activation_norm_type (str, optional, default='none'): + Type of activation normalization. + ``'none'``, ``'instance'``, ``'batch'``, ``'sync_batch'``, + ``'layer'``, ``'layer_2d'``, ``'group'``, ``'adaptive'``, + ``'spatially_adaptive'`` or ``'hyper_spatially_adaptive'``. + activation_norm_params (obj, optional, default=None): + Parameters of activation normalization. + If not ``None``, ``activation_norm_params.__dict__`` will be used as + keyword arguments when initializing activation normalization. + nonlinearity (str, optional, default='none'): + Type of nonlinear activation function. + ``'none'``, ``'relu'``, ``'leakyrelu'``, ``'prelu'``, + ``'tanh'`` , ``'sigmoid'`` or ``'softmax'``. + inplace_nonlinearity (bool, optional, default=False): If ``True``, + set ``inplace=True`` when initializing the nonlinearity layer. + apply_noise (bool, optional, default=False): If ``True``, adds + Gaussian noise with learnable magnitude to the convolution output. + order (str, optional, default='CNA'): Order of operations. + ``'C'``: convolution, + ``'N'``: normalization, + ``'A'``: nonlinear activation. + For example, a block initialized with ``order='CNA'`` will + do convolution first, then normalization, then nonlinearity. + """ + + def __init__(self, in_channels, out_channels, kernel_size, stride=1, + padding=0, dilation=1, groups=1, bias=True, + padding_mode='zeros', + weight_norm_type='none', weight_norm_params=None, + activation_norm_type='none', activation_norm_params=None, + nonlinearity='none', inplace_nonlinearity=False, + apply_noise=False, order='CNA'): + super().__init__(in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + nonlinearity, inplace_nonlinearity, apply_noise, + order, 1) + + +class Conv2dBlock(_BaseConvBlock): + r"""A Wrapper class that wraps ``torch.nn.Conv2d`` with normalization and + nonlinearity. + + Args: + in_channels (int): Number of channels in the input tensor. + out_channels (int): Number of channels in the output tensor. + kernel_size (int or tuple): Size of the convolving kernel. + stride (int or tuple, optional, default=1): + Stride of the convolution. + padding (int or tuple, optional, default=0): + Zero-padding added to both sides of the input. + dilation (int or tuple, optional, default=1): + Spacing between kernel elements. + groups (int, optional, default=1): Number of blocked connections + from input channels to output channels. + bias (bool, optional, default=True): + If ``True``, adds a learnable bias to the output. + padding_mode (string, optional, default='zeros'): Type of padding: + ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. + weight_norm_type (str, optional, default='none'): + Type of weight normalization. + ``'none'``, ``'spectral'``, ``'weight'`` + or ``'weight_demod'``. + weight_norm_params (obj, optional, default=None): + Parameters of weight normalization. + If not ``None``, ``weight_norm_params.__dict__`` will be used as + keyword arguments when initializing weight normalization. + activation_norm_type (str, optional, default='none'): + Type of activation normalization. + ``'none'``, ``'instance'``, ``'batch'``, ``'sync_batch'``, + ``'layer'``, ``'layer_2d'``, ``'group'``, ``'adaptive'``, + ``'spatially_adaptive'`` or ``'hyper_spatially_adaptive'``. + activation_norm_params (obj, optional, default=None): + Parameters of activation normalization. + If not ``None``, ``activation_norm_params.__dict__`` will be used as + keyword arguments when initializing activation normalization. + nonlinearity (str, optional, default='none'): + Type of nonlinear activation function. + ``'none'``, ``'relu'``, ``'leakyrelu'``, ``'prelu'``, + ``'tanh'`` , ``'sigmoid'`` or ``'softmax'``. + inplace_nonlinearity (bool, optional, default=False): If ``True``, + set ``inplace=True`` when initializing the nonlinearity layer. + apply_noise (bool, optional, default=False): If ``True``, adds + Gaussian noise with learnable magnitude to the convolution output. + order (str, optional, default='CNA'): Order of operations. + ``'C'``: convolution, + ``'N'``: normalization, + ``'A'``: nonlinear activation. + For example, a block initialized with ``order='CNA'`` will + do convolution first, then normalization, then nonlinearity. + """ + + def __init__(self, in_channels, out_channels, kernel_size, stride=1, + padding=0, dilation=1, groups=1, bias=True, + padding_mode='zeros', + weight_norm_type='none', weight_norm_params=None, + activation_norm_type='none', activation_norm_params=None, + nonlinearity='none', inplace_nonlinearity=False, + apply_noise=False, order='CNA'): + super().__init__(in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + nonlinearity, inplace_nonlinearity, + apply_noise, order, 2) + + +class Conv3dBlock(_BaseConvBlock): + r"""A Wrapper class that wraps ``torch.nn.Conv3d`` with normalization and + nonlinearity. + + Args: + in_channels (int): Number of channels in the input tensor. + out_channels (int): Number of channels in the output tensor. + kernel_size (int or tuple): Size of the convolving kernel. + stride (int or tuple, optional, default=1): + Stride of the convolution. + padding (int or tuple, optional, default=0): + Zero-padding added to both sides of the input. + dilation (int or tuple, optional, default=1): + Spacing between kernel elements. + groups (int, optional, default=1): Number of blocked connections + from input channels to output channels. + bias (bool, optional, default=True): + If ``True``, adds a learnable bias to the output. + padding_mode (string, optional, default='zeros'): Type of padding: + ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. + weight_norm_type (str, optional, default='none'): + Type of weight normalization. + ``'none'``, ``'spectral'``, ``'weight'`` + or ``'weight_demod'``. + weight_norm_params (obj, optional, default=None): + Parameters of weight normalization. + If not ``None``, ``weight_norm_params.__dict__`` will be used as + keyword arguments when initializing weight normalization. + activation_norm_type (str, optional, default='none'): + Type of activation normalization. + ``'none'``, ``'instance'``, ``'batch'``, ``'sync_batch'``, + ``'layer'``, ``'layer_2d'``, ``'group'``, ``'adaptive'``, + ``'spatially_adaptive'`` or ``'hyper_spatially_adaptive'``. + activation_norm_params (obj, optional, default=None): + Parameters of activation normalization. + If not ``None``, ``activation_norm_params.__dict__`` will be used as + keyword arguments when initializing activation normalization. + nonlinearity (str, optional, default='none'): + Type of nonlinear activation function. + ``'none'``, ``'relu'``, ``'leakyrelu'``, ``'prelu'``, + ``'tanh'`` , ``'sigmoid'`` or ``'softmax'``. + inplace_nonlinearity (bool, optional, default=False): If ``True``, + set ``inplace=True`` when initializing the nonlinearity layer. + apply_noise (bool, optional, default=False): If ``True``, adds + Gaussian noise with learnable magnitude to the convolution output. + order (str, optional, default='CNA'): Order of operations. + ``'C'``: convolution, + ``'N'``: normalization, + ``'A'``: nonlinear activation. + For example, a block initialized with ``order='CNA'`` will + do convolution first, then normalization, then nonlinearity. + """ + + def __init__(self, in_channels, out_channels, kernel_size, stride=1, + padding=0, dilation=1, groups=1, bias=True, + padding_mode='zeros', + weight_norm_type='none', weight_norm_params=None, + activation_norm_type='none', activation_norm_params=None, + nonlinearity='none', inplace_nonlinearity=False, + apply_noise=False, + order='CNA'): + super().__init__(in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + nonlinearity, inplace_nonlinearity, + apply_noise, order, 3) + + +class _BaseHyperConvBlock(_BaseConvBlock): + r"""An abstract wrapper class that wraps a hyper convolutional layer + with normalization and nonlinearity. + """ + + def __init__(self, in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, + padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + nonlinearity, inplace_nonlinearity, apply_noise, + is_hyper_conv, is_hyper_norm, + order, input_dim): + self.is_hyper_conv = is_hyper_conv + if is_hyper_conv: + weight_norm_type = 'none' + if is_hyper_norm: + activation_norm_type = 'hyper_' + activation_norm_type + super().__init__(in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + nonlinearity, inplace_nonlinearity, apply_noise, + order, input_dim) + + def _get_conv_layer(self, in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, padding_mode, + input_dim): + if input_dim == 0: + raise ValueError('HyperLinearBlock is not supported.') + else: + name = 'HyperConv' if self.is_hyper_conv else 'nn.Conv' + layer_type = eval(name + '%dd' % input_dim) + layer = layer_type( + in_channels, out_channels, kernel_size, stride, padding, + dilation, groups, bias, padding_mode) + return layer + + +class HyperConv2dBlock(_BaseHyperConvBlock): + r"""A Wrapper class that wraps ``HyperConv2d`` with normalization and + nonlinearity. + + Args: + in_channels (int): Number of channels in the input tensor. + out_channels (int): Number of channels in the output tensor. + kernel_size (int or tuple): Size of the convolving kernel. + stride (int or tuple, optional, default=1): + Stride of the convolution. + padding (int or tuple, optional, default=0): + Zero-padding added to both sides of the input. + dilation (int or tuple, optional, default=1): + Spacing between kernel elements. + groups (int, optional, default=1): Number of blocked connections + from input channels to output channels. + bias (bool, optional, default=True): + If ``True``, adds a learnable bias to the output. + padding_mode (string, optional, default='zeros'): Type of padding: + ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. + weight_norm_type (str, optional, default='none'): + Type of weight normalization. + ``'none'``, ``'spectral'``, ``'weight'`` + or ``'weight_demod'``. + weight_norm_params (obj, optional, default=None): + Parameters of weight normalization. + If not ``None``, ``weight_norm_params.__dict__`` will be used as + keyword arguments when initializing weight normalization. + activation_norm_type (str, optional, default='none'): + Type of activation normalization. + ``'none'``, ``'instance'``, ``'batch'``, ``'sync_batch'``, + ``'layer'``, ``'layer_2d'``, ``'group'``, ``'adaptive'``, + ``'spatially_adaptive'`` or ``'hyper_spatially_adaptive'``. + activation_norm_params (obj, optional, default=None): + Parameters of activation normalization. + If not ``None``, ``activation_norm_params.__dict__`` will be used as + keyword arguments when initializing activation normalization. + is_hyper_conv (bool, optional, default=False): If ``True``, use + ``HyperConv2d``, otherwise use ``torch.nn.Conv2d``. + is_hyper_norm (bool, optional, default=False): If ``True``, use + hyper normalizations. + nonlinearity (str, optional, default='none'): + Type of nonlinear activation function. + ``'none'``, ``'relu'``, ``'leakyrelu'``, ``'prelu'``, + ``'tanh'`` , ``'sigmoid'`` or ``'softmax'``. + inplace_nonlinearity (bool, optional, default=False): If ``True``, + set ``inplace=True`` when initializing the nonlinearity layer. + apply_noise (bool, optional, default=False): If ``True``, adds + Gaussian noise with learnable magnitude to the convolution output. + order (str, optional, default='CNA'): Order of operations. + ``'C'``: convolution, + ``'N'``: normalization, + ``'A'``: nonlinear activation. + For example, a block initialized with ``order='CNA'`` will + do convolution first, then normalization, then nonlinearity. + """ + + def __init__(self, in_channels, out_channels, kernel_size, stride=1, + padding=0, dilation=1, groups=1, bias=True, + padding_mode='zeros', + weight_norm_type='none', weight_norm_params=None, + activation_norm_type='none', activation_norm_params=None, + is_hyper_conv=False, is_hyper_norm=False, + nonlinearity='none', inplace_nonlinearity=False, + apply_noise=False, order='CNA'): + super().__init__(in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + nonlinearity, inplace_nonlinearity, apply_noise, + is_hyper_conv, is_hyper_norm, order, 2) + + +class HyperConv2d(nn.Module): + r"""Hyper Conv2d initialization. + + Args: + in_channels (int): Dummy parameter. + out_channels (int): Dummy parameter. + kernel_size (int or tuple): Dummy parameter. + stride (int or tuple, optional, default=1): + Stride of the convolution. Default: 1 + padding (int or tuple, optional, default=0): + Zero-padding added to both sides of the input. + padding_mode (string, optional, default='zeros'): + ``'zeros'``, ``'reflect'``, ``'replicate'`` + or ``'circular'``. + dilation (int or tuple, optional, default=1): + Spacing between kernel elements. + groups (int, optional, default=1): Number of blocked connections + from input channels to output channels. + bias (bool, optional, default=True): If ``True``, + adds a learnable bias to the output. + """ + + def __init__(self, in_channels=0, out_channels=0, kernel_size=3, + stride=1, padding=1, dilation=1, groups=1, bias=True, + padding_mode='zeros'): + super().__init__() + self.stride = stride + self.padding = padding + self.dilation = dilation + self.groups = groups + self.use_bias = bias + self.padding_mode = padding_mode + self.conditional = True + + def forward(self, x, *args, conv_weights=(None, None), **kwargs): + r"""Hyper Conv2d forward. Convolve x using the provided weight and bias. + + Args: + x (N x C x H x W tensor): Input tensor. + conv_weights (N x C2 x C1 x k x k tensor or list of tensors): + Convolution weights or [weight, bias]. + Returns: + y (N x C2 x H x W tensor): Output tensor. + """ + if conv_weights is None: + conv_weight, conv_bias = None, None + elif isinstance(conv_weights, torch.Tensor): + conv_weight, conv_bias = conv_weights, None + else: + conv_weight, conv_bias = conv_weights + + if conv_weight is None: + return x + if conv_bias is None: + if self.use_bias: + raise ValueError('bias not provided but set to true during ' + 'initialization') + conv_bias = [None] * x.size(0) + if self.padding_mode != 'zeros': + x = F.pad(x, [self.padding] * 4, mode=self.padding_mode) + padding = 0 + else: + padding = self.padding + + y = None + for i in range(x.size(0)): + if self.stride >= 1: + yi = F.conv2d(x[i: i + 1], + weight=conv_weight[i], bias=conv_bias[i], + stride=self.stride, padding=padding, + dilation=self.dilation, groups=self.groups) + else: + yi = F.conv_transpose2d(x[i: i + 1], weight=conv_weight[i], + bias=conv_bias[i], padding=self.padding, + stride=int(1 / self.stride), + dilation=self.dilation, + output_padding=self.padding, + groups=self.groups) + y = torch.cat([y, yi]) if y is not None else yi + return y + + +class _BasePartialConvBlock(_BaseConvBlock): + r"""An abstract wrapper class that wraps a partial convolutional layer + with normalization and nonlinearity. + """ + + def __init__(self, in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + nonlinearity, inplace_nonlinearity, + multi_channel, return_mask, + apply_noise, order, input_dim): + self.multi_channel = multi_channel + self.return_mask = return_mask + self.partial_conv = True + super().__init__(in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + nonlinearity, inplace_nonlinearity, apply_noise, + order, input_dim) + + def _get_conv_layer(self, in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, padding_mode, + input_dim): + if input_dim == 2: + layer_type = PartialConv2d + elif input_dim == 3: + layer_type = PartialConv3d + else: + raise ValueError('Partial conv only supports 2D and 3D conv now.') + layer = layer_type( + in_channels, out_channels, kernel_size, stride, padding, + dilation, groups, bias, padding_mode, + multi_channel=self.multi_channel, return_mask=self.return_mask) + return layer + + def forward(self, x, *cond_inputs, mask_in=None, **kw_cond_inputs): + r""" + + Args: + x (tensor): Input tensor. + cond_inputs (list of tensors) : Conditional input tensors. + mask_in (tensor, optional, default=``None``) If not ``None``, + it masks the valid input region. + kw_cond_inputs (dict) : Keyword conditional inputs. + Returns: + (tuple): + - x (tensor): Output tensor. + - mask_out (tensor, optional): Masks the valid output region. + """ + mask_out = None + for layer in self.layers.values(): + if getattr(layer, 'conditional', False): + x = layer(x, *cond_inputs, **kw_cond_inputs) + elif getattr(layer, 'partial_conv', False): + x = layer(x, mask_in=mask_in, **kw_cond_inputs) + if type(x) == tuple: + x, mask_out = x + else: + x = layer(x) + + if mask_out is not None: + return x, mask_out + return x + + +class PartialConv2dBlock(_BasePartialConvBlock): + r"""A Wrapper class that wraps ``PartialConv2d`` with normalization and + nonlinearity. + + Args: + in_channels (int): Number of channels in the input tensor. + out_channels (int): Number of channels in the output tensor. + kernel_size (int or tuple): Size of the convolving kernel. + stride (int or tuple, optional, default=1): + Stride of the convolution. + padding (int or tuple, optional, default=0): + Zero-padding added to both sides of the input. + dilation (int or tuple, optional, default=1): + Spacing between kernel elements. + groups (int, optional, default=1): Number of blocked connections + from input channels to output channels. + bias (bool, optional, default=True): + If ``True``, adds a learnable bias to the output. + padding_mode (string, optional, default='zeros'): Type of padding: + ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. + weight_norm_type (str, optional, default='none'): + Type of weight normalization. + ``'none'``, ``'spectral'``, ``'weight'`` + or ``'weight_demod'``. + weight_norm_params (obj, optional, default=None): + Parameters of weight normalization. + If not ``None``, ``weight_norm_params.__dict__`` will be used as + keyword arguments when initializing weight normalization. + activation_norm_type (str, optional, default='none'): + Type of activation normalization. + ``'none'``, ``'instance'``, ``'batch'``, ``'sync_batch'``, + ``'layer'``, ``'layer_2d'``, ``'group'``, ``'adaptive'``, + ``'spatially_adaptive'`` or ``'hyper_spatially_adaptive'``. + activation_norm_params (obj, optional, default=None): + Parameters of activation normalization. + If not ``None``, ``activation_norm_params.__dict__`` will be used as + keyword arguments when initializing activation normalization. + nonlinearity (str, optional, default='none'): + Type of nonlinear activation function. + ``'none'``, ``'relu'``, ``'leakyrelu'``, ``'prelu'``, + ``'tanh'`` , ``'sigmoid'`` or ``'softmax'``. + inplace_nonlinearity (bool, optional, default=False): If ``True``, + set ``inplace=True`` when initializing the nonlinearity layer. + apply_noise (bool, optional, default=False): If ``True``, adds + Gaussian noise with learnable magnitude to the convolution output. + order (str, optional, default='CNA'): Order of operations. + ``'C'``: convolution, + ``'N'``: normalization, + ``'A'``: nonlinear activation. + For example, a block initialized with ``order='CNA'`` will + do convolution first, then normalization, then nonlinearity. + multi_channel (bool, optional, default=False): If ``True``, use + different masks for different channels. + return_mask (bool, optional, default=True): If ``True``, the + forward call also returns a new mask. + """ + + def __init__(self, in_channels, out_channels, kernel_size, stride=1, + padding=0, dilation=1, groups=1, bias=True, + padding_mode='zeros', + weight_norm_type='none', weight_norm_params=None, + activation_norm_type='none', activation_norm_params=None, + nonlinearity='none', inplace_nonlinearity=False, + multi_channel=False, return_mask=True, + apply_noise=False, order='CNA'): + super().__init__(in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + nonlinearity, inplace_nonlinearity, + multi_channel, return_mask, apply_noise, order, 2) + + +class PartialConv3dBlock(_BasePartialConvBlock): + r"""A Wrapper class that wraps ``PartialConv3d`` with normalization and + nonlinearity. + + Args: + in_channels (int): Number of channels in the input tensor. + out_channels (int): Number of channels in the output tensor. + kernel_size (int or tuple): Size of the convolving kernel. + stride (int or tuple, optional, default=1): + Stride of the convolution. + padding (int or tuple, optional, default=0): + Zero-padding added to both sides of the input. + dilation (int or tuple, optional, default=1): + Spacing between kernel elements. + groups (int, optional, default=1): Number of blocked connections + from input channels to output channels. + bias (bool, optional, default=True): + If ``True``, adds a learnable bias to the output. + padding_mode (string, optional, default='zeros'): Type of padding: + ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. + weight_norm_type (str, optional, default='none'): + Type of weight normalization. + ``'none'``, ``'spectral'``, ``'weight'`` + or ``'weight_demod'``. + weight_norm_params (obj, optional, default=None): + Parameters of weight normalization. + If not ``None``, ``weight_norm_params.__dict__`` will be used as + keyword arguments when initializing weight normalization. + activation_norm_type (str, optional, default='none'): + Type of activation normalization. + ``'none'``, ``'instance'``, ``'batch'``, ``'sync_batch'``, + ``'layer'``, ``'layer_2d'``, ``'group'``, ``'adaptive'``, + ``'spatially_adaptive'`` or ``'hyper_spatially_adaptive'``. + activation_norm_params (obj, optional, default=None): + Parameters of activation normalization. + If not ``None``, ``activation_norm_params.__dict__`` will be used as + keyword arguments when initializing activation normalization. + nonlinearity (str, optional, default='none'): + Type of nonlinear activation function. + ``'none'``, ``'relu'``, ``'leakyrelu'``, ``'prelu'``, + ``'tanh'`` , ``'sigmoid'`` or ``'softmax'``. + inplace_nonlinearity (bool, optional, default=False): If ``True``, + set ``inplace=True`` when initializing the nonlinearity layer. + apply_noise (bool, optional, default=False): If ``True``, adds + Gaussian noise with learnable magnitude to the convolution output. + order (str, optional, default='CNA'): Order of operations. + ``'C'``: convolution, + ``'N'``: normalization, + ``'A'``: nonlinear activation. + For example, a block initialized with ``order='CNA'`` will + do convolution first, then normalization, then nonlinearity. + multi_channel (bool, optional, default=False): If ``True``, use + different masks for different channels. + return_mask (bool, optional, default=True): If ``True``, the + forward call also returns a new mask. + """ + + def __init__(self, in_channels, out_channels, kernel_size, stride=1, + padding=0, dilation=1, groups=1, bias=True, + padding_mode='zeros', + weight_norm_type='none', weight_norm_params=None, + activation_norm_type='none', activation_norm_params=None, + nonlinearity='none', inplace_nonlinearity=False, + multi_channel=False, return_mask=True, + apply_noise=False, order='CNA'): + super().__init__(in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + nonlinearity, inplace_nonlinearity, + multi_channel, return_mask, apply_noise, order, 3) + + +class _MultiOutBaseConvBlock(_BaseConvBlock): + r"""An abstract wrapper class that wraps a hyper convolutional layer with + normalization and nonlinearity. It can return multiple outputs, if some + layers in the block return more than one output. + """ + + def __init__(self, in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, + padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + nonlinearity, inplace_nonlinearity, + apply_noise, order, input_dim): + super().__init__(in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + nonlinearity, inplace_nonlinearity, + apply_noise, order, input_dim) + self.multiple_outputs = True + + def forward(self, x, *cond_inputs, **kw_cond_inputs): + r""" + + Args: + x (tensor): Input tensor. + cond_inputs (list of tensors) : Conditional input tensors. + kw_cond_inputs (dict) : Keyword conditional inputs. + Returns: + (tuple): + - x (tensor): Main output tensor. + - other_outputs (list of tensors): Other output tensors. + """ + other_outputs = [] + for layer in self.layers.values(): + if getattr(layer, 'conditional', False): + x = layer(x, *cond_inputs, **kw_cond_inputs) + if getattr(layer, 'multiple_outputs', False): + x, other_output = layer(x) + other_outputs.append(other_output) + else: + x = layer(x) + return (x, *other_outputs) + + +class MultiOutConv2dBlock(_MultiOutBaseConvBlock): + r"""A Wrapper class that wraps ``torch.nn.Conv2d`` with normalization and + nonlinearity. It can return multiple outputs, if some layers in the block + return more than one output. + + Args: + in_channels (int): Number of channels in the input tensor. + out_channels (int): Number of channels in the output tensor. + kernel_size (int or tuple): Size of the convolving kernel. + stride (int or tuple, optional, default=1): + Stride of the convolution. + padding (int or tuple, optional, default=0): + Zero-padding added to both sides of the input. + dilation (int or tuple, optional, default=1): + Spacing between kernel elements. + groups (int, optional, default=1): Number of blocked connections + from input channels to output channels. + bias (bool, optional, default=True): + If ``True``, adds a learnable bias to the output. + padding_mode (string, optional, default='zeros'): Type of padding: + ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. + weight_norm_type (str, optional, default='none'): + Type of weight normalization. + ``'none'``, ``'spectral'``, ``'weight'`` + or ``'weight_demod'``. + weight_norm_params (obj, optional, default=None): + Parameters of weight normalization. + If not ``None``, ``weight_norm_params.__dict__`` will be used as + keyword arguments when initializing weight normalization. + activation_norm_type (str, optional, default='none'): + Type of activation normalization. + ``'none'``, ``'instance'``, ``'batch'``, ``'sync_batch'``, + ``'layer'``, ``'layer_2d'``, ``'group'``, ``'adaptive'``, + ``'spatially_adaptive'`` or ``'hyper_spatially_adaptive'``. + activation_norm_params (obj, optional, default=None): + Parameters of activation normalization. + If not ``None``, ``activation_norm_params.__dict__`` will be used as + keyword arguments when initializing activation normalization. + nonlinearity (str, optional, default='none'): + Type of nonlinear activation function. + ``'none'``, ``'relu'``, ``'leakyrelu'``, ``'prelu'``, + ``'tanh'`` , ``'sigmoid'`` or ``'softmax'``. + inplace_nonlinearity (bool, optional, default=False): If ``True``, + set ``inplace=True`` when initializing the nonlinearity layer. + apply_noise (bool, optional, default=False): If ``True``, adds + Gaussian noise with learnable magnitude to the convolution output. + order (str, optional, default='CNA'): Order of operations. + ``'C'``: convolution, + ``'N'``: normalization, + ``'A'``: nonlinear activation. + For example, a block initialized with ``order='CNA'`` will + do convolution first, then normalization, then nonlinearity. + """ + + def __init__(self, in_channels, out_channels, kernel_size, stride=1, + padding=0, dilation=1, groups=1, bias=True, + padding_mode='zeros', + weight_norm_type='none', weight_norm_params=None, + activation_norm_type='none', activation_norm_params=None, + nonlinearity='none', inplace_nonlinearity=False, + apply_noise=False, order='CNA'): + super().__init__(in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + nonlinearity, inplace_nonlinearity, + apply_noise, order, 2) + + +############################################################################### +# BSD 3-Clause License +# +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Author & Contact: Guilin Liu (guilinl@nvidia.com) +############################################################################### +class PartialConv2d(nn.Conv2d): + r"""Partial 2D convolution in + "Image inpainting for irregular holes using partial convolutions." + Liu et al., ECCV 2018 + """ + + def __init__(self, *args, multi_channel=False, return_mask=True, **kwargs): + # whether the mask is multi-channel or not + self.multi_channel = multi_channel + self.return_mask = return_mask + super(PartialConv2d, self).__init__(*args, **kwargs) + + if self.multi_channel: + self.weight_maskUpdater = torch.ones(self.out_channels, + self.in_channels, + self.kernel_size[0], + self.kernel_size[1]) + else: + self.weight_maskUpdater = torch.ones(1, 1, self.kernel_size[0], + self.kernel_size[1]) + + shape = self.weight_maskUpdater.shape + self.slide_winsize = shape[1] * shape[2] * shape[3] + + self.last_size = (None, None, None, None) + self.update_mask = None + self.mask_ratio = None + self.partial_conv = True + + def forward(self, x, mask_in=None): + r""" + + Args: + x (tensor): Input tensor. + mask_in (tensor, optional, default=``None``) If not ``None``, + it masks the valid input region. + """ + assert len(x.shape) == 4 + if mask_in is not None or self.last_size != tuple(x.shape): + self.last_size = tuple(x.shape) + + with torch.no_grad(): + if self.weight_maskUpdater.type() != x.type(): + self.weight_maskUpdater = self.weight_maskUpdater.to(x) + + if mask_in is None: + # If mask is not provided, create a mask. + if self.multi_channel: + mask = torch.ones(x.data.shape[0], + x.data.shape[1], + x.data.shape[2], + x.data.shape[3]).to(x) + else: + mask = torch.ones(1, 1, x.data.shape[2], + x.data.shape[3]).to(x) + else: + mask = mask_in + + self.update_mask = F.conv2d(mask, self.weight_maskUpdater, + bias=None, stride=self.stride, + padding=self.padding, + dilation=self.dilation, groups=1) + + # For mixed precision training, eps from 1e-8 to 1e-6. + eps = 1e-6 + self.mask_ratio = self.slide_winsize / (self.update_mask + eps) + self.update_mask = torch.clamp(self.update_mask, 0, 1) + self.mask_ratio = torch.mul(self.mask_ratio, self.update_mask) + + raw_out = super(PartialConv2d, self).forward( + torch.mul(x, mask) if mask_in is not None else x) + + if self.bias is not None: + bias_view = self.bias.view(1, self.out_channels, 1, 1) + output = torch.mul(raw_out - bias_view, self.mask_ratio) + bias_view + output = torch.mul(output, self.update_mask) + else: + output = torch.mul(raw_out, self.mask_ratio) + + if self.return_mask: + return output, self.update_mask + else: + return output + + +class PartialConv3d(nn.Conv3d): + r"""Partial 3D convolution in + "Image inpainting for irregular holes using partial convolutions." + Liu et al., ECCV 2018 + """ + + def __init__(self, *args, multi_channel=False, return_mask=True, **kwargs): + # whether the mask is multi-channel or not + self.multi_channel = multi_channel + self.return_mask = return_mask + super(PartialConv3d, self).__init__(*args, **kwargs) + + if self.multi_channel: + self.weight_maskUpdater = \ + torch.ones(self.out_channels, self.in_channels, + self.kernel_size[0], self.kernel_size[1], + self.kernel_size[2]) + else: + self.weight_maskUpdater = torch.ones(1, 1, self.kernel_size[0], + self.kernel_size[1], + self.kernel_size[2]) + self.weight_maskUpdater = self.weight_maskUpdater.to('cuda') + + shape = self.weight_maskUpdater.shape + self.slide_winsize = shape[1] * shape[2] * shape[3] * shape[4] + self.partial_conv = True + + def forward(self, x, mask_in=None): + r""" + + Args: + x (tensor): Input tensor. + mask_in (tensor, optional, default=``None``) If not ``None``, it + masks the valid input region. + """ + assert len(x.shape) == 5 + + with torch.no_grad(): + mask = mask_in + update_mask = F.conv3d(mask, self.weight_maskUpdater, bias=None, + stride=self.stride, padding=self.padding, + dilation=self.dilation, groups=1) + + mask_ratio = self.slide_winsize / (update_mask + 1e-8) + update_mask = torch.clamp(update_mask, 0, 1) + mask_ratio = torch.mul(mask_ratio, update_mask) + + raw_out = super(PartialConv3d, self).forward(torch.mul(x, mask_in)) + + if self.bias is not None: + bias_view = self.bias.view(1, self.out_channels, 1, 1, 1) + output = torch.mul(raw_out - bias_view, mask_ratio) + bias_view + if mask_in is not None: + output = torch.mul(output, update_mask) + else: + output = torch.mul(raw_out, mask_ratio) + + if self.return_mask: + return output, update_mask + else: + return output diff --git a/models/layers/misc.py b/models/layers/misc.py new file mode 100644 index 0000000000000000000000000000000000000000..4877af6f9a730bb4d5526a04864f2b345cf309ea --- /dev/null +++ b/models/layers/misc.py @@ -0,0 +1,47 @@ +# Copyright (C) 2020 NVIDIA Corporation. All rights reserved. +# +# This work is made available under the Nvidia Source Code License-NC. +# To view a copy of this license, check out LICENSE.md +import torch +from torch import nn + + +class ApplyNoise(nn.Module): + r"""Add Gaussian noise to the input tensor.""" + + def __init__(self): + super().__init__() + # scale of the noise + self.weight = nn.Parameter(torch.zeros(1)) + + def forward(self, x, noise=None): + r""" + + Args: + x (tensor): Input tensor. + noise (tensor, optional, default=``None``) : Noise tensor to be + added to the input. + """ + if noise is None: + sz = x.size() + noise = x.new_empty(sz[0], 1, *sz[2:]).normal_() + + return x + self.weight * noise + + +class PartialSequential(nn.Sequential): + r"""Sequential block for partial convolutions.""" + def __init__(self, *modules): + super(PartialSequential, self).__init__(*modules) + + def forward(self, x): + r""" + + Args: + x (tensor): Input tensor. + """ + act = x[:, :-1] + mask = x[:, -1].unsqueeze(1) + for module in self: + act, mask = module(act, mask_in=mask) + return act diff --git a/models/layers/non_local.py b/models/layers/non_local.py new file mode 100644 index 0000000000000000000000000000000000000000..68a6c863d822ff53ca14b7c74fd6574923197882 --- /dev/null +++ b/models/layers/non_local.py @@ -0,0 +1,79 @@ +# Copyright (C) 2020 NVIDIA Corporation. All rights reserved. +# +# This work is made available under the Nvidia Source Code License-NC. +# To view a copy of this license, check out LICENSE.md +from functools import partial + +import torch +import torch.nn as nn + +from imaginaire.layers import Conv2dBlock + + +class NonLocal2dBlock(nn.Module): + r"""Self attention Layer + + Args: + in_channels (int): Number of channels in the input tensor. + scale (bool, optional, default=True): If ``True``, scale the + output by a learnable parameter. + clamp (bool, optional, default=``False``): If ``True``, clamp the + scaling parameter to (-1, 1). + weight_norm_type (str, optional, default='none'): + Type of weight normalization. + ``'none'``, ``'spectral'``, ``'weight'`` + or ``'weight_demod'``. + """ + + def __init__(self, + in_channels, + scale=True, + clamp=False, + weight_norm_type='none'): + super(NonLocal2dBlock, self).__init__() + self.clamp = clamp + self.gamma = nn.Parameter(torch.zeros(1)) if scale else 1.0 + self.in_channels = in_channels + base_conv2d_block = partial(Conv2dBlock, + kernel_size=1, + stride=1, + padding=0, + weight_norm_type=weight_norm_type) + self.theta = base_conv2d_block(in_channels, in_channels // 8) + self.phi = base_conv2d_block(in_channels, in_channels // 8) + self.g = base_conv2d_block(in_channels, in_channels // 2) + self.out_conv = base_conv2d_block(in_channels // 2, in_channels) + self.softmax = nn.Softmax(dim=-1) + self.max_pool = nn.MaxPool2d(2) + + def forward(self, x): + r""" + + Args: + x (tensor) : input feature maps (B X C X W X H) + Returns: + (tuple): + - out (tensor) : self attention value + input feature + - attention (tensor): B x N x N (N is Width*Height) + """ + n, c, h, w = x.size() + theta = self.theta(x).view(n, -1, h * w).permute(0, 2, 1) + + phi = self.phi(x) + phi = self.max_pool(phi).view(n, -1, h * w // 4) + + energy = torch.bmm(theta, phi) + attention = self.softmax(energy) + + g = self.g(x) + g = self.max_pool(g).view(n, -1, h * w // 4) + + out = torch.bmm(g, attention.permute(0, 2, 1)) + out = out.view(n, c // 2, h, w) + out = self.out_conv(out) + + if self.clamp: + out = self.gamma.clamp(-1, 1) * out + x + else: + out = self.gamma * out + x + return out diff --git a/models/layers/nonlinearity.py b/models/layers/nonlinearity.py new file mode 100644 index 0000000000000000000000000000000000000000..cf43a940a8a2c3ea2d8b932ca7528b525c76aae4 --- /dev/null +++ b/models/layers/nonlinearity.py @@ -0,0 +1,37 @@ +# Copyright (C) 2020 NVIDIA Corporation. All rights reserved. +# +# This work is made available under the Nvidia Source Code License-NC. +# To view a copy of this license, check out LICENSE.md +from torch import nn + + +def get_nonlinearity_layer(nonlinearity_type, inplace): + r"""Return a nonlinearity layer. + + Args: + nonlinearity_type (str): + Type of nonlinear activation function. + ``'none'``, ``'relu'``, ``'leakyrelu'``, ``'prelu'``, + ``'tanh'`` , ``'sigmoid'`` or ``'softmax'``. + inplace (bool): If ``True``, set ``inplace=True`` when initializing + the nonlinearity layer. + """ + if nonlinearity_type == 'relu': + nonlinearity = nn.ReLU(inplace=inplace) + elif nonlinearity_type == 'leakyrelu': + nonlinearity = nn.LeakyReLU(0.2, inplace=inplace) + elif nonlinearity_type == 'prelu': + nonlinearity = nn.PReLU() + elif nonlinearity_type == 'tanh': + nonlinearity = nn.Tanh() + elif nonlinearity_type == 'sigmoid': + nonlinearity = nn.Sigmoid() + elif nonlinearity_type.startswith('softmax'): + dim = nonlinearity_type.split(',')[1] if ',' in nonlinearity_type else 1 + nonlinearity = nn.Softmax(dim=int(dim)) + elif nonlinearity_type == 'none' or nonlinearity_type == '': + nonlinearity = None + else: + raise ValueError('Nonlinearity %s is not recognized' % + nonlinearity_type) + return nonlinearity diff --git a/models/layers/residual.py b/models/layers/residual.py new file mode 100644 index 0000000000000000000000000000000000000000..ff6256f712434b9b9231cd88f0d79b0a5b54932c --- /dev/null +++ b/models/layers/residual.py @@ -0,0 +1,1235 @@ +# Copyright (C) 2020 NVIDIA Corporation. All rights reserved. +# +# This work is made available under the Nvidia Source Code License-NC. +# To view a copy of this license, check out LICENSE.md +import functools + +from torch import nn +from torch.nn import Upsample as NearestUpsample +from torch.utils.checkpoint import checkpoint + +from .conv import (Conv1dBlock, Conv2dBlock, Conv3dBlock, HyperConv2dBlock, + LinearBlock, MultiOutConv2dBlock, PartialConv2dBlock, + PartialConv3dBlock) + + +class _BaseResBlock(nn.Module): + r"""An abstract class for residual blocks. + """ + + def __init__(self, in_channels, out_channels, kernel_size, + padding, dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + skip_activation_norm, skip_nonlinearity, + nonlinearity, inplace_nonlinearity, apply_noise, + hidden_channels_equal_out_channels, + order, block, learn_shortcut): + super().__init__() + if order == 'pre_act': + order = 'NACNAC' + if isinstance(bias, bool): + # The bias for conv_block_0, conv_block_1, and conv_block_s. + biases = [bias, bias, bias] + elif isinstance(bias, list): + if len(bias) == 3: + biases = bias + else: + raise ValueError('Bias list must be 3.') + else: + raise ValueError('Bias must be either an integer or s list.') + self.learn_shortcut = (in_channels != out_channels) or learn_shortcut + if len(order) > 6 or len(order) < 5: + raise ValueError('order must be either 5 or 6 characters') + if hidden_channels_equal_out_channels: + hidden_channels = out_channels + else: + hidden_channels = min(in_channels, out_channels) + + # Parameters that are specific for convolutions. + conv_main_params = {} + conv_skip_params = {} + if block != LinearBlock: + conv_base_params = dict(stride=1, dilation=dilation, + groups=groups, padding_mode=padding_mode) + conv_main_params.update(conv_base_params) + conv_main_params.update( + dict(kernel_size=kernel_size, + activation_norm_type=activation_norm_type, + activation_norm_params=activation_norm_params, + padding=padding)) + conv_skip_params.update(conv_base_params) + conv_skip_params.update(dict(kernel_size=1)) + if skip_activation_norm: + conv_skip_params.update( + dict(activation_norm_type=activation_norm_type, + activation_norm_params=activation_norm_params)) + + # Other parameters. + other_params = dict(weight_norm_type=weight_norm_type, + weight_norm_params=weight_norm_params, + apply_noise=apply_noise) + + # Residual branch. + if order.find('A') < order.find('C') and \ + (activation_norm_type == '' or activation_norm_type == 'none'): + # Nonlinearity is the first operation in the residual path. + # In-place nonlinearity will modify the input variable and cause + # backward error. + first_inplace = False + else: + first_inplace = inplace_nonlinearity + self.conv_block_0 = block(in_channels, hidden_channels, + bias=biases[0], + nonlinearity=nonlinearity, + order=order[0:3], + inplace_nonlinearity=first_inplace, + **conv_main_params, + **other_params) + self.conv_block_1 = block(hidden_channels, out_channels, + bias=biases[1], + nonlinearity=nonlinearity, + order=order[3:], + inplace_nonlinearity=inplace_nonlinearity, + **conv_main_params, + **other_params) + + # Shortcut branch. + if self.learn_shortcut: + if skip_nonlinearity: + skip_nonlinearity_type = nonlinearity + else: + skip_nonlinearity_type = '' + self.conv_block_s = block(in_channels, out_channels, + bias=biases[2], + nonlinearity=skip_nonlinearity_type, + order=order[0:3], + **conv_skip_params, + **other_params) + + # Whether this block expects conditional inputs. + self.conditional = \ + getattr(self.conv_block_0, 'conditional', False) or \ + getattr(self.conv_block_1, 'conditional', False) + + def conv_blocks(self, x, *cond_inputs, **kw_cond_inputs): + r"""Returns the output of the residual branch. + + Args: + x (tensor): Input tensor. + cond_inputs (list of tensors) : Conditional input tensors. + kw_cond_inputs (dict) : Keyword conditional inputs. + Returns: + dx (tensor): Output tensor. + """ + dx = self.conv_block_0(x, *cond_inputs, **kw_cond_inputs) + dx = self.conv_block_1(dx, *cond_inputs, **kw_cond_inputs) + return dx + + def forward(self, x, *cond_inputs, do_checkpoint=False, **kw_cond_inputs): + r""" + + Args: + x (tensor): Input tensor. + cond_inputs (list of tensors) : Conditional input tensors. + do_checkpoint (bool, optional, default=``False``) If ``True``, + trade compute for memory by checkpointing the model. + kw_cond_inputs (dict) : Keyword conditional inputs. + Returns: + output (tensor): Output tensor. + """ + if do_checkpoint: + dx = checkpoint(self.conv_blocks, x, *cond_inputs, **kw_cond_inputs) + else: + dx = self.conv_blocks(x, *cond_inputs, **kw_cond_inputs) + + if self.learn_shortcut: + x_shortcut = self.conv_block_s(x, *cond_inputs, **kw_cond_inputs) + else: + x_shortcut = x + output = x_shortcut + dx + return output + + +class ResLinearBlock(_BaseResBlock): + r"""Residual block with full-connected layers. + + Args: + in_channels (int) : Number of channels in the input tensor. + out_channels (int) : Number of channels in the output tensor. + weight_norm_type (str, optional, default='none'): + Type of weight normalization. + ``'none'``, ``'spectral'``, ``'weight'`` + or ``'weight_demod'``. + weight_norm_params (obj, optional, default=None): + Parameters of weight normalization. + If not ``None``, ``weight_norm_params.__dict__`` will be used as + keyword arguments when initializing weight normalization. + activation_norm_type (str, optional, default='none'): + Type of activation normalization. + ``'none'``, ``'instance'``, ``'batch'``, ``'sync_batch'``, + ``'layer'``, ``'layer_2d'``, ``'group'``, ``'adaptive'``, + ``'spatially_adaptive'`` or ``'hyper_spatially_adaptive'``. + activation_norm_params (obj, optional, default=None): + Parameters of activation normalization. + If not ``None``, ``activation_norm_params.__dict__`` will be used as + keyword arguments when initializing activation normalization. + skip_activation_norm (bool, optional, default=True): If ``True`` and + ``learn_shortcut`` is also ``True``, applies activation norm to the + learned shortcut connection. + skip_nonlinearity (bool, optional, default=True): If ``True`` and + ``learn_shortcut`` is also ``True``, applies nonlinearity to the + learned shortcut connection. + nonlinearity (str, optional, default='none'): + Type of nonlinear activation function in the residual link. + ``'none'``, ``'relu'``, ``'leakyrelu'``, ``'prelu'``, + ``'tanh'`` , ``'sigmoid'`` or ``'softmax'``. + inplace_nonlinearity (bool, optional, default=False): If ``True``, + set ``inplace=True`` when initializing the nonlinearity layers. + apply_noise (bool, optional, default=False): If ``True``, add + Gaussian noise with learnable magnitude after the + fully-connected layer. + hidden_channels_equal_out_channels (bool, optional, default=False): + If ``True``, set the hidden channel number to be equal to the + output channel number. If ``False``, the hidden channel number + equals to the smaller of the input channel number and the + output channel number. + order (str, optional, default='CNACNA'): Order of operations + in the residual link. + ``'C'``: fully-connected, + ``'N'``: normalization, + ``'A'``: nonlinear activation. + learn_shortcut (bool, optional, default=False): If ``True``, always use + a convolutional shortcut instead of an identity one, otherwise only + use a convolutional one if input and output have different number of + channels. + """ + + def __init__(self, in_channels, out_channels, bias=True, + weight_norm_type='none', weight_norm_params=None, + activation_norm_type='none', activation_norm_params=None, + skip_activation_norm=True, skip_nonlinearity=False, + nonlinearity='leakyrelu', inplace_nonlinearity=False, + apply_noise=False, hidden_channels_equal_out_channels=False, + order='CNACNA', learn_shortcut=False): + super().__init__(in_channels, out_channels, None, None, + None, None, bias, None, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + skip_activation_norm, skip_nonlinearity, + nonlinearity, inplace_nonlinearity, + apply_noise, hidden_channels_equal_out_channels, + order, LinearBlock, learn_shortcut) + + +class Res1dBlock(_BaseResBlock): + r"""Residual block for 1D input. + + Args: + in_channels (int) : Number of channels in the input tensor. + out_channels (int) : Number of channels in the output tensor. + kernel_size (int, optional, default=3): Kernel size for the + convolutional filters in the residual link. + padding (int, optional, default=1): Padding size. + dilation (int, optional, default=1): Dilation factor. + groups (int, optional, default=1): Number of convolutional/linear + groups. + padding_mode (string, optional, default='zeros'): Type of padding: + ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. + weight_norm_type (str, optional, default='none'): + Type of weight normalization. + ``'none'``, ``'spectral'``, ``'weight'`` + or ``'weight_demod'``. + weight_norm_params (obj, optional, default=None): + Parameters of weight normalization. + If not ``None``, ``weight_norm_params.__dict__`` will be used as + keyword arguments when initializing weight normalization. + activation_norm_type (str, optional, default='none'): + Type of activation normalization. + ``'none'``, ``'instance'``, ``'batch'``, ``'sync_batch'``, + ``'layer'``, ``'layer_2d'``, ``'group'``, ``'adaptive'``, + ``'spatially_adaptive'`` or ``'hyper_spatially_adaptive'``. + activation_norm_params (obj, optional, default=None): + Parameters of activation normalization. + If not ``None``, ``activation_norm_params.__dict__`` will be used as + keyword arguments when initializing activation normalization. + skip_activation_norm (bool, optional, default=True): If ``True`` and + ``learn_shortcut`` is also ``True``, applies activation norm to the + learned shortcut connection. + skip_nonlinearity (bool, optional, default=True): If ``True`` and + ``learn_shortcut`` is also ``True``, applies nonlinearity to the + learned shortcut connection. + nonlinearity (str, optional, default='none'): + Type of nonlinear activation function in the residual link. + ``'none'``, ``'relu'``, ``'leakyrelu'``, ``'prelu'``, + ``'tanh'`` , ``'sigmoid'`` or ``'softmax'``. + inplace_nonlinearity (bool, optional, default=False): If ``True``, + set ``inplace=True`` when initializing the nonlinearity layers. + apply_noise (bool, optional, default=False): If ``True``, adds + Gaussian noise with learnable magnitude to the convolution output. + hidden_channels_equal_out_channels (bool, optional, default=False): + If ``True``, set the hidden channel number to be equal to the + output channel number. If ``False``, the hidden channel number + equals to the smaller of the input channel number and the + output channel number. + order (str, optional, default='CNACNA'): Order of operations + in the residual link. + ``'C'``: convolution, + ``'N'``: normalization, + ``'A'``: nonlinear activation. + learn_shortcut (bool, optional, default=False): If ``True``, always use + a convolutional shortcut instead of an identity one, otherwise only + use a convolutional one if input and output have different number of + channels. + """ + + def __init__(self, in_channels, out_channels, kernel_size=3, + padding=1, dilation=1, groups=1, bias=True, + padding_mode='zeros', + weight_norm_type='none', weight_norm_params=None, + activation_norm_type='none', activation_norm_params=None, + skip_activation_norm=True, skip_nonlinearity=False, + nonlinearity='leakyrelu', inplace_nonlinearity=False, + apply_noise=False, hidden_channels_equal_out_channels=False, + order='CNACNA', learn_shortcut=False): + super().__init__(in_channels, out_channels, kernel_size, padding, + dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + skip_activation_norm, skip_nonlinearity, + nonlinearity, inplace_nonlinearity, apply_noise, + hidden_channels_equal_out_channels, + order, Conv1dBlock, learn_shortcut) + + +class Res2dBlock(_BaseResBlock): + r"""Residual block for 2D input. + + Args: + in_channels (int) : Number of channels in the input tensor. + out_channels (int) : Number of channels in the output tensor. + kernel_size (int, optional, default=3): Kernel size for the + convolutional filters in the residual link. + padding (int, optional, default=1): Padding size. + dilation (int, optional, default=1): Dilation factor. + groups (int, optional, default=1): Number of convolutional/linear + groups. + padding_mode (string, optional, default='zeros'): Type of padding: + ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. + weight_norm_type (str, optional, default='none'): + Type of weight normalization. + ``'none'``, ``'spectral'``, ``'weight'`` + or ``'weight_demod'``. + weight_norm_params (obj, optional, default=None): + Parameters of weight normalization. + If not ``None``, ``weight_norm_params.__dict__`` will be used as + keyword arguments when initializing weight normalization. + activation_norm_type (str, optional, default='none'): + Type of activation normalization. + ``'none'``, ``'instance'``, ``'batch'``, ``'sync_batch'``, + ``'layer'``, ``'layer_2d'``, ``'group'``, ``'adaptive'``, + ``'spatially_adaptive'`` or ``'hyper_spatially_adaptive'``. + activation_norm_params (obj, optional, default=None): + Parameters of activation normalization. + If not ``None``, ``activation_norm_params.__dict__`` will be used as + keyword arguments when initializing activation normalization. + skip_activation_norm (bool, optional, default=True): If ``True`` and + ``learn_shortcut`` is also ``True``, applies activation norm to the + learned shortcut connection. + skip_nonlinearity (bool, optional, default=True): If ``True`` and + ``learn_shortcut`` is also ``True``, applies nonlinearity to the + learned shortcut connection. + nonlinearity (str, optional, default='none'): + Type of nonlinear activation function in the residual link. + ``'none'``, ``'relu'``, ``'leakyrelu'``, ``'prelu'``, + ``'tanh'`` , ``'sigmoid'`` or ``'softmax'``. + inplace_nonlinearity (bool, optional, default=False): If ``True``, + set ``inplace=True`` when initializing the nonlinearity layers. + apply_noise (bool, optional, default=False): If ``True``, adds + Gaussian noise with learnable magnitude to the convolution output. + hidden_channels_equal_out_channels (bool, optional, default=False): + If ``True``, set the hidden channel number to be equal to the + output channel number. If ``False``, the hidden channel number + equals to the smaller of the input channel number and the + output channel number. + order (str, optional, default='CNACNA'): Order of operations + in the residual link. + ``'C'``: convolution, + ``'N'``: normalization, + ``'A'``: nonlinear activation. + learn_shortcut (bool, optional, default=False): If ``True``, always use + a convolutional shortcut instead of an identity one, otherwise only + use a convolutional one if input and output have different number of + channels. + """ + + def __init__(self, in_channels, out_channels, kernel_size=3, + padding=1, dilation=1, groups=1, bias=True, + padding_mode='zeros', + weight_norm_type='none', weight_norm_params=None, + activation_norm_type='none', activation_norm_params=None, + skip_activation_norm=True, skip_nonlinearity=False, + nonlinearity='leakyrelu', inplace_nonlinearity=False, + apply_noise=False, hidden_channels_equal_out_channels=False, + order='CNACNA', learn_shortcut=False): + super().__init__(in_channels, out_channels, kernel_size, padding, + dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + skip_activation_norm, skip_nonlinearity, + nonlinearity, inplace_nonlinearity, apply_noise, + hidden_channels_equal_out_channels, + order, Conv2dBlock, learn_shortcut) + + +class Res3dBlock(_BaseResBlock): + r"""Residual block for 3D input. + + Args: + in_channels (int) : Number of channels in the input tensor. + out_channels (int) : Number of channels in the output tensor. + kernel_size (int, optional, default=3): Kernel size for the + convolutional filters in the residual link. + padding (int, optional, default=1): Padding size. + dilation (int, optional, default=1): Dilation factor. + groups (int, optional, default=1): Number of convolutional/linear + groups. + padding_mode (string, optional, default='zeros'): Type of padding: + ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. + weight_norm_type (str, optional, default='none'): + Type of weight normalization. + ``'none'``, ``'spectral'``, ``'weight'`` + or ``'weight_demod'``. + weight_norm_params (obj, optional, default=None): + Parameters of weight normalization. + If not ``None``, ``weight_norm_params.__dict__`` will be used as + keyword arguments when initializing weight normalization. + activation_norm_type (str, optional, default='none'): + Type of activation normalization. + ``'none'``, ``'instance'``, ``'batch'``, ``'sync_batch'``, + ``'layer'``, ``'layer_2d'``, ``'group'``, ``'adaptive'``, + ``'spatially_adaptive'`` or ``'hyper_spatially_adaptive'``. + activation_norm_params (obj, optional, default=None): + Parameters of activation normalization. + If not ``None``, ``activation_norm_params.__dict__`` will be used as + keyword arguments when initializing activation normalization. + skip_activation_norm (bool, optional, default=True): If ``True`` and + ``learn_shortcut`` is also ``True``, applies activation norm to the + learned shortcut connection. + skip_nonlinearity (bool, optional, default=True): If ``True`` and + ``learn_shortcut`` is also ``True``, applies nonlinearity to the + learned shortcut connection. + nonlinearity (str, optional, default='none'): + Type of nonlinear activation function in the residual link. + ``'none'``, ``'relu'``, ``'leakyrelu'``, ``'prelu'``, + ``'tanh'`` , ``'sigmoid'`` or ``'softmax'``. + inplace_nonlinearity (bool, optional, default=False): If ``True``, + set ``inplace=True`` when initializing the nonlinearity layers. + apply_noise (bool, optional, default=False): If ``True``, adds + Gaussian noise with learnable magnitude to the convolution output. + hidden_channels_equal_out_channels (bool, optional, default=False): + If ``True``, set the hidden channel number to be equal to the + output channel number. If ``False``, the hidden channel number + equals to the smaller of the input channel number and the + output channel number. + order (str, optional, default='CNACNA'): Order of operations + in the residual link. + ``'C'``: convolution, + ``'N'``: normalization, + ``'A'``: nonlinear activation. + learn_shortcut (bool, optional, default=False): If ``True``, always use + a convolutional shortcut instead of an identity one, otherwise only + use a convolutional one if input and output have different number of + channels. + """ + + def __init__(self, in_channels, out_channels, kernel_size=3, + padding=1, dilation=1, groups=1, bias=True, + padding_mode='zeros', + weight_norm_type='none', weight_norm_params=None, + activation_norm_type='none', activation_norm_params=None, + skip_activation_norm=True, skip_nonlinearity=False, + nonlinearity='leakyrelu', inplace_nonlinearity=False, + apply_noise=False, hidden_channels_equal_out_channels=False, + order='CNACNA', learn_shortcut=False): + super().__init__(in_channels, out_channels, kernel_size, padding, + dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + skip_activation_norm, skip_nonlinearity, + nonlinearity, inplace_nonlinearity, apply_noise, + hidden_channels_equal_out_channels, + order, Conv3dBlock, learn_shortcut) + + +class _BaseHyperResBlock(_BaseResBlock): + r"""An abstract class for hyper residual blocks. + """ + + def __init__(self, in_channels, out_channels, kernel_size, + padding, dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + skip_activation_norm, skip_nonlinearity, + nonlinearity, inplace_nonlinearity, apply_noise, + hidden_channels_equal_out_channels, + order, + is_hyper_conv, is_hyper_norm, block, learn_shortcut): + block = functools.partial(block, + is_hyper_conv=is_hyper_conv, + is_hyper_norm=is_hyper_norm) + super().__init__(in_channels, out_channels, kernel_size, padding, + dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + skip_activation_norm, skip_nonlinearity, + nonlinearity, inplace_nonlinearity, apply_noise, + hidden_channels_equal_out_channels, + order, block, learn_shortcut) + + def forward(self, x, *cond_inputs, conv_weights=(None,) * 3, + norm_weights=(None,) * 3, **kw_cond_inputs): + r""" + + Args: + x (tensor): Input tensor. + cond_inputs (list of tensors) : Conditional input tensors. + conv_weights (list of tensors): Convolution weights for + three convolutional layers respectively. + norm_weights (list of tensors): Normalization weights for + three convolutional layers respectively. + kw_cond_inputs (dict) : Keyword conditional inputs. + Returns: + output (tensor): Output tensor. + """ + dx = self.conv_block_0(x, *cond_inputs, conv_weights=conv_weights[0], + norm_weights=norm_weights[0]) + dx = self.conv_block_1(dx, *cond_inputs, conv_weights=conv_weights[1], + norm_weights=norm_weights[1]) + if self.learn_shortcut: + x_shortcut = self.conv_block_s(x, *cond_inputs, + conv_weights=conv_weights[2], + norm_weights=norm_weights[2]) + else: + x_shortcut = x + output = x_shortcut + dx + return output + + +class HyperRes2dBlock(_BaseHyperResBlock): + r"""Hyper residual block for 2D input. + + Args: + in_channels (int) : Number of channels in the input tensor. + out_channels (int) : Number of channels in the output tensor. + kernel_size (int, optional, default=3): Kernel size for the + convolutional filters in the residual link. + padding (int, optional, default=1): Padding size. + dilation (int, optional, default=1): Dilation factor. + groups (int, optional, default=1): Number of convolutional/linear + groups. + padding_mode (string, optional, default='zeros'): Type of padding: + ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. + weight_norm_type (str, optional, default='none'): + Type of weight normalization. + ``'none'``, ``'spectral'``, ``'weight'`` + or ``'weight_demod'``. + weight_norm_params (obj, optional, default=None): + Parameters of weight normalization. + If not ``None``, ``weight_norm_params.__dict__`` will be used as + keyword arguments when initializing weight normalization. + activation_norm_type (str, optional, default='none'): + Type of activation normalization. + ``'none'``, ``'instance'``, ``'batch'``, ``'sync_batch'``, + ``'layer'``, ``'layer_2d'``, ``'group'``, ``'adaptive'``, + ``'spatially_adaptive'`` or ``'hyper_spatially_adaptive'``. + activation_norm_params (obj, optional, default=None): + Parameters of activation normalization. + If not ``None``, ``activation_norm_params.__dict__`` will be used as + keyword arguments when initializing activation normalization. + skip_activation_norm (bool, optional, default=True): If ``True`` and + ``learn_shortcut`` is also ``True``, applies activation norm to the + learned shortcut connection. + skip_nonlinearity (bool, optional, default=True): If ``True`` and + ``learn_shortcut`` is also ``True``, applies nonlinearity to the + learned shortcut connection. + nonlinearity (str, optional, default='none'): + Type of nonlinear activation function in the residual link. + ``'none'``, ``'relu'``, ``'leakyrelu'``, ``'prelu'``, + ``'tanh'`` , ``'sigmoid'`` or ``'softmax'``. + inplace_nonlinearity (bool, optional, default=False): If ``True``, + set ``inplace=True`` when initializing the nonlinearity layers. + apply_noise (bool, optional, default=False): If ``True``, adds + Gaussian noise with learnable magnitude to the convolution output. + hidden_channels_equal_out_channels (bool, optional, default=False): + If ``True``, set the hidden channel number to be equal to the + output channel number. If ``False``, the hidden channel number + equals to the smaller of the input channel number and the + output channel number. + order (str, optional, default='CNACNA'): Order of operations + in the residual link. + ``'C'``: convolution, + ``'N'``: normalization, + ``'A'``: nonlinear activation. + is_hyper_conv (bool, optional, default=False): If ``True``, use + ``HyperConv2d``, otherwise use ``torch.nn.Conv2d``. + is_hyper_norm (bool, optional, default=False): If ``True``, use + hyper normalizations. + learn_shortcut (bool, optional, default=False): If ``True``, always use + a convolutional shortcut instead of an identity one, otherwise only + use a convolutional one if input and output have different number of + channels. + """ + + def __init__(self, in_channels, out_channels, kernel_size=3, + padding=1, dilation=1, groups=1, bias=True, + padding_mode='zeros', + weight_norm_type='', weight_norm_params=None, + activation_norm_type='', activation_norm_params=None, + skip_activation_norm=True, skip_nonlinearity=False, + nonlinearity='leakyrelu', inplace_nonlinearity=False, + apply_noise=False, hidden_channels_equal_out_channels=False, + order='CNACNA', is_hyper_conv=False, is_hyper_norm=False, + learn_shortcut=False): + super().__init__(in_channels, out_channels, kernel_size, padding, + dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + skip_activation_norm, skip_nonlinearity, + nonlinearity, inplace_nonlinearity, apply_noise, + hidden_channels_equal_out_channels, + order, is_hyper_conv, is_hyper_norm, + HyperConv2dBlock, learn_shortcut) + + +class _BaseDownResBlock(_BaseResBlock): + r"""An abstract class for residual blocks with downsampling. + """ + + def __init__(self, in_channels, out_channels, kernel_size, + padding, dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + skip_activation_norm, skip_nonlinearity, + nonlinearity, inplace_nonlinearity, + apply_noise, hidden_channels_equal_out_channels, + order, block, pooling, down_factor, learn_shortcut): + super().__init__(in_channels, out_channels, kernel_size, padding, + dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + skip_activation_norm, skip_nonlinearity, + nonlinearity, inplace_nonlinearity, + apply_noise, hidden_channels_equal_out_channels, + order, block, learn_shortcut) + self.pooling = pooling(down_factor) + + def forward(self, x, *cond_inputs): + r""" + + Args: + x (tensor) : Input tensor. + cond_inputs (list of tensors) : conditional input. + Returns: + output (tensor) : Output tensor. + """ + dx = self.conv_block_0(x, *cond_inputs) + dx = self.conv_block_1(dx, *cond_inputs) + dx = self.pooling(dx) + if self.learn_shortcut: + x_shortcut = self.conv_block_s(x, *cond_inputs) + else: + x_shortcut = x + x_shortcut = self.pooling(x_shortcut) + output = x_shortcut + dx + return output + + +class DownRes2dBlock(_BaseDownResBlock): + r"""Residual block for 2D input with downsampling. + + Args: + in_channels (int) : Number of channels in the input tensor. + out_channels (int) : Number of channels in the output tensor. + kernel_size (int, optional, default=3): Kernel size for the + convolutional filters in the residual link. + padding (int, optional, default=1): Padding size. + dilation (int, optional, default=1): Dilation factor. + groups (int, optional, default=1): Number of convolutional/linear + groups. + padding_mode (string, optional, default='zeros'): Type of padding: + ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. + weight_norm_type (str, optional, default='none'): + Type of weight normalization. + ``'none'``, ``'spectral'``, ``'weight'`` + or ``'weight_demod'``. + weight_norm_params (obj, optional, default=None): + Parameters of weight normalization. + If not ``None``, ``weight_norm_params.__dict__`` will be used as + keyword arguments when initializing weight normalization. + activation_norm_type (str, optional, default='none'): + Type of activation normalization. + ``'none'``, ``'instance'``, ``'batch'``, ``'sync_batch'``, + ``'layer'``, ``'layer_2d'``, ``'group'``, ``'adaptive'``, + ``'spatially_adaptive'`` or ``'hyper_spatially_adaptive'``. + activation_norm_params (obj, optional, default=None): + Parameters of activation normalization. + If not ``None``, ``activation_norm_params.__dict__`` will be used as + keyword arguments when initializing activation normalization. + skip_activation_norm (bool, optional, default=True): If ``True`` and + ``learn_shortcut`` is also ``True``, applies activation norm to the + learned shortcut connection. + skip_nonlinearity (bool, optional, default=True): If ``True`` and + ``learn_shortcut`` is also ``True``, applies nonlinearity to the + learned shortcut connection. + nonlinearity (str, optional, default='none'): + Type of nonlinear activation function in the residual link. + ``'none'``, ``'relu'``, ``'leakyrelu'``, ``'prelu'``, + ``'tanh'`` , ``'sigmoid'`` or ``'softmax'``. + inplace_nonlinearity (bool, optional, default=False): If ``True``, + set ``inplace=True`` when initializing the nonlinearity layers. + apply_noise (bool, optional, default=False): If ``True``, adds + Gaussian noise with learnable magnitude to the convolution output. + hidden_channels_equal_out_channels (bool, optional, default=False): + If ``True``, set the hidden channel number to be equal to the + output channel number. If ``False``, the hidden channel number + equals to the smaller of the input channel number and the + output channel number. + order (str, optional, default='CNACNA'): Order of operations + in the residual link. + ``'C'``: convolution, + ``'N'``: normalization, + ``'A'``: nonlinear activation. + pooling (class, optional, default=nn.AvgPool2d): Pytorch pooling + layer to be used. + down_factor (int, optional, default=2): Downsampling factor. + learn_shortcut (bool, optional, default=False): If ``True``, always use + a convolutional shortcut instead of an identity one, otherwise only + use a convolutional one if input and output have different number of + channels. + """ + + def __init__(self, in_channels, out_channels, kernel_size=3, + padding=1, dilation=1, groups=1, bias=True, + padding_mode='zeros', + weight_norm_type='none', weight_norm_params=None, + activation_norm_type='none', activation_norm_params=None, + skip_activation_norm=True, skip_nonlinearity=False, + nonlinearity='leakyrelu', inplace_nonlinearity=False, + apply_noise=False, hidden_channels_equal_out_channels=False, + order='CNACNA', pooling=nn.AvgPool2d, down_factor=2, + learn_shortcut=False): + super().__init__(in_channels, out_channels, kernel_size, padding, + dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + skip_activation_norm, skip_nonlinearity, + nonlinearity, inplace_nonlinearity, apply_noise, + hidden_channels_equal_out_channels, + order, Conv2dBlock, pooling, + down_factor, learn_shortcut) + + +class _BaseUpResBlock(_BaseResBlock): + r"""An abstract class for residual blocks with upsampling. + """ + + def __init__(self, in_channels, out_channels, kernel_size, + padding, dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + skip_activation_norm, skip_nonlinearity, + nonlinearity, inplace_nonlinearity, + apply_noise, hidden_channels_equal_out_channels, + order, block, upsample, up_factor, learn_shortcut): + super().__init__(in_channels, out_channels, kernel_size, padding, + dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + skip_activation_norm, skip_nonlinearity, + nonlinearity, inplace_nonlinearity, + apply_noise, hidden_channels_equal_out_channels, + order, block, learn_shortcut) + self.order = order + self.upsample = upsample(scale_factor=up_factor) + + def forward(self, x, *cond_inputs): + r"""Implementation of the up residual block forward function. + If the order is 'NAC' for the first residual block, we will first + do the activation norm and nonlinearity, in the original resolution. + We will then upsample the activation map to a higher resolution. We + then do the convolution. + It is is other orders, then we first do the whole processing and + then upsample. + + Args: + x (tensor) : Input tensor. + cond_inputs (list of tensors) : Conditional input. + Returns: + output (tensor) : Output tensor. + """ + # In this particular upsample residual block operation, we first + # upsample the skip connection. + if self.learn_shortcut: + x_shortcut = self.upsample(x) + x_shortcut = self.conv_block_s(x_shortcut, *cond_inputs) + else: + x_shortcut = self.upsample(x) + + if self.order[0:3] == 'NAC': + for ix, layer in enumerate(self.conv_block_0.layers.values()): + if getattr(layer, 'conditional', False): + x = layer(x, *cond_inputs) + else: + x = layer(x) + if ix == 1: + x = self.upsample(x) + else: + x = self.conv_block_0(x, *cond_inputs) + x = self.upsample(x) + x = self.conv_block_1(x, *cond_inputs) + + output = x_shortcut + x + return output + + +class UpRes2dBlock(_BaseUpResBlock): + r"""Residual block for 2D input with downsampling. + + Args: + in_channels (int) : Number of channels in the input tensor. + out_channels (int) : Number of channels in the output tensor. + kernel_size (int, optional, default=3): Kernel size for the + convolutional filters in the residual link. + padding (int, optional, default=1): Padding size. + dilation (int, optional, default=1): Dilation factor. + groups (int, optional, default=1): Number of convolutional/linear + groups. + padding_mode (string, optional, default='zeros'): Type of padding: + ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. + weight_norm_type (str, optional, default='none'): + Type of weight normalization. + ``'none'``, ``'spectral'``, ``'weight'`` + or ``'weight_demod'``. + weight_norm_params (obj, optional, default=None): + Parameters of weight normalization. + If not ``None``, ``weight_norm_params.__dict__`` will be used as + keyword arguments when initializing weight normalization. + activation_norm_type (str, optional, default='none'): + Type of activation normalization. + ``'none'``, ``'instance'``, ``'batch'``, ``'sync_batch'``, + ``'layer'``, ``'layer_2d'``, ``'group'``, ``'adaptive'``, + ``'spatially_adaptive'`` or ``'hyper_spatially_adaptive'``. + activation_norm_params (obj, optional, default=None): + Parameters of activation normalization. + If not ``None``, ``activation_norm_params.__dict__`` will be used as + keyword arguments when initializing activation normalization. + skip_activation_norm (bool, optional, default=True): If ``True`` and + ``learn_shortcut`` is also ``True``, applies activation norm to the + learned shortcut connection. + skip_nonlinearity (bool, optional, default=True): If ``True`` and + ``learn_shortcut`` is also ``True``, applies nonlinearity to the + learned shortcut connection. + nonlinearity (str, optional, default='none'): + Type of nonlinear activation function in the residual link. + ``'none'``, ``'relu'``, ``'leakyrelu'``, ``'prelu'``, + ``'tanh'`` , ``'sigmoid'`` or ``'softmax'``. + inplace_nonlinearity (bool, optional, default=False): If ``True``, + set ``inplace=True`` when initializing the nonlinearity layers. + apply_noise (bool, optional, default=False): If ``True``, adds + Gaussian noise with learnable magnitude to the convolution output. + hidden_channels_equal_out_channels (bool, optional, default=False): + If ``True``, set the hidden channel number to be equal to the + output channel number. If ``False``, the hidden channel number + equals to the smaller of the input channel number and the + output channel number. + order (str, optional, default='CNACNA'): Order of operations + in the residual link. + ``'C'``: convolution, + ``'N'``: normalization, + ``'A'``: nonlinear activation. + upsample (class, optional, default=NearestUpsample): PPytorch + upsampling layer to be used. + up_factor (int, optional, default=2): Upsampling factor. + learn_shortcut (bool, optional, default=False): If ``True``, always use + a convolutional shortcut instead of an identity one, otherwise only + use a convolutional one if input and output have different number of + channels. + """ + + def __init__(self, in_channels, out_channels, kernel_size=3, + padding=1, dilation=1, groups=1, bias=True, + padding_mode='zeros', + weight_norm_type='none', weight_norm_params=None, + activation_norm_type='none', activation_norm_params=None, + skip_activation_norm=True, skip_nonlinearity=False, + nonlinearity='leakyrelu', inplace_nonlinearity=False, + apply_noise=False, hidden_channels_equal_out_channels=False, + order='CNACNA', upsample=NearestUpsample, up_factor=2, + learn_shortcut=False): + super().__init__(in_channels, out_channels, kernel_size, padding, + dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + skip_activation_norm, skip_nonlinearity, + nonlinearity, inplace_nonlinearity, + apply_noise, hidden_channels_equal_out_channels, + order, Conv2dBlock, + upsample, up_factor, learn_shortcut) + + +class _BasePartialResBlock(_BaseResBlock): + r"""An abstract class for residual blocks with partial convolution. + """ + + def __init__(self, in_channels, out_channels, kernel_size, + padding, dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + skip_activation_norm, skip_nonlinearity, + nonlinearity, inplace_nonlinearity, + multi_channel, return_mask, + apply_noise, hidden_channels_equal_out_channels, + order, block, learn_shortcut): + block = functools.partial(block, + multi_channel=multi_channel, + return_mask=return_mask) + self.partial_conv = True + super().__init__(in_channels, out_channels, kernel_size, padding, + dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + skip_activation_norm, skip_nonlinearity, + nonlinearity, inplace_nonlinearity, + apply_noise, hidden_channels_equal_out_channels, + order, block, learn_shortcut) + + def forward(self, x, *cond_inputs, mask_in=None, **kw_cond_inputs): + r""" + + Args: + x (tensor): Input tensor. + cond_inputs (list of tensors) : Conditional input tensors. + mask_in (tensor, optional, default=``None``) If not ``None``, + it masks the valid input region. + kw_cond_inputs (dict) : Keyword conditional inputs. + Returns: + (tuple): + - output (tensor): Output tensor. + - mask_out (tensor, optional): Masks the valid output region. + """ + if self.conv_block_0.layers.conv.return_mask: + dx, mask_out = self.conv_block_0(x, *cond_inputs, + mask_in=mask_in, **kw_cond_inputs) + dx, mask_out = self.conv_block_1(dx, *cond_inputs, + mask_in=mask_out, **kw_cond_inputs) + else: + dx = self.conv_block_0(x, *cond_inputs, + mask_in=mask_in, **kw_cond_inputs) + dx = self.conv_block_1(dx, *cond_inputs, + mask_in=mask_in, **kw_cond_inputs) + mask_out = None + + if self.learn_shortcut: + x_shortcut = self.conv_block_s(x, mask_in=mask_in, *cond_inputs, + **kw_cond_inputs) + if type(x_shortcut) == tuple: + x_shortcut, _ = x_shortcut + else: + x_shortcut = x + output = x_shortcut + dx + + if mask_out is not None: + return output, mask_out + return output + + +class PartialRes2dBlock(_BasePartialResBlock): + r"""Residual block for 2D input with partial convolution. + + Args: + in_channels (int) : Number of channels in the input tensor. + out_channels (int) : Number of channels in the output tensor. + kernel_size (int, optional, default=3): Kernel size for the + convolutional filters in the residual link. + padding (int, optional, default=1): Padding size. + dilation (int, optional, default=1): Dilation factor. + groups (int, optional, default=1): Number of convolutional/linear + groups. + padding_mode (string, optional, default='zeros'): Type of padding: + ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. + weight_norm_type (str, optional, default='none'): + Type of weight normalization. + ``'none'``, ``'spectral'``, ``'weight'`` + or ``'weight_demod'``. + weight_norm_params (obj, optional, default=None): + Parameters of weight normalization. + If not ``None``, ``weight_norm_params.__dict__`` will be used as + keyword arguments when initializing weight normalization. + activation_norm_type (str, optional, default='none'): + Type of activation normalization. + ``'none'``, ``'instance'``, ``'batch'``, ``'sync_batch'``, + ``'layer'``, ``'layer_2d'``, ``'group'``, ``'adaptive'``, + ``'spatially_adaptive'`` or ``'hyper_spatially_adaptive'``. + activation_norm_params (obj, optional, default=None): + Parameters of activation normalization. + If not ``None``, ``activation_norm_params.__dict__`` will be used as + keyword arguments when initializing activation normalization. + skip_activation_norm (bool, optional, default=True): If ``True`` and + ``learn_shortcut`` is also ``True``, applies activation norm to the + learned shortcut connection. + skip_nonlinearity (bool, optional, default=True): If ``True`` and + ``learn_shortcut`` is also ``True``, applies nonlinearity to the + learned shortcut connection. + nonlinearity (str, optional, default='none'): + Type of nonlinear activation function in the residual link. + ``'none'``, ``'relu'``, ``'leakyrelu'``, ``'prelu'``, + ``'tanh'`` , ``'sigmoid'`` or ``'softmax'``. + inplace_nonlinearity (bool, optional, default=False): If ``True``, + set ``inplace=True`` when initializing the nonlinearity layers. + apply_noise (bool, optional, default=False): If ``True``, adds + Gaussian noise with learnable magnitude to the convolution output. + hidden_channels_equal_out_channels (bool, optional, default=False): + If ``True``, set the hidden channel number to be equal to the + output channel number. If ``False``, the hidden channel number + equals to the smaller of the input channel number and the + output channel number. + order (str, optional, default='CNACNA'): Order of operations + in the residual link. + ``'C'``: convolution, + ``'N'``: normalization, + ``'A'``: nonlinear activation. + learn_shortcut (bool, optional, default=False): If ``True``, always use + a convolutional shortcut instead of an identity one, otherwise only + use a convolutional one if input and output have different number of + channels. + """ + + def __init__(self, in_channels, out_channels, kernel_size=3, + padding=1, dilation=1, groups=1, bias=True, + padding_mode='zeros', + weight_norm_type='none', weight_norm_params=None, + activation_norm_type='none', activation_norm_params=None, + skip_activation_norm=True, skip_nonlinearity=False, + nonlinearity='leakyrelu', inplace_nonlinearity=False, + multi_channel=False, return_mask=True, + apply_noise=False, + hidden_channels_equal_out_channels=False, + order='CNACNA', learn_shortcut=False): + super().__init__(in_channels, out_channels, kernel_size, padding, + dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + skip_activation_norm, skip_nonlinearity, + nonlinearity, inplace_nonlinearity, + multi_channel, return_mask, + apply_noise, hidden_channels_equal_out_channels, + order, PartialConv2dBlock, learn_shortcut) + + +class PartialRes3dBlock(_BasePartialResBlock): + r"""Residual block for 3D input with partial convolution. + + Args: + in_channels (int) : Number of channels in the input tensor. + out_channels (int) : Number of channels in the output tensor. + kernel_size (int, optional, default=3): Kernel size for the + convolutional filters in the residual link. + padding (int, optional, default=1): Padding size. + dilation (int, optional, default=1): Dilation factor. + groups (int, optional, default=1): Number of convolutional/linear + groups. + padding_mode (string, optional, default='zeros'): Type of padding: + ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. + weight_norm_type (str, optional, default='none'): + Type of weight normalization. + ``'none'``, ``'spectral'``, ``'weight'`` + or ``'weight_demod'``. + weight_norm_params (obj, optional, default=None): + Parameters of weight normalization. + If not ``None``, ``weight_norm_params.__dict__`` will be used as + keyword arguments when initializing weight normalization. + activation_norm_type (str, optional, default='none'): + Type of activation normalization. + ``'none'``, ``'instance'``, ``'batch'``, ``'sync_batch'``, + ``'layer'``, ``'layer_2d'``, ``'group'``, ``'adaptive'``, + ``'spatially_adaptive'`` or ``'hyper_spatially_adaptive'``. + activation_norm_params (obj, optional, default=None): + Parameters of activation normalization. + If not ``None``, ``activation_norm_params.__dict__`` will be used as + keyword arguments when initializing activation normalization. + skip_activation_norm (bool, optional, default=True): If ``True`` and + ``learn_shortcut`` is also ``True``, applies activation norm to the + learned shortcut connection. + skip_nonlinearity (bool, optional, default=True): If ``True`` and + ``learn_shortcut`` is also ``True``, applies nonlinearity to the + learned shortcut connection. + nonlinearity (str, optional, default='none'): + Type of nonlinear activation function in the residual link. + ``'none'``, ``'relu'``, ``'leakyrelu'``, ``'prelu'``, + ``'tanh'`` , ``'sigmoid'`` or ``'softmax'``. + inplace_nonlinearity (bool, optional, default=False): If ``True``, + set ``inplace=True`` when initializing the nonlinearity layers. + apply_noise (bool, optional, default=False): If ``True``, adds + Gaussian noise with learnable magnitude to the convolution output. + hidden_channels_equal_out_channels (bool, optional, default=False): + If ``True``, set the hidden channel number to be equal to the + output channel number. If ``False``, the hidden channel number + equals to the smaller of the input channel number and the + output channel number. + order (str, optional, default='CNACNA'): Order of operations + in the residual link. + ``'C'``: convolution, + ``'N'``: normalization, + ``'A'``: nonlinear activation. + learn_shortcut (bool, optional, default=False): If ``True``, always use + a convolutional shortcut instead of an identity one, otherwise only + use a convolutional one if input and output have different number of + channels. + """ + + def __init__(self, in_channels, out_channels, kernel_size=3, + padding=1, dilation=1, groups=1, bias=True, + padding_mode='zeros', + weight_norm_type='none', weight_norm_params=None, + activation_norm_type='none', activation_norm_params=None, + skip_activation_norm=True, skip_nonlinearity=False, + nonlinearity='leakyrelu', inplace_nonlinearity=False, + multi_channel=False, return_mask=True, + apply_noise=False, hidden_channels_equal_out_channels=False, + order='CNACNA', learn_shortcut=False): + super().__init__(in_channels, out_channels, kernel_size, padding, + dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + skip_activation_norm, skip_nonlinearity, + nonlinearity, inplace_nonlinearity, + multi_channel, return_mask, + apply_noise, hidden_channels_equal_out_channels, + order, PartialConv3dBlock, learn_shortcut) + + +class _BaseMultiOutResBlock(_BaseResBlock): + r"""An abstract class for residual blocks that can returns multiple outputs. + """ + + def __init__(self, in_channels, out_channels, kernel_size, + padding, dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + skip_activation_norm, skip_nonlinearity, + nonlinearity, inplace_nonlinearity, + apply_noise, hidden_channels_equal_out_channels, + order, block, learn_shortcut): + self.multiple_outputs = True + super().__init__(in_channels, out_channels, kernel_size, padding, + dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + skip_activation_norm, skip_nonlinearity, + nonlinearity, inplace_nonlinearity, apply_noise, + hidden_channels_equal_out_channels, + order, block, learn_shortcut) + + def forward(self, x, *cond_inputs): + r""" + + Args: + x (tensor): Input tensor. + cond_inputs (list of tensors) : Conditional input tensors. + Returns: + (tuple): + - output (tensor): Output tensor. + - aux_outputs_0 (tensor): Auxiliary output of the first block. + - aux_outputs_1 (tensor): Auxiliary output of the second block. + """ + dx, aux_outputs_0 = self.conv_block_0(x, *cond_inputs) + dx, aux_outputs_1 = self.conv_block_1(dx, *cond_inputs) + if self.learn_shortcut: + # We are not using the auxiliary outputs of self.conv_block_s. + x_shortcut, _ = self.conv_block_s(x, *cond_inputs) + else: + x_shortcut = x + output = x_shortcut + dx + return output, aux_outputs_0, aux_outputs_1 + + +class MultiOutRes2dBlock(_BaseMultiOutResBlock): + r"""Residual block for 2D input. It can return multiple outputs, if some + layers in the block return more than one output. + + Args: + in_channels (int) : Number of channels in the input tensor. + out_channels (int) : Number of channels in the output tensor. + kernel_size (int, optional, default=3): Kernel size for the + convolutional filters in the residual link. + padding (int, optional, default=1): Padding size. + dilation (int, optional, default=1): Dilation factor. + groups (int, optional, default=1): Number of convolutional/linear + groups. + padding_mode (string, optional, default='zeros'): Type of padding: + ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. + weight_norm_type (str, optional, default='none'): + Type of weight normalization. + ``'none'``, ``'spectral'``, ``'weight'`` + or ``'weight_demod'``. + weight_norm_params (obj, optional, default=None): + Parameters of weight normalization. + If not ``None``, ``weight_norm_params.__dict__`` will be used as + keyword arguments when initializing weight normalization. + activation_norm_type (str, optional, default='none'): + Type of activation normalization. + ``'none'``, ``'instance'``, ``'batch'``, ``'sync_batch'``, + ``'layer'``, ``'layer_2d'``, ``'group'``, ``'adaptive'``, + ``'spatially_adaptive'`` or ``'hyper_spatially_adaptive'``. + activation_norm_params (obj, optional, default=None): + Parameters of activation normalization. + If not ``None``, ``activation_norm_params.__dict__`` will be used as + keyword arguments when initializing activation normalization. + skip_activation_norm (bool, optional, default=True): If ``True`` and + ``learn_shortcut`` is also ``True``, applies activation norm to the + learned shortcut connection. + skip_nonlinearity (bool, optional, default=True): If ``True`` and + ``learn_shortcut`` is also ``True``, applies nonlinearity to the + learned shortcut connection. + nonlinearity (str, optional, default='none'): + Type of nonlinear activation function in the residual link. + ``'none'``, ``'relu'``, ``'leakyrelu'``, ``'prelu'``, + ``'tanh'`` , ``'sigmoid'`` or ``'softmax'``. + inplace_nonlinearity (bool, optional, default=False): If ``True``, + set ``inplace=True`` when initializing the nonlinearity layers. + apply_noise (bool, optional, default=False): If ``True``, adds + Gaussian noise with learnable magnitude to the convolution output. + hidden_channels_equal_out_channels (bool, optional, default=False): + If ``True``, set the hidden channel number to be equal to the + output channel number. If ``False``, the hidden channel number + equals to the smaller of the input channel number and the + output channel number. + order (str, optional, default='CNACNA'): Order of operations + in the residual link. + ``'C'``: convolution, + ``'N'``: normalization, + ``'A'``: nonlinear activation. + learn_shortcut (bool, optional, default=False): If ``True``, always use + a convolutional shortcut instead of an identity one, otherwise only + use a convolutional one if input and output have different number of + channels. + """ + + def __init__(self, in_channels, out_channels, kernel_size=3, + padding=1, dilation=1, groups=1, bias=True, + padding_mode='zeros', + weight_norm_type='none', weight_norm_params=None, + activation_norm_type='none', activation_norm_params=None, + skip_activation_norm=True, skip_nonlinearity=False, + nonlinearity='leakyrelu', inplace_nonlinearity=False, + apply_noise=False, hidden_channels_equal_out_channels=False, + order='CNACNA', learn_shortcut=False): + super().__init__(in_channels, out_channels, kernel_size, padding, + dilation, groups, bias, padding_mode, + weight_norm_type, weight_norm_params, + activation_norm_type, activation_norm_params, + skip_activation_norm, skip_nonlinearity, + nonlinearity, inplace_nonlinearity, + apply_noise, hidden_channels_equal_out_channels, + order, MultiOutConv2dBlock, learn_shortcut) diff --git a/models/layers/weight_norm.py b/models/layers/weight_norm.py new file mode 100644 index 0000000000000000000000000000000000000000..49cf9dc78be17401138aa0957672340231ed248e --- /dev/null +++ b/models/layers/weight_norm.py @@ -0,0 +1,92 @@ +# Copyright (C) 2020 NVIDIA Corporation. All rights reserved. +# +# This work is made available under the Nvidia Source Code License-NC. +# To view a copy of this license, check out LICENSE.md +import functools + +import torch +from torch import nn +from torch.nn.utils import spectral_norm, weight_norm + +from .conv import LinearBlock + + +class WeightDemodulation(nn.Module): + r"""Weight demodulation in + "Analyzing and Improving the Image Quality of StyleGAN", Karras et al. + + Args: + conv (torch.nn.Modules): Convolutional layer. + cond_dims (int): The number of channels in the conditional input. + eps (float, optional, default=1e-8): a value added to the + denominator for numerical stability. + adaptive_bias (bool, optional, default=False): If ``True``, adaptively + predicts bias from the conditional input. + demod (bool, optional, default=False): If ``True``, performs + weight demodulation. + """ + + def __init__(self, conv, cond_dims, eps=1e-8, + adaptive_bias=False, demod=True): + super().__init__() + self.conv = conv + self.adaptive_bias = adaptive_bias + if adaptive_bias: + self.conv.register_parameter('bias', None) + self.fc_beta = LinearBlock(cond_dims, self.conv.out_channels) + self.fc_gamma = LinearBlock(cond_dims, self.conv.in_channels) + self.eps = eps + self.demod = demod + self.conditional = True + + def forward(self, x, y): + r"""Weight demodulation forward""" + b, c, h, w = x.size() + self.conv.groups = b + gamma = self.fc_gamma(y) + gamma = gamma[:, None, :, None, None] + weight = self.conv.weight[None, :, :, :, :] * (gamma + 1) + + if self.demod: + d = torch.rsqrt( + (weight ** 2).sum(dim=(2, 3, 4), keepdim=True) + self.eps) + weight = weight * d + + x = x.reshape(1, -1, h, w) + _, _, *ws = weight.shape + weight = weight.reshape(b * self.conv.out_channels, *ws) + x = self.conv.conv2d_forward(x, weight) + + x = x.reshape(-1, self.conv.out_channels, h, w) + if self.adaptive_bias: + x += self.fc_beta(y)[:, :, None, None] + return x + + +def weight_demod(conv, cond_dims=256, eps=1e-8, demod=True): + r"""Weight demodulation.""" + return WeightDemodulation(conv, cond_dims, eps, demod) + + +def get_weight_norm_layer(norm_type, **norm_params): + r"""Return weight normalization. + + Args: + norm_type (str): + Type of weight normalization. + ``'none'``, ``'spectral'``, ``'weight'`` + or ``'weight_demod'``. + norm_params: Arbitrary keyword arguments that will be used to + initialize the weight normalization. + """ + if norm_type == 'none' or norm_type == '': # no normalization + return lambda x: x + elif norm_type == 'spectral': # spectral normalization + return functools.partial(spectral_norm, **norm_params) + elif norm_type == 'weight': # weight normalization + return functools.partial(weight_norm, **norm_params) + elif norm_type == 'weight_demod': # weight demodulation + return functools.partial(weight_demod, **norm_params) + else: + raise ValueError( + 'Weight norm layer %s is not recognized' % norm_type) diff --git a/models/resnet.py b/models/resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..2b17b0801584c8d38fdc57d7a472f88ba1ab35df --- /dev/null +++ b/models/resnet.py @@ -0,0 +1,204 @@ +import torch +import torch.nn as nn + + +def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=dilation, groups=groups, bias=False, dilation=dilation) + + +def conv1x1(in_planes, out_planes, stride=1): + """1x1 convolution""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None): + super(BasicBlock, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + if groups != 1 or base_width != 64: + raise ValueError('BasicBlock only supports groups=1 and base_width=64') + if dilation > 1: + raise NotImplementedError("Dilation > 1 not supported in BasicBlock") + # Both self.conv1 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = norm_layer(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = norm_layer(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None): + super(Bottleneck, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + width = int(planes * (base_width / 64.)) * groups + # Both self.conv2 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv1x1(inplanes, width) + self.bn1 = norm_layer(width) + self.conv2 = conv3x3(width, width, stride, groups, dilation) + self.bn2 = norm_layer(width) + self.conv3 = conv1x1(width, planes * self.expansion) + self.bn3 = norm_layer(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + +class ResNet(nn.Module): + + def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, + groups=1, width_per_group=64, replace_stride_with_dilation=None, + norm_layer=None,input_channel = 3): + super(ResNet, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + self._norm_layer = norm_layer + + self.inplanes = 64 + self.dilation = 1 + if replace_stride_with_dilation is None: + # each element in the tuple indicates if we should replace + # the 2x2 stride with a dilated convolution instead + replace_stride_with_dilation = [False, False, False] + if len(replace_stride_with_dilation) != 3: + raise ValueError("replace_stride_with_dilation should be None " + "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) + self.groups = groups + self.base_width = width_per_group + self.conv1 = nn.Conv2d(input_channel, self.inplanes, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = norm_layer(self.inplanes) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2, + dilate=replace_stride_with_dilation[0]) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2, + dilate=replace_stride_with_dilation[1]) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2, + dilate=replace_stride_with_dilation[2]) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.fc = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an identity. + # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 + if zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) + elif isinstance(m, BasicBlock): + nn.init.constant_(m.bn2.weight, 0) + + def _make_layer(self, block, planes, blocks, stride=1, dilate=False): + norm_layer = self._norm_layer + downsample = None + previous_dilation = self.dilation + if dilate: + self.dilation *= stride + stride = 1 + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + conv1x1(self.inplanes, planes * block.expansion, stride), + norm_layer(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample, self.groups, + self.base_width, previous_dilation, norm_layer)) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append(block(self.inplanes, planes, groups=self.groups, + base_width=self.base_width, dilation=self.dilation, + norm_layer=norm_layer)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.fc(x) + + return x + +def _resnet(arch, block, layers, pretrained, progress, **kwargs): + model = ResNet(block, layers, **kwargs) + return model + +def resnet34(pretrained=False, progress=True, **kwargs): + r"""ResNet-34 model from + `"Deep Residual Learning for Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress, + **kwargs) \ No newline at end of file diff --git a/models/transformer.py b/models/transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..f442750f77cb18a34e890fd6454028632f4e48df --- /dev/null +++ b/models/transformer.py @@ -0,0 +1,391 @@ +import torch.nn as nn +import torch +from models.util import mydownres2Dblock +import numpy as np +from models.util import AntiAliasInterpolation2d,make_coordinate_grid +from sync_batchnorm import SynchronizedBatchNorm2d as BatchNorm2d +import torch.nn.functional as F +import copy + + +class PositionalEncoding(nn.Module): + + def __init__(self, d_hid, n_position=200): + super(PositionalEncoding, self).__init__() + + # Not a parameter + self.register_buffer('pos_table', self._get_sinusoid_encoding_table(n_position, d_hid)) + + def _get_sinusoid_encoding_table(self, n_position, d_hid): + ''' Sinusoid position encoding table ''' + # TODO: make it with torch instead of numpy + + def get_position_angle_vec(position): + return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)] + + sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)]) + sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i + sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 + + return torch.FloatTensor(sinusoid_table).unsqueeze(0) + + def forward(self, winsize): + return self.pos_table[:, :winsize].clone().detach() + +def _get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + raise RuntimeError(F"activation should be relu/gelu, not {activation}.") + +def _get_clones(module, N): + return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) + +class Transformer(nn.Module): + + def __init__(self, d_model=512, nhead=8, num_encoder_layers=6, + num_decoder_layers=6, dim_feedforward=2048, dropout=0.1, + activation="relu", normalize_before=False, + return_intermediate_dec=True): + super().__init__() + + encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, + dropout, activation, normalize_before) + encoder_norm = nn.LayerNorm(d_model) if normalize_before else None + self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) + + decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, + dropout, activation, normalize_before) + decoder_norm = nn.LayerNorm(d_model) + self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm, + return_intermediate=return_intermediate_dec) + + self._reset_parameters() + + self.d_model = d_model + self.nhead = nhead + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def forward(self,opt, src, query_embed, pos_embed): + # flatten NxCxHxW to HWxNxC + + src = src.permute(1, 0, 2) + pos_embed = pos_embed.permute(1, 0, 2) + query_embed = query_embed.permute(1, 0, 2) + + tgt = torch.zeros_like(query_embed) + memory = self.encoder(src, pos=pos_embed) + + hs = self.decoder(tgt, memory, + pos=pos_embed, query_pos=query_embed) + return hs + + +class TransformerEncoder(nn.Module): + + def __init__(self, encoder_layer, num_layers, norm=None): + super().__init__() + self.layers = _get_clones(encoder_layer, num_layers) + self.num_layers = num_layers + self.norm = norm + + def forward(self, src, mask = None, src_key_padding_mask = None, pos = None): + output = src+pos + + for layer in self.layers: + output = layer(output, src_mask=mask, + src_key_padding_mask=src_key_padding_mask, pos=pos) + + if self.norm is not None: + output = self.norm(output) + + return output + + +class TransformerDecoder(nn.Module): + + def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False): + super().__init__() + self.layers = _get_clones(decoder_layer, num_layers) + self.num_layers = num_layers + self.norm = norm + self.return_intermediate = return_intermediate + + def forward(self, tgt, memory, tgt_mask = None, memory_mask = None, tgt_key_padding_mask = None, + memory_key_padding_mask = None, + pos = None, + query_pos = None): + output = tgt+pos+query_pos + + intermediate = [] + + for layer in self.layers: + output = layer(output, memory, tgt_mask=tgt_mask, + memory_mask=memory_mask, + tgt_key_padding_mask=tgt_key_padding_mask, + memory_key_padding_mask=memory_key_padding_mask, + pos=pos, query_pos=query_pos) + if self.return_intermediate: + intermediate.append(self.norm(output)) + + if self.norm is not None: + output = self.norm(output) + if self.return_intermediate: + intermediate.pop() + intermediate.append(output) + + if self.return_intermediate: + return torch.stack(intermediate) + + return output.unsqueeze(0) + + +class TransformerEncoderLayer(nn.Module): + + def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, + activation="relu", normalize_before=False): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + def with_pos_embed(self, tensor, pos): + return tensor if pos is None else tensor + pos + + def forward_post(self, + src, + src_mask = None, + src_key_padding_mask = None, + pos = None): + # q = k = self.with_pos_embed(src, pos) + src2 = self.self_attn(src, src, value=src, attn_mask=src_mask, + key_padding_mask=src_key_padding_mask)[0] + src = src + self.dropout1(src2) + src = self.norm1(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) + src = src + self.dropout2(src2) + src = self.norm2(src) + return src + + def forward_pre(self, src, + src_mask = None, + src_key_padding_mask = None, + pos = None): + src2 = self.norm1(src) + # q = k = self.with_pos_embed(src2, pos) + src2 = self.self_attn(src2, src2, value=src2, attn_mask=src_mask, + key_padding_mask=src_key_padding_mask)[0] + src = src + self.dropout1(src2) + src2 = self.norm2(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src2)))) + src = src + self.dropout2(src2) + return src + + def forward(self, src, + src_mask = None, + src_key_padding_mask = None, + pos = None): + if self.normalize_before: + return self.forward_pre(src, src_mask, src_key_padding_mask, pos) + return self.forward_post(src, src_mask, src_key_padding_mask, pos) + + +class TransformerDecoderLayer(nn.Module): + + def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, + activation="relu", normalize_before=False): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.norm3 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + self.dropout3 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + def with_pos_embed(self, tensor, pos): + return tensor if pos is None else tensor + pos + + def forward_post(self, tgt, memory, + tgt_mask = None, + memory_mask = None, + tgt_key_padding_mask = None, + memory_key_padding_mask = None, + pos = None, + query_pos = None): + # q = k = self.with_pos_embed(tgt, query_pos) + tgt2 = self.self_attn(tgt, tgt, value=tgt, attn_mask=tgt_mask, + key_padding_mask=tgt_key_padding_mask)[0] + tgt = tgt + self.dropout1(tgt2) + tgt = self.norm1(tgt) + tgt2 = self.multihead_attn(query=tgt, + key=memory, + value=memory, attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask)[0] + tgt = tgt + self.dropout2(tgt2) + tgt = self.norm2(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) + tgt = tgt + self.dropout3(tgt2) + tgt = self.norm3(tgt) + return tgt + + def forward_pre(self, tgt, memory, + tgt_mask = None, + memory_mask = None, + tgt_key_padding_mask = None, + memory_key_padding_mask = None, + pos = None, + query_pos = None): + tgt2 = self.norm1(tgt) + # q = k = self.with_pos_embed(tgt2, query_pos) + tgt2 = self.self_attn(tgt2, tgt2, value=tgt2, attn_mask=tgt_mask, + key_padding_mask=tgt_key_padding_mask)[0] + tgt = tgt + self.dropout1(tgt2) + tgt2 = self.norm2(tgt) + tgt2 = self.multihead_attn(query=tgt2, + key=memory, + value=memory, attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask)[0] + tgt = tgt + self.dropout2(tgt2) + tgt2 = self.norm3(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) + tgt = tgt + self.dropout3(tgt2) + return tgt + + def forward(self, tgt, memory, + tgt_mask = None, + memory_mask = None, + tgt_key_padding_mask = None, + memory_key_padding_mask = None, + pos = None, + query_pos = None): + if self.normalize_before: + return self.forward_pre(tgt, memory, tgt_mask, memory_mask, + tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) + return self.forward_post(tgt, memory, tgt_mask, memory_mask, + tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) + + + +class Audio2kpTransformer(nn.Module): + def __init__(self,opt): + super(Audio2kpTransformer, self).__init__() + self.opt = opt + + + self.embedding = nn.Embedding(41, opt.embedding_dim) + self.pos_enc = PositionalEncoding(512,20) + self.down_pose = AntiAliasInterpolation2d(1,0.25) + input_dim = 2 + self.feature_extract = nn.Sequential(mydownres2Dblock(input_dim,32), + mydownres2Dblock(32,64), + mydownres2Dblock(64,128), + mydownres2Dblock(128,256), + mydownres2Dblock(256,512), + nn.AvgPool2d(2)) + + self.decode_dim = 70 + self.audio_embedding = nn.Sequential(nn.ConvTranspose2d(1, 8, (29, 14), stride=(1, 1), padding=(0, 11)), + BatchNorm2d(8), + nn.ReLU(inplace=True), + nn.Conv2d(8, 35, (13, 13), stride=(1, 1), padding=(6, 6))) + self.decodefeature_extract = nn.Sequential(mydownres2Dblock(self.decode_dim,32), + mydownres2Dblock(32,64), + mydownres2Dblock(64,128), + mydownres2Dblock(128,256), + mydownres2Dblock(256,512), + nn.AvgPool2d(2)) + + self.transformer = Transformer() + self.kp = nn.Linear(512,opt.num_kp*2) + self.jacobian = nn.Linear(512,opt.num_kp*4) + self.jacobian.weight.data.zero_() + self.jacobian.bias.data.copy_(torch.tensor([1, 0, 0, 1] * self.opt.num_kp, dtype=torch.float)) + self.criterion = nn.L1Loss() + + def create_sparse_motions(self, source_image, kp_source): + """ + Eq 4. in the paper T_{s<-d}(z) + """ + bs, _, h, w = source_image.shape + identity_grid = make_coordinate_grid((h, w), type=kp_source['value'].type()) + identity_grid = identity_grid.view(1, 1, h, w, 2) + coordinate_grid = identity_grid + if 'jacobian' in kp_source: + jacobian = kp_source['jacobian'] + jacobian = jacobian.unsqueeze(-3).unsqueeze(-3) + jacobian = jacobian.repeat(1, 1, h, w, 1, 1) + coordinate_grid = torch.matmul(jacobian, coordinate_grid.unsqueeze(-1)) + coordinate_grid = coordinate_grid.squeeze(-1) + + driving_to_source = coordinate_grid + kp_source['value'].view(bs, self.opt.num_kp, 1, 1, 2) + + #adding background feature + identity_grid = identity_grid.repeat(bs, 1, 1, 1, 1) + sparse_motions = torch.cat([identity_grid, driving_to_source], dim=1) + + return sparse_motions.permute(0,1,4,2,3).reshape(bs,(self.opt.num_kp+1)*2,64,64) + + + + def forward(self,x, initial_kp = None): + bs,seqlen = x["ph"].shape + ph = x["ph"].reshape(bs*seqlen,1) + pose = x["pose"].reshape(bs*seqlen,1,256,256) + input_feature = self.down_pose(pose) + + phoneme_embedding = self.embedding(ph.long()) + phoneme_embedding = phoneme_embedding.reshape(bs*seqlen, 1, 16, 16) + phoneme_embedding = F.interpolate(phoneme_embedding, scale_factor=4) + input_feature = torch.cat((input_feature, phoneme_embedding), dim=1) + + input_feature = self.feature_extract(input_feature).unsqueeze(-1).reshape(bs,seqlen,512) + + audio = x["audio"].reshape(bs * seqlen, 1, 4, 41) + decoder_feature = self.audio_embedding(audio) + decoder_feature = F.interpolate(decoder_feature, scale_factor=2) + decoder_feature = self.decodefeature_extract(torch.cat( + (decoder_feature, + initial_kp["feature_map"].unsqueeze(1).repeat(1, seqlen, 1, 1, 1).reshape(bs * seqlen, 35, 64, 64)), + dim=1)).unsqueeze(-1).reshape(bs, seqlen, 512) + + posi_em = self.pos_enc(self.opt.num_w*2+1) + + + out = {} + + output_feature = self.transformer(self.opt,input_feature,decoder_feature,posi_em)[-1,self.opt.num_w] + + out["value"] = self.kp(output_feature).reshape(bs,self.opt.num_kp,2) + out["jacobian"] = self.jacobian(output_feature).reshape(bs,self.opt.num_kp,2,2) + + return out + + diff --git a/models/util.py b/models/util.py new file mode 100644 index 0000000000000000000000000000000000000000..02317ced0c8e1d82f26ec01c2a0062ded6d20b9a --- /dev/null +++ b/models/util.py @@ -0,0 +1,354 @@ +from torch import nn + +import torch.nn.functional as F +import torch +import cv2 +import numpy as np + +from models.resnet import resnet34 +from models.layers.residual import Res2dBlock,Res1dBlock,DownRes2dBlock + +from sync_batchnorm import SynchronizedBatchNorm2d as BatchNorm2d + + +def myres2Dblock(indim,outdim,k_size = 3,padding = 1, normalize = "batch",nonlinearity = "relu",order = "NACNAC"): + return Res2dBlock(indim,outdim,k_size,padding,activation_norm_type=normalize,nonlinearity=nonlinearity,inplace_nonlinearity=True,order = order) + +def myres1Dblock(indim,outdim,k_size = 3,padding = 1, normalize = "batch",nonlinearity = "relu",order = "NACNAC"): + return Res1dBlock(indim,outdim,k_size,padding,activation_norm_type=normalize,nonlinearity=nonlinearity,inplace_nonlinearity=True,order = order) + +def mydownres2Dblock(indim,outdim,k_size = 3,padding = 1, normalize = "batch",nonlinearity = "leakyrelu",order = "NACNAC"): + return DownRes2dBlock(indim,outdim,k_size,padding=padding,activation_norm_type=normalize,nonlinearity=nonlinearity,inplace_nonlinearity=True,order = order) + +def gaussian2kp(heatmap): + """ + Extract the mean and from a heatmap + """ + shape = heatmap.shape + heatmap = heatmap.unsqueeze(-1) + grid = make_coordinate_grid(shape[2:], heatmap.type()).unsqueeze_(0).unsqueeze_(0) + value = (heatmap * grid).sum(dim=(2, 3)) + kp = {'value': value} + + return kp + +def kp2gaussian(kp, spatial_size, kp_variance): + """ + Transform a keypoint into gaussian like representation + """ + mean = kp['value'] #bs*numkp*2 + + coordinate_grid = make_coordinate_grid(spatial_size, mean.type()) #h*w*2 + number_of_leading_dimensions = len(mean.shape) - 1 + shape = (1,) * number_of_leading_dimensions + coordinate_grid.shape #1*1*h*w*2 + coordinate_grid = coordinate_grid.view(*shape) + repeats = mean.shape[:number_of_leading_dimensions] + (1, 1, 1) + coordinate_grid = coordinate_grid.repeat(*repeats) #bs*numkp*h*w*2 + + # Preprocess kp shape + shape = mean.shape[:number_of_leading_dimensions] + (1, 1, 2) + mean = mean.view(*shape) + + mean_sub = (coordinate_grid - mean) + + out = torch.exp(-0.5 * (mean_sub ** 2).sum(-1) / kp_variance) + + return out + + +def make_coordinate_grid(spatial_size, type): + """ + Create a meshgrid [-1,1] x [-1,1] of given spatial_size. + """ + h, w = spatial_size + x = torch.arange(w).type(type) + y = torch.arange(h).type(type) + + x = (2 * (x / (w - 1)) - 1) + y = (2 * (y / (h - 1)) - 1) + + yy = y.view(-1, 1).repeat(1, w) + xx = x.view(1, -1).repeat(h, 1) + + meshed = torch.cat([xx.unsqueeze_(2), yy.unsqueeze_(2)], 2) + + return meshed + + +class ResBlock2d(nn.Module): + """ + Res block, preserve spatial resolution. + """ + + def __init__(self, in_features, kernel_size, padding): + super(ResBlock2d, self).__init__() + self.conv1 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, + padding=padding) + self.conv2 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, + padding=padding) + self.norm1 = BatchNorm2d(in_features, affine=True) + self.norm2 = BatchNorm2d(in_features, affine=True) + + def forward(self, x): + out = self.norm1(x) + out = F.relu(out,inplace=True) + out = self.conv1(out) + out = self.norm2(out) + out = F.relu(out,inplace=True) + out = self.conv2(out) + out += x + return out + + +class UpBlock2d(nn.Module): + """ + Upsampling block for use in decoder. + """ + + def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1): + super(UpBlock2d, self).__init__() + + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding, groups=groups) + self.norm = BatchNorm2d(out_features, affine=True) + + def forward(self, x): + out = F.interpolate(x, scale_factor=2) + del x + out = self.conv(out) + out = self.norm(out) + out = F.relu(out,inplace=True) + return out + + +class DownBlock2d(nn.Module): + """ + Downsampling block for use in encoder. + """ + + def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1): + super(DownBlock2d, self).__init__() + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding, groups=groups) + self.norm = BatchNorm2d(out_features, affine=True) + self.pool = nn.AvgPool2d(kernel_size=(2, 2)) + + def forward(self, x): + out = self.conv(x) + del x + out = self.norm(out) + out = F.relu(out,inplace=True) + out = self.pool(out) + return out + + +class SameBlock2d(nn.Module): + """ + Simple block, preserve spatial resolution. + """ + + def __init__(self, in_features, out_features, groups=1, kernel_size=3, padding=1): + super(SameBlock2d, self).__init__() + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, + kernel_size=kernel_size, padding=padding, groups=groups) + self.norm = BatchNorm2d(out_features, affine=True) + + def forward(self, x): + out = self.conv(x) + out = self.norm(out) + out = F.relu(out,inplace=True) + return out + + +class Encoder(nn.Module): + """ + Hourglass Encoder + """ + + def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): + super(Encoder, self).__init__() + + down_blocks = [] + for i in range(num_blocks): + down_blocks.append(DownBlock2d(in_features if i == 0 else min(max_features, block_expansion * (2 ** i)), + min(max_features, block_expansion * (2 ** (i + 1))), + kernel_size=3, padding=1)) + self.down_blocks = nn.ModuleList(down_blocks) + + def forward(self, x): + outs = [x] + for down_block in self.down_blocks: + outs.append(down_block(outs[-1])) + return outs + +class Decoder(nn.Module): + """ + Hourglass Decoder + """ + + def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): + super(Decoder, self).__init__() + + up_blocks = [] + + for i in range(num_blocks)[::-1]: + in_filters = (1 if i == num_blocks - 1 else 2) * min(max_features, block_expansion * (2 ** (i + 1))) + out_filters = min(max_features, block_expansion * (2 ** i)) + up_blocks.append(UpBlock2d(in_filters, out_filters, kernel_size=3, padding=1)) + + self.up_blocks = nn.ModuleList(up_blocks) + self.out_filters = block_expansion + in_features + + def forward(self, x): + out = x.pop() + for up_block in self.up_blocks: + out = up_block(out) + skip = x.pop() + out = torch.cat([out, skip], dim=1) + return out + +class Hourglass(nn.Module): + """ + Hourglass architecture. + """ + + def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): + super(Hourglass, self).__init__() + self.encoder = Encoder(block_expansion, in_features, num_blocks, max_features) + self.decoder = Decoder(block_expansion, in_features, num_blocks, max_features) + self.out_filters = self.decoder.out_filters + + def forward(self, x): + return self.decoder(self.encoder(x)) + +class AntiAliasInterpolation2d(nn.Module): + """ + Band-limited downsampling, for better preservation of the input signal. + """ + def __init__(self, channels, scale): + super(AntiAliasInterpolation2d, self).__init__() + sigma = (1 / scale - 1) / 2 + kernel_size = 2 * round(sigma * 4) + 1 + self.ka = kernel_size // 2 + self.kb = self.ka - 1 if kernel_size % 2 == 0 else self.ka + + + kernel_size = [kernel_size, kernel_size] + sigma = [sigma, sigma] + # The gaussian kernel is the product of the + # gaussian function of each dimension. + kernel = 1 + meshgrids = torch.meshgrid( + [ + torch.arange(size, dtype=torch.float32) + for size in kernel_size + ] + ) + for size, std, mgrid in zip(kernel_size, sigma, meshgrids): + mean = (size - 1) / 2 + kernel *= torch.exp(-(mgrid - mean) ** 2 / (2 * std ** 2)) + + # Make sure sum of values in gaussian kernel equals 1. + kernel = kernel / torch.sum(kernel) + # Reshape to depthwise convolutional weight + kernel = kernel.view(1, 1, *kernel.size()) + kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1)) + + self.register_buffer('weight', kernel) + self.groups = channels + self.scale = scale + + def forward(self, input): + if self.scale == 1.0: + return input + + out = F.pad(input, (self.ka, self.kb, self.ka, self.kb)) + out = F.conv2d(out, weight=self.weight, groups=self.groups) + out = F.interpolate(out, scale_factor=(self.scale, self.scale)) + + return out + +def draw_annotation_box( image, rotation_vector, translation_vector, color=(255, 255, 255), line_width=2): + """Draw a 3D box as annotation of pose""" + + camera_matrix = np.array( + [[233.333, 0, 128], + [0, 233.333, 128], + [0, 0, 1]], dtype="double") + + dist_coeefs = np.zeros((4, 1)) + + point_3d = [] + rear_size = 75 + rear_depth = 0 + point_3d.append((-rear_size, -rear_size, rear_depth)) + point_3d.append((-rear_size, rear_size, rear_depth)) + point_3d.append((rear_size, rear_size, rear_depth)) + point_3d.append((rear_size, -rear_size, rear_depth)) + point_3d.append((-rear_size, -rear_size, rear_depth)) + + front_size = 100 + front_depth = 100 + point_3d.append((-front_size, -front_size, front_depth)) + point_3d.append((-front_size, front_size, front_depth)) + point_3d.append((front_size, front_size, front_depth)) + point_3d.append((front_size, -front_size, front_depth)) + point_3d.append((-front_size, -front_size, front_depth)) + point_3d = np.array(point_3d, dtype=np.float).reshape(-1, 3) + + # Map to 2d image points + (point_2d, _) = cv2.projectPoints(point_3d, + rotation_vector, + translation_vector, + camera_matrix, + dist_coeefs) + point_2d = np.int32(point_2d.reshape(-1, 2)) + + # Draw all the lines + cv2.polylines(image, [point_2d], True, color, line_width, cv2.LINE_AA) + cv2.line(image, tuple(point_2d[1]), tuple( + point_2d[6]), color, line_width, cv2.LINE_AA) + cv2.line(image, tuple(point_2d[2]), tuple( + point_2d[7]), color, line_width, cv2.LINE_AA) + cv2.line(image, tuple(point_2d[3]), tuple( + point_2d[8]), color, line_width, cv2.LINE_AA) + + + +class up_sample(nn.Module): + def __init__(self, scale_factor): + super(up_sample, self).__init__() + self.interp = nn.functional.interpolate + self.scale_factor = scale_factor + + def forward(self, x): + x = self.interp(x, scale_factor=self.scale_factor,mode = 'linear',align_corners = True) + return x + + + +class MyResNet34(nn.Module): + def __init__(self,embedding_dim,input_channel = 3): + super(MyResNet34, self).__init__() + self.resnet = resnet34(norm_layer = BatchNorm2d,num_classes=embedding_dim,input_channel = input_channel) + def forward(self, x): + return self.resnet(x) + + + +class ImagePyramide(torch.nn.Module): + """ + Create image pyramide for computing pyramide perceptual loss. See Sec 3.3 + """ + def __init__(self, scales, num_channels): + super(ImagePyramide, self).__init__() + downs = {} + for scale in scales: + downs[str(scale).replace('.', '-')] = AntiAliasInterpolation2d(num_channels, scale) + self.downs = nn.ModuleDict(downs) + + def forward(self, x): + out_dict = {} + for scale, down_module in self.downs.items(): + out_dict['prediction_' + str(scale).replace('-', '.')] = down_module(x) + return out_dict diff --git a/phindex.json b/phindex.json new file mode 100644 index 0000000000000000000000000000000000000000..755ac0a7eb5b118d98582126c88c9490a047c62d --- /dev/null +++ b/phindex.json @@ -0,0 +1 @@ +{"AA": 0, "AE": 1, "AH": 2, "AO": 3, "AW": 4, "AY": 5, "B": 6, "CH": 7, "D": 8, "DH": 9, "EH": 10, "ER": 11, "EY": 12, "F": 13, "G": 14, "HH": 15, "IH": 16, "IY": 17, "JH": 18, "K": 19, "L": 20, "M": 21, "N": 22, "NG": 23, "NSN": 24, "OW": 25, "OY": 26, "P": 27, "R": 28, "S": 29, "SH": 30, "SIL": 31, "T": 32, "TH": 33, "UH": 34, "UW": 35, "V": 36, "W": 37, 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index 0000000000000000000000000000000000000000..ccb359d364d9238ef9124c653e6a3f230d5e825d --- /dev/null +++ b/samples/phonemes/id01849#WopMMu6MChc#00050.txt#000.json @@ -0,0 +1,407 @@ +[ + { + "word": "sil", + "phones": [ + { + "ph": "SIL", + "bg": 0, + "ed": 12 + } + ] + }, + { + "word": "REMEMBERING", + "phones": [ + { + "ph": "R", + "bg": 12, + "ed": 30 + }, + { + "ph": "IY", + "bg": 30, + "ed": 36 + }, + { + "ph": "M", + "bg": 36, + "ed": 42 + }, + { + "ph": "EH", + "bg": 42, + "ed": 48 + }, + { + "ph": "M", + "bg": 48, + "ed": 54 + }, + { + "ph": "B", + "bg": 54, + "ed": 57 + }, + { + "ph": "ER", + "bg": 57, + "ed": 63 + }, + { + "ph": "IH", + "bg": 63, + "ed": 69 + }, + { + "ph": "NG", + "bg": 69, + "ed": 75 + } + ] + }, + { + "word": "THAT", + "phones": [ + { + "ph": "DH", + "bg": 75, + "ed": 81 + }, + { + "ph": "AE", + "bg": 81, + "ed": 90 + }, + { + "ph": "T", + "bg": 90, + "ed": 96 + } + ] + }, + { + "word": "I", + "phones": [ + { + "ph": "AY", + "bg": 96, + "ed": 102 + } + ] 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diff --git a/samples/phonemes/trump_strong.json b/samples/phonemes/trump_strong.json new file mode 100644 index 0000000000000000000000000000000000000000..200e7e5e2f947b53afecb13503bea29a73615685 --- /dev/null +++ b/samples/phonemes/trump_strong.json @@ -0,0 +1,1232 @@ +[ + { + "word": "sil", + "phones": [ + { + "ph": "SIL", + "bg": 0, + "ed": 126 + } + ] + }, + { + "word": "YOU", + "phones": [ + { + "ph": "Y", + "bg": 126, + "ed": 138 + }, + { + "ph": "UW", + "bg": 138, + "ed": 141 + } + ] + }, + { + "word": "LOOK", + "phones": [ + { + "ph": "L", + "bg": 141, + "ed": 147 + }, + { + "ph": "UH", + "bg": 147, + "ed": 156 + }, + { + "ph": "K", + "bg": 156, + "ed": 162 + } + ] + }, + { + "word": "AT", + "phones": [ + { + "ph": "AE", + "bg": 162, + "ed": 168 + }, + { + "ph": "T", + "bg": 168, + "ed": 174 + } + ] + }, + { + "word": "GOOGLE", + "phones": [ + { + "ph": "G", + "bg": 174, + "ed": 183 + }, + { + "ph": "UW", + "bg": 183, + "ed": 195 + }, + { + "ph": "G", + "bg": 195, + "ed": 204 + }, + { + "ph": "AH", + "bg": 204, + "ed": 210 + }, + { + "ph": "L", + "bg": 210, + "ed": 216 + } + ] + }, + { + "word": "FACEBOOK", + "phones": [ + { + "ph": "F", + "bg": 216, + "ed": 228 + }, + { + "ph": "EY", + "bg": 228, + "ed": 240 + }, + { + "ph": "S", + "bg": 240, + "ed": 249 + }, + { + "ph": "AH", + "bg": 249, + "ed": 252 + }, + { + "ph": "B", + "bg": 252, + "ed": 255 + }, + { + "ph": "UH", + "bg": 255, + "ed": 267 + }, + { + "ph": "K", + "bg": 267, + "ed": 273 + } + ] + }, + { + "word": "TWITTER", + "phones": [ + { + "ph": "T", + "bg": 273, + "ed": 279 + }, + { + "ph": "W", + "bg": 279, + "ed": 288 + }, + { + "ph": "IH", + "bg": 288, + "ed": 294 + }, + { + "ph": "T", + "bg": 294, + "ed": 300 + }, + { + "ph": "ER", + "bg": 300, + "ed": 357 + } + ] + }, + { + "word": "sil", + "phones": [ + { + "ph": "SIL", + "bg": 357, + "ed": 366 + } + ] + }, + { + "word": "AND", + "phones": [ + { + "ph": "AE", + "bg": 366, + "ed": 378 + }, + { + "ph": "N", + "bg": 378, + "ed": 384 + }, + { + "ph": "D", + "bg": 384, + "ed": 390 + } + ] + }, + { + "word": "OTHER", + "phones": [ + { + "ph": "AH", + "bg": 390, + "ed": 405 + }, + { + "ph": "DH", + "bg": 405, + "ed": 420 + }, + { + "ph": "ER", + "bg": 420, + "ed": 468 + } + ] + }, + { + "word": "sil", + "phones": [ + { + "ph": "SIL", + "bg": 468, + "ed": 474 + } + ] + }, + { + "word": "SOCIAL", + "phones": [ + { + "ph": "S", + "bg": 474, + "ed": 501 + }, + { + "ph": "OW", + "bg": 501, + "ed": 525 + }, + { + "ph": "SH", + "bg": 525, + "ed": 543 + }, + { + "ph": "AH", + "bg": 543, + "ed": 555 + }, + { + "ph": "L", + "bg": 555, + "ed": 588 + } + ] + }, + { + "word": "MEDIA", + "phones": [ + { + "ph": "M", + "bg": 588, + "ed": 603 + }, + { + "ph": "IY", + "bg": 603, + "ed": 618 + }, + { + "ph": "D", + "bg": 618, + "ed": 624 + }, + { + "ph": "IY", + "bg": 624, + "ed": 636 + }, + { + "ph": "AH", + "bg": 636, + "ed": 645 + } + ] + }, + { + "word": "sil", + "phones": [ + { + "ph": "SIL", + "bg": 645, + "ed": 648 + } + ] + }, + { + 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+ "bg": 2244, + "ed": 2250 + }, + { + "ph": "S", + "bg": 2250, + "ed": 2262 + }, + { + "ph": "ER", + "bg": 2262, + "ed": 2280 + }, + { + "ph": "V", + "bg": 2280, + "ed": 2289 + }, + { + "ph": "AH", + "bg": 2289, + "ed": 2292 + }, + { + "ph": "T", + "bg": 2292, + "ed": 2298 + }, + { + "ph": "IH", + "bg": 2298, + "ed": 2304 + }, + { + "ph": "V", + "bg": 2304, + "ed": 2307 + } + ] + }, + { + "word": "VOICES", + "phones": [ + { + "ph": "V", + "bg": 2307, + "ed": 2313 + }, + { + "ph": "OY", + "bg": 2313, + "ed": 2334 + }, + { + "ph": "S", + "bg": 2334, + "ed": 2349 + }, + { + "ph": "IH", + "bg": 2349, + "ed": 2358 + }, + { + "ph": "Z", + "bg": 2358, + "ed": 2394 + } + ] + }, + { + "word": "sil", + "phones": [ + { + "ph": "SIL", + "bg": 2394, + "ed": 2403 + } + ] + }, + { + "word": "THAT", + "phones": [ + { + "ph": "DH", + "bg": 2403, + "ed": 2415 + }, + { + "ph": "AE", + "bg": 2415, + "ed": 2424 + }, + { + "ph": "T", + "bg": 2424, + "ed": 2436 + } + ] + }, + { + "word": "sil", + "phones": [ + { + "ph": "SIL", + "bg": 2436, + "ed": 2583 + } + ] + } +] \ No newline at end of file diff --git a/samples/temp.wav b/samples/temp.wav new file mode 100644 index 0000000000000000000000000000000000000000..dcad5b1fe25626d888134e045348da056b6a1797 Binary files /dev/null and b/samples/temp.wav differ diff --git a/samples/tmp_dir/image.csv b/samples/tmp_dir/image.csv new file mode 100644 index 0000000000000000000000000000000000000000..b435cb5bf374ba1355246a791caa1af500f24ccb --- /dev/null +++ b/samples/tmp_dir/image.csv @@ -0,0 +1,2 @@ +frame,face_id,timestamp,confidence,success,pose_Tx,pose_Ty,pose_Tz,pose_Rx,pose_Ry,pose_Rz +1,0,0.000,0.98,1,-1.2,26.8,246.8,0.017,0.025,0.026 diff --git a/samples/tmp_dir/image_of_details.txt b/samples/tmp_dir/image_of_details.txt new file mode 100644 index 0000000000000000000000000000000000000000..2ad1d35850394ae7db7f7c5636bade6b57b38cbc --- /dev/null +++ b/samples/tmp_dir/image_of_details.txt @@ -0,0 +1,10 @@ +Input:/content/train/image.png +Input full path:/content/train/image.png +Camera parameters:466.667,466.667,256,256 +Output csv:image.csv +Gaze: 0 +AUs: 0 +Landmarks 2D: 0 +Landmarks 3D: 0 +Pose: 1 +Shape parameters: 0 diff --git a/samples/tmp_dir/tangni.csv b/samples/tmp_dir/tangni.csv new file mode 100644 index 0000000000000000000000000000000000000000..f1621edd3af9ac4f9678c907ede3f9f43b900ba2 --- /dev/null +++ b/samples/tmp_dir/tangni.csv @@ -0,0 +1,2 @@ +frame,face_id,timestamp,confidence,success,pose_Tx,pose_Ty,pose_Tz,pose_Rx,pose_Ry,pose_Rz +1,0,0.000,0.98,1,-13.6,37.1,257.5,-0.109,0.128,0.012 diff --git a/samples/tmp_dir/tangni_of_details.txt b/samples/tmp_dir/tangni_of_details.txt new file mode 100644 index 0000000000000000000000000000000000000000..f125db9d8405b26eee850587c62c81ea2d4e215d --- /dev/null +++ b/samples/tmp_dir/tangni_of_details.txt @@ -0,0 +1,10 @@ +Input:/content/one-shot-talking-face/samples/imgs/tangni.jpg +Input full path:/content/one-shot-talking-face/samples/imgs/tangni.jpg +Camera parameters:233.333,233.333,128,128 +Output csv:tangni.csv +Gaze: 0 +AUs: 0 +Landmarks 2D: 0 +Landmarks 3D: 0 +Pose: 1 +Shape parameters: 0 diff --git a/sync_batchnorm/__init__.py b/sync_batchnorm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bc8709d92c610b36e0bcbd7da20c1eb41dc8cfcf --- /dev/null +++ b/sync_batchnorm/__init__.py @@ -0,0 +1,12 @@ +# -*- coding: utf-8 -*- +# File : __init__.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d +from .replicate import DataParallelWithCallback, patch_replication_callback diff --git a/sync_batchnorm/batchnorm.py b/sync_batchnorm/batchnorm.py new file mode 100644 index 0000000000000000000000000000000000000000..5f4e763f0366dffa10320116413f8c7181a8aeb1 --- /dev/null +++ b/sync_batchnorm/batchnorm.py @@ -0,0 +1,315 @@ +# -*- coding: utf-8 -*- +# File : batchnorm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import collections + +import torch +import torch.nn.functional as F + +from torch.nn.modules.batchnorm import _BatchNorm +from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast + +from .comm import SyncMaster + +__all__ = ['SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d'] + + +def _sum_ft(tensor): + """sum over the first and last dimention""" + return tensor.sum(dim=0).sum(dim=-1) + + +def _unsqueeze_ft(tensor): + """add new dementions at the front and the tail""" + return tensor.unsqueeze(0).unsqueeze(-1) + + +_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size']) +_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std']) + + +class _SynchronizedBatchNorm(_BatchNorm): + def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True): + super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine) + + self._sync_master = SyncMaster(self._data_parallel_master) + + self._is_parallel = False + self._parallel_id = None + self._slave_pipe = None + + def forward(self, input): + # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation. + if not (self._is_parallel and self.training): + return F.batch_norm( + input, self.running_mean, self.running_var, self.weight, self.bias, + self.training, self.momentum, self.eps) + + # Resize the input to (B, C, -1). + input_shape = input.size() + input = input.view(input.size(0), self.num_features, -1) + + # Compute the sum and square-sum. + sum_size = input.size(0) * input.size(2) + input_sum = _sum_ft(input) + input_ssum = _sum_ft(input ** 2) + + # Reduce-and-broadcast the statistics. + if self._parallel_id == 0: + mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size)) + else: + mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size)) + + # Compute the output. + if self.affine: + # MJY:: Fuse the multiplication for speed. + output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias) + else: + output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std) + + # Reshape it. + return output.view(input_shape) + + def __data_parallel_replicate__(self, ctx, copy_id): + self._is_parallel = True + self._parallel_id = copy_id + + # parallel_id == 0 means master device. + if self._parallel_id == 0: + ctx.sync_master = self._sync_master + else: + self._slave_pipe = ctx.sync_master.register_slave(copy_id) + + def _data_parallel_master(self, intermediates): + """Reduce the sum and square-sum, compute the statistics, and broadcast it.""" + + # Always using same "device order" makes the ReduceAdd operation faster. + # Thanks to:: Tete Xiao (http://tetexiao.com/) + intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device()) + + to_reduce = [i[1][:2] for i in intermediates] + to_reduce = [j for i in to_reduce for j in i] # flatten + target_gpus = [i[1].sum.get_device() for i in intermediates] + + sum_size = sum([i[1].sum_size for i in intermediates]) + sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce) + mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size) + + broadcasted = Broadcast.apply(target_gpus, mean, inv_std) + + outputs = [] + for i, rec in enumerate(intermediates): + outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2]))) + + return outputs + + def _compute_mean_std(self, sum_, ssum, size): + """Compute the mean and standard-deviation with sum and square-sum. This method + also maintains the moving average on the master device.""" + assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.' + mean = sum_ / size + sumvar = ssum - sum_ * mean + unbias_var = sumvar / (size - 1) + bias_var = sumvar / size + + self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean.data + self.running_var = (1 - self.momentum) * self.running_var + self.momentum * unbias_var.data + + return mean, bias_var.clamp(self.eps) ** -0.5 + + +class SynchronizedBatchNorm1d(_SynchronizedBatchNorm): + r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a + mini-batch. + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm1d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm + + Args: + num_features: num_features from an expected input of size + `batch_size x num_features [x width]` + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape: + - Input: :math:`(N, C)` or :math:`(N, C, L)` + - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm1d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm1d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 2 and input.dim() != 3: + raise ValueError('expected 2D or 3D input (got {}D input)' + .format(input.dim())) + super(SynchronizedBatchNorm1d, self)._check_input_dim(input) + + +class SynchronizedBatchNorm2d(_SynchronizedBatchNorm): + r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch + of 3d inputs + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm2d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm + + Args: + num_features: num_features from an expected input of + size batch_size x num_features x height x width + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape: + - Input: :math:`(N, C, H, W)` + - Output: :math:`(N, C, H, W)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm2d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm2d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 4: + raise ValueError('expected 4D input (got {}D input)' + .format(input.dim())) + super(SynchronizedBatchNorm2d, self)._check_input_dim(input) + + +class SynchronizedBatchNorm3d(_SynchronizedBatchNorm): + r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch + of 4d inputs + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm3d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm + or Spatio-temporal BatchNorm + + Args: + num_features: num_features from an expected input of + size batch_size x num_features x depth x height x width + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape: + - Input: :math:`(N, C, D, H, W)` + - Output: :math:`(N, C, D, H, W)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm3d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm3d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 5: + raise ValueError('expected 5D input (got {}D input)' + .format(input.dim())) + super(SynchronizedBatchNorm3d, self)._check_input_dim(input) diff --git a/sync_batchnorm/comm.py b/sync_batchnorm/comm.py new file mode 100644 index 0000000000000000000000000000000000000000..922f8c4a3adaa9b32fdcaef09583be03b0d7eb2b --- /dev/null +++ b/sync_batchnorm/comm.py @@ -0,0 +1,137 @@ +# -*- coding: utf-8 -*- +# File : comm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import queue +import collections +import threading + +__all__ = ['FutureResult', 'SlavePipe', 'SyncMaster'] + + +class FutureResult(object): + """A thread-safe future implementation. Used only as one-to-one pipe.""" + + def __init__(self): + self._result = None + self._lock = threading.Lock() + self._cond = threading.Condition(self._lock) + + def put(self, result): + with self._lock: + assert self._result is None, 'Previous result has\'t been fetched.' + self._result = result + self._cond.notify() + + def get(self): + with self._lock: + if self._result is None: + self._cond.wait() + + res = self._result + self._result = None + return res + + +_MasterRegistry = collections.namedtuple('MasterRegistry', ['result']) +_SlavePipeBase = collections.namedtuple('_SlavePipeBase', ['identifier', 'queue', 'result']) + + +class SlavePipe(_SlavePipeBase): + """Pipe for master-slave communication.""" + + def run_slave(self, msg): + self.queue.put((self.identifier, msg)) + ret = self.result.get() + self.queue.put(True) + return ret + + +class SyncMaster(object): + """An abstract `SyncMaster` object. + + - During the replication, as the data parallel will trigger an callback of each module, all slave devices should + call `register(id)` and obtain an `SlavePipe` to communicate with the master. + - During the forward pass, master device invokes `run_master`, all messages from slave devices will be collected, + and passed to a registered callback. + - After receiving the messages, the master device should gather the information and determine to message passed + back to each slave devices. + """ + + def __init__(self, master_callback): + """ + + Args: + master_callback: a callback to be invoked after having collected messages from slave devices. + """ + self._master_callback = master_callback + self._queue = queue.Queue() + self._registry = collections.OrderedDict() + self._activated = False + + def __getstate__(self): + return {'master_callback': self._master_callback} + + def __setstate__(self, state): + self.__init__(state['master_callback']) + + def register_slave(self, identifier): + """ + Register an slave device. + + Args: + identifier: an identifier, usually is the device id. + + Returns: a `SlavePipe` object which can be used to communicate with the master device. + + """ + if self._activated: + assert self._queue.empty(), 'Queue is not clean before next initialization.' + self._activated = False + self._registry.clear() + future = FutureResult() + self._registry[identifier] = _MasterRegistry(future) + return SlavePipe(identifier, self._queue, future) + + def run_master(self, master_msg): + """ + Main entry for the master device in each forward pass. + The messages were first collected from each devices (including the master device), and then + an callback will be invoked to compute the message to be sent back to each devices + (including the master device). + + Args: + master_msg: the message that the master want to send to itself. This will be placed as the first + message when calling `master_callback`. For detailed usage, see `_SynchronizedBatchNorm` for an example. + + Returns: the message to be sent back to the master device. + + """ + self._activated = True + + intermediates = [(0, master_msg)] + for i in range(self.nr_slaves): + intermediates.append(self._queue.get()) + + results = self._master_callback(intermediates) + assert results[0][0] == 0, 'The first result should belongs to the master.' + + for i, res in results: + if i == 0: + continue + self._registry[i].result.put(res) + + for i in range(self.nr_slaves): + assert self._queue.get() is True + + return results[0][1] + + @property + def nr_slaves(self): + return len(self._registry) diff --git a/sync_batchnorm/replicate.py b/sync_batchnorm/replicate.py new file mode 100644 index 0000000000000000000000000000000000000000..b71c7b8ed51a1d6c55b1f753bdd8d90bad79bd06 --- /dev/null +++ b/sync_batchnorm/replicate.py @@ -0,0 +1,94 @@ +# -*- coding: utf-8 -*- +# File : replicate.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import functools + +from torch.nn.parallel.data_parallel import DataParallel + +__all__ = [ + 'CallbackContext', + 'execute_replication_callbacks', + 'DataParallelWithCallback', + 'patch_replication_callback' +] + + +class CallbackContext(object): + pass + + +def execute_replication_callbacks(modules): + """ + Execute an replication callback `__data_parallel_replicate__` on each module created by original replication. + + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + + Note that, as all modules are isomorphism, we assign each sub-module with a context + (shared among multiple copies of this module on different devices). + Through this context, different copies can share some information. + + We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback + of any slave copies. + """ + master_copy = modules[0] + nr_modules = len(list(master_copy.modules())) + ctxs = [CallbackContext() for _ in range(nr_modules)] + + for i, module in enumerate(modules): + for j, m in enumerate(module.modules()): + if hasattr(m, '__data_parallel_replicate__'): + m.__data_parallel_replicate__(ctxs[j], i) + + +class DataParallelWithCallback(DataParallel): + """ + Data Parallel with a replication callback. + + An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by + original `replicate` function. + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + # sync_bn.__data_parallel_replicate__ will be invoked. + """ + + def replicate(self, module, device_ids): + modules = super(DataParallelWithCallback, self).replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + +def patch_replication_callback(data_parallel): + """ + Monkey-patch an existing `DataParallel` object. Add the replication callback. + Useful when you have customized `DataParallel` implementation. + + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallel(sync_bn, device_ids=[0, 1]) + > patch_replication_callback(sync_bn) + # this is equivalent to + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + """ + + assert isinstance(data_parallel, DataParallel) + + old_replicate = data_parallel.replicate + + @functools.wraps(old_replicate) + def new_replicate(module, device_ids): + modules = old_replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + data_parallel.replicate = new_replicate diff --git a/sync_batchnorm/unittest.py b/sync_batchnorm/unittest.py new file mode 100644 index 0000000000000000000000000000000000000000..0675c022e4ba85d38d1f813490f6740150909524 --- /dev/null +++ b/sync_batchnorm/unittest.py @@ -0,0 +1,29 @@ +# -*- coding: utf-8 -*- +# File : unittest.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import unittest + +import numpy as np +from torch.autograd import Variable + + +def as_numpy(v): + if isinstance(v, Variable): + v = v.data + return v.cpu().numpy() + + +class TorchTestCase(unittest.TestCase): + def assertTensorClose(self, a, b, atol=1e-3, rtol=1e-3): + npa, npb = as_numpy(a), as_numpy(b) + self.assertTrue( + np.allclose(npa, npb, atol=atol), + 'Tensor close check failed\n{}\n{}\nadiff={}, rdiff={}'.format(a, b, np.abs(npa - npb).max(), np.abs((npa - npb) / np.fmax(npa, 1e-5)).max()) + ) diff --git a/test_script.py b/test_script.py new file mode 100644 index 0000000000000000000000000000000000000000..4def21e772dc49bbf1f53c008ffdbb597a7c7714 --- /dev/null +++ b/test_script.py @@ -0,0 +1,180 @@ +import os +import numpy as np +import torch +import yaml +from models.generator import OcclusionAwareGenerator +from models.keypoint_detector import KPDetector +import argparse +import imageio +from models.util import draw_annotation_box +from models.transformer import Audio2kpTransformer +from scipy.io import wavfile +from tools.interface import read_img,get_img_pose,get_pose_from_audio,get_audio_feature_from_audio,\ + parse_phoneme_file,load_ckpt +import config + +def normalize_kp(kp_source, kp_driving, kp_driving_initial, + use_relative_movement=True, use_relative_jacobian=True): + + kp_new = {k: v for k, v in kp_driving.items()} + if use_relative_movement: + kp_value_diff = (kp_driving['value'] - kp_driving_initial['value']) + # kp_value_diff *= adapt_movement_scale + kp_new['value'] = kp_value_diff + kp_source['value'] + + if use_relative_jacobian: + jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) + kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) + + return kp_new + + +def test_with_input_audio_and_image(img_path, audio_path,phs, generator_ckpt, audio2pose_ckpt, save_dir="samples/results"): + with open("config_file/vox-256.yaml") as f: + config = yaml.full_load(f) + # temp_audio = audio_path + # print(audio_path) + cur_path = os.getcwd() + + sr,_ = wavfile.read(audio_path) + if sr!=16000: + temp_audio = os.path.join(cur_path,"samples","temp.wav") + command = "ffmpeg -y -i %s -async 1 -ac 1 -vn -acodec pcm_s16le -ar 16000 %s" % (audio_path, temp_audio) + os.system(command) + else: + temp_audio = audio_path + + + opt = argparse.Namespace(**yaml.full_load(open("config_file/audio2kp.yaml"))) + + img = read_img(img_path).cuda() + + first_pose = get_img_pose(img_path)#.cuda() + + audio_feature = get_audio_feature_from_audio(temp_audio) + frames = len(audio_feature) // 4 + frames = min(frames,len(phs["phone_list"])) + + tp = np.zeros([256, 256], dtype=np.float32) + draw_annotation_box(tp, first_pose[:3], first_pose[3:]) + tp = torch.from_numpy(tp).unsqueeze(0).unsqueeze(0).cuda() + ref_pose = get_pose_from_audio(tp, audio_feature, audio2pose_ckpt) + torch.cuda.empty_cache() + trans_seq = ref_pose[:, 3:] + rot_seq = ref_pose[:, :3] + + + + audio_seq = audio_feature#[40:] + ph_seq = phs["phone_list"] + + + ph_frames = [] + audio_frames = [] + pose_frames = [] + name_len = frames + + pad = np.zeros((4, audio_seq.shape[1]), dtype=np.float32) + + for rid in range(0, frames): + ph = [] + audio = [] + pose = [] + for i in range(rid - opt.num_w, rid + opt.num_w + 1): + if i < 0: + rot = rot_seq[0] + trans = trans_seq[0] + ph.append(31) + audio.append(pad) + elif i >= name_len: + ph.append(31) + rot = rot_seq[name_len - 1] + trans = trans_seq[name_len - 1] + audio.append(pad) + else: + ph.append(ph_seq[i]) + rot = rot_seq[i] + trans = trans_seq[i] + audio.append(audio_seq[i * 4:i * 4 + 4]) + tmp_pose = np.zeros([256, 256]) + draw_annotation_box(tmp_pose, np.array(rot), np.array(trans)) + pose.append(tmp_pose) + + ph_frames.append(ph) + audio_frames.append(audio) + pose_frames.append(pose) + + audio_f = torch.from_numpy(np.array(audio_frames,dtype=np.float32)).unsqueeze(0) + poses = torch.from_numpy(np.array(pose_frames, dtype=np.float32)).unsqueeze(0) + ph_frames = torch.from_numpy(np.array(ph_frames)).unsqueeze(0) + bs = audio_f.shape[1] + predictions_gen = [] + + kp_detector = KPDetector(**config['model_params']['kp_detector_params'], + **config['model_params']['common_params']) + generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], + **config['model_params']['common_params']) + kp_detector = kp_detector.cuda() + generator = generator.cuda() + + ph2kp = Audio2kpTransformer(opt).cuda() + + load_ckpt(generator_ckpt, kp_detector=kp_detector, generator=generator,ph2kp=ph2kp) + + + ph2kp.eval() + generator.eval() + kp_detector.eval() + + with torch.no_grad(): + for frame_idx in range(bs): + t = {} + + t["audio"] = audio_f[:, frame_idx].cuda() + t["pose"] = poses[:, frame_idx].cuda() + t["ph"] = ph_frames[:,frame_idx].cuda() + t["id_img"] = img + + kp_gen_source = kp_detector(img, True) + + gen_kp = ph2kp(t,kp_gen_source) + if frame_idx == 0: + drive_first = gen_kp + + norm = normalize_kp(kp_source=kp_gen_source, kp_driving=gen_kp, kp_driving_initial=drive_first) + out_gen = generator(img, kp_source=kp_gen_source, kp_driving=norm) + + predictions_gen.append( + (np.transpose(out_gen['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0] * 255).astype(np.uint8)) + + + log_dir = save_dir + os.makedirs(os.path.join(log_dir, "temp"),exist_ok=True) + + f_name = os.path.basename(img_path)[:-4] + "_" + os.path.basename(audio_path)[:-4] + ".mp4" + # kwargs = {'duration': 1. / 25.0} + video_path = os.path.join(log_dir, "temp", f_name) + print("save video to: ", video_path) + imageio.mimsave(video_path, predictions_gen, fps=25.0) + + # audio_path = os.path.join(audio_dir, x['name'][0].replace(".mp4", ".wav")) + save_video = os.path.join(log_dir, f_name) + cmd = r'ffmpeg -y -i "%s" -i "%s" -vcodec copy "%s"' % (video_path, audio_path, save_video) + os.system(cmd) + os.remove(video_path) + + + + + + +if __name__ == '__main__': + argparser = argparse.ArgumentParser() + argparser.add_argument("--img_path", type=str, default=None, help="path of the input image ( .jpg ), preprocessed by image_preprocess.py") + argparser.add_argument("--audio_path", type=str, default=None, help="path of the input audio ( .wav )") + argparser.add_argument("--phoneme_path", type=str, default=None, help="path of the input phoneme. It should be note that the phoneme must be consistent with the input audio") + argparser.add_argument("--save_dir", type=str, default="samples/results", help="path of the output video") + args = argparser.parse_args() + + phoneme = parse_phoneme_file(args.phoneme_path) + test_with_input_audio_and_image(args.img_path,args.audio_path,phoneme,config.GENERATOR_CKPT,config.AUDIO2POSE_CKPT,args.save_dir) diff --git a/tools/interface.py b/tools/interface.py new file mode 100644 index 0000000000000000000000000000000000000000..8a08263ef34b22a6d02f35c758c9dc68a2a8ac94 --- /dev/null +++ b/tools/interface.py @@ -0,0 +1,190 @@ +import os + +from skimage import io,img_as_float32 +import cv2 +import torch +import numpy as np +import subprocess +import pandas +from models.audio2pose import audio2poseLSTM +from scipy.io import wavfile +import python_speech_features +import pyworld +import config +import json +from scipy.interpolate import interp1d + +def inter_pitch(y,y_flag): + frame_num = y.shape[0] + i = 0 + last = -1 + while(i= frame_num: + break + elif last == -1: + y[:i] = y[i] + else: + inter_num = i-last+1 + fy = np.array([y[last],y[i]]) + fx = np.linspace(0, 1, num=2) + f = interp1d(fx,fy) + fx_new = np.linspace(0,1,inter_num) + fy_new = f(fx_new) + y[last+1:i] = fy_new[1:-1] + last = i + i+=1 + + else: + last = i + i+=1 + return y + + +def load_ckpt(checkpoint_path, generator = None, kp_detector = None, ph2kp = None): + checkpoint = torch.load(checkpoint_path) + if ph2kp is not None: + ph2kp.load_state_dict(checkpoint['ph2kp']) + if generator is not None: + generator.load_state_dict(checkpoint['generator']) + if kp_detector is not None: + kp_detector.load_state_dict(checkpoint['kp_detector']) + +def get_img_pose(img_path): + processor = config.OPENFACE_POSE_EXTRACTOR_PATH + + tmp_dir = "samples/tmp_dir" + os.makedirs((tmp_dir),exist_ok=True) + subprocess.call([processor, "-f", img_path, "-out_dir", tmp_dir, "-pose"]) + + img_file = os.path.basename(img_path)[:-4]+".csv" + csv_file = os.path.join(tmp_dir,img_file) + pos_data = pandas.read_csv(csv_file) + i = 0 + pose = [pos_data["pose_Rx"][i], pos_data["pose_Ry"][i], pos_data["pose_Rz"][i],pos_data["pose_Tx"][i], pos_data["pose_Ty"][i], pos_data["pose_Tz"][i]] + # pose = [pose] + pose = np.array(pose,dtype=np.float32) + return pose + +def read_img(path): + img = io.imread(path)[:,:,:3] + img = cv2.resize(img, (256, 256)) + # img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + img = np.array(img_as_float32(img)) + img = img.transpose((2, 0, 1)) + img = torch.from_numpy(img).unsqueeze(0) + return img + + +def parse_phoneme_file(phoneme_path,use_index = True): + with open(phoneme_path,'r') as f: + result_text = json.load(f) + frame_num = int(result_text[-1]['phones'][-1]['ed']/100*25) + phoneset_list = [] + index = 0 + + word_len = len(result_text) + word_index = 0 + phone_index = 0 + cur_phone_list = result_text[0]["phones"] + phone_len = len(cur_phone_list) + cur_end = cur_phone_list[0]["ed"] + + phone_list = [] + + phoneset_list.append(cur_phone_list[0]["ph"]) + i = 0 + while i < frame_num: + if i * 4 < cur_end: + phone_list.append(cur_phone_list[phone_index]["ph"]) + i += 1 + else: + phone_index += 1 + if phone_index >= phone_len: + word_index += 1 + if word_index >= word_len: + phone_list.append(cur_phone_list[-1]["ph"]) + i += 1 + else: + phone_index = 0 + cur_phone_list = result_text[word_index]["phones"] + phone_len = len(cur_phone_list) + cur_end = cur_phone_list[phone_index]["ed"] + phoneset_list.append(cur_phone_list[phone_index]["ph"]) + index += 1 + else: + # print(word_index,phone_index) + cur_end = cur_phone_list[phone_index]["ed"] + phoneset_list.append(cur_phone_list[phone_index]["ph"]) + index += 1 + + with open("phindex.json") as f: + ph2index = json.load(f) + if use_index: + phone_list = [ph2index[p] for p in phone_list] + saves = {"phone_list": phone_list} + + return saves + +def get_audio_feature_from_audio(audio_path): + sample_rate, audio = wavfile.read(audio_path) + if len(audio.shape) == 2: + if np.min(audio[:, 0]) <= 0: + audio = audio[:, 1] + else: + audio = audio[:, 0] + + audio = audio - np.mean(audio) + audio = audio / np.max(np.abs(audio)) + a = python_speech_features.mfcc(audio, sample_rate) + b = python_speech_features.logfbank(audio, sample_rate) + c, _ = pyworld.harvest(audio, sample_rate, frame_period=10) + c_flag = (c == 0.0) ^ 1 + c = inter_pitch(c, c_flag) + c = np.expand_dims(c, axis=1) + c_flag = np.expand_dims(c_flag, axis=1) + frame_num = np.min([a.shape[0], b.shape[0], c.shape[0]]) + + cat = np.concatenate([a[:frame_num], b[:frame_num], c[:frame_num], c_flag[:frame_num]], axis=1) + return cat + +def get_pose_from_audio(img,audio,audio2pose): + + num_frame = len(audio) // 4 + + minv = np.array([-0.6, -0.6, -0.6, -128.0, -128.0, 128.0], dtype=np.float32) + maxv = np.array([0.6, 0.6, 0.6, 128.0, 128.0, 384.0], dtype=np.float32) + generator = audio2poseLSTM().cuda().eval() + + ckpt_para = torch.load(audio2pose) + + generator.load_state_dict(ckpt_para["generator"]) + generator.eval() + + + audio_seq = [] + for i in range(num_frame): + audio_seq.append(audio[i*4:i*4+4]) + + audio = torch.from_numpy(np.array(audio_seq,dtype=np.float32)).unsqueeze(0).cuda() + + x = {} + x ["img"] = img + x["audio"] = audio + poses = generator(x) + + poses = poses.cpu().data.numpy()[0] + poses = (poses+1)/2*(maxv-minv)+minv + + return poses +