From 9ae5bdeb5c1f2e368ba7b1aaac6cedc955509741 Mon Sep 17 00:00:00 2001 From: bengbeng-bomb <10098452+bit_fuyuxuan@user.noreply.gitee.com> Date: Mon, 15 Jan 2024 20:20:20 +0800 Subject: [PATCH 1/5] test --- README1.md | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 README1.md diff --git a/README1.md b/README1.md new file mode 100644 index 00000000..1078dd30 --- /dev/null +++ b/README1.md @@ -0,0 +1,7 @@ + +### test From ed7f7be3486e7b3bb62106428c49bbfac622a3f3 Mon Sep 17 00:00:00 2001 From: bengbeng-bomb <10098452+bit_fuyuxuan@user.noreply.gitee.com> Date: Mon, 15 Jan 2024 20:25:17 +0800 Subject: [PATCH 2/5] test --- README1.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README1.md b/README1.md index 1078dd30..924377f6 100644 --- a/README1.md +++ b/README1.md @@ -2,6 +2,6 @@ * @Description: * @Author: Fu Yuxuan * @Date: 2024-01-15 20:19:14 - * @LastEditTime: 2024-01-15 20:19:38 + * @LastEditTime: 2024-01-15 20:23:03 --> ### test From 70bd4bc585735d630095ef72934b0462500bea62 Mon Sep 17 00:00:00 2001 From: bengbeng-bomb <10098452+bit_fuyuxuan@user.noreply.gitee.com> Date: Sun, 21 Jan 2024 15:43:35 +0800 Subject: [PATCH 3/5] =?UTF-8?q?2024/1/21=20=E5=AE=8C=E6=88=90=E6=A8=A1?= =?UTF-8?q?=E5=9E=8B=E7=9A=84=E8=AE=AD=E7=BB=83=E5=92=8C=E9=AA=8C=E8=AF=81?= =?UTF-8?q?=EF=BC=8C=E5=9F=BA=E6=9C=AC=E4=B8=8A=E5=B0=86adaptive-VC?= =?UTF-8?q?=E9=A1=B9=E7=9B=AE=E4=BB=A3=E7=A0=81=E6=8C=89=E7=85=A7=E6=A8=A1?= =?UTF-8?q?=E6=9D=BF=E8=A6=81=E6=B1=82=E6=95=B4=E5=90=88=E5=88=B0talkingfa?= =?UTF-8?q?ce-toolkit=E6=A1=86=E6=9E=B6=EF=BC=88=E5=88=A0=E9=99=A4?= =?UTF-8?q?=E4=BA=86=E9=83=A8=E5=88=86=E4=B8=8E=E8=AF=A5=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=E6=97=A0=E5=85=B3=E7=9A=84=E5=85=B6=E4=BB=96=E6=96=B9=E9=9D=A2?= =?UTF-8?q?=E7=9A=84=E4=BB=A3=E7=A0=81=EF=BC=89?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README1.md | 7 - checkpoints/README.md | 15 - dataset/VCTK-Corpus/README.txt | 68 + 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a/README1.md +++ /dev/null @@ -1,7 +0,0 @@ - -### test diff --git a/checkpoints/README.md b/checkpoints/README.md index 0a1432d6..8b137891 100644 --- a/checkpoints/README.md +++ b/checkpoints/README.md @@ -1,16 +1 @@ -这个文件夹中保存的是，模型训练或验证过程中用到的一些额外的预训练权重如： -- wav2lip中用到的syncnet权重 -- 计算合成视频lip-audio同步LSE用到的syncnet-v2权重 -- ....... -目录结构为： -``` -checkpoints/ - -├── LSE -| ├── syncnet_v2.model () - -├── Wav2Lip -| ├── lipsync_expert.pth () - -``` diff --git a/dataset/VCTK-Corpus/README.txt b/dataset/VCTK-Corpus/README.txt new file mode 100644 index 00000000..9a554681 --- /dev/null +++ b/dataset/VCTK-Corpus/README.txt @@ -0,0 +1,68 @@ +--------------------------------------------------------------------- + CSTR VCTK Corpus + English Multi-speaker Corpus for CSTR Voice Cloning Toolkit + + (Version 0.80) + RELEASE August 2012 + The Centre for Speech Technology Research + University of Edinburgh + Copyright (c) 2012 + + Junichi Yamagishi + jyamagis@inf.ed.ac.uk +--------------------------------------------------------------------- + +Overview + +This CSTR VCTK Corpus includes speech data uttered by 109 English +speakers with various accents. Each speaker reads out about 400 +sentences, which were selected from a newspaper, the rainbow passage +and an elicitation paragraph used for the speech accent archive. + +The newspaper texts were taken from Herald Glasgow, with permission +from Herald & Times Group. Each speaker has a different set of the +newspaper texts selected based a greedy algorithm that increases the +contextual and phonetic coverage. + +The rainbow passage and elicitation paragraph are the same for all +speakers. The rainbow passage can be found at International Dialects +of English Archive: +(http://web.ku.edu/~idea/readings/rainbow.htm). The elicitation +paragraph is identical to the one used for the speech accent archive +(http://accent.gmu.edu). The details of the the speech accent archive +can be found at +http://www.ualberta.ca/~aacl2009/PDFs/WeinbergerKunath2009AACL.pdf + +All speech data was recorded using an identical recording setup: an +omni-directional microphone (DPA 4035), 96kHz sampling frequency at 24 +bits and in a hemi-anechoic chamber of the University of +Edinburgh. All recordings were converted into 16 bits, were downsampled +to 48 kHz based on STPK, and were manually end-pointed. + +This corpus is aimed for HMM-based text-to-speech synthesis systems, +especially for speaker-adaptive HMM-based speech synthesis that uses +average voice models trained on multiple speakers and speaker +adaptation technologies. + +COPYING + +This corpus is licensed under Open Data Commons Attribution License +(ODC-By) v1.0. + +http://opendatacommons.org/licenses/by/1.0/ +http://opendatacommons.org/licenses/by/summary/ + + +ACKNOWLEDGEMENTS + +The CSTR VCTK Corpus was constructed by: + + Christophe Veaux (University of Edinburgh) + Junichi Yamagishi (University of Edinburgh) + Kirsten MacDonald + +The research leading to these results was partly funded from EPSRC +grants EP/I031022/1 (NST) and EP/J002526/1 (CAF), from the RSE-NSFC +grant (61111130120), and from the JST CREST (uDialogue). + + diff --git a/dataset/preprocessed_data/attr.pkl 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["p274_368.wav", 7], ["p326_109.wav", 35], ["p247_023.wav", 325], ["p326_119.wav", 3], ["p364_180.wav", 3], ["p230_007.wav", 36], ["p307_012.wav", 104], ["p238_097.wav", 12], ["p307_355.wav", 10], ["p238_069.wav", 22], ["p260_230.wav", 11], ["p285_134.wav", 88], ["p326_060.wav", 14], ["p239_155.wav", 2], ["p285_307.wav", 51], ["p345_389.wav", 4], ["p269_228.wav", 53], ["p292_155.wav", 26], ["p247_278.wav", 5], ["p341_273.wav", 1], ["p305_342.wav", 3], ["p326_235.wav", 5], ["p292_286.wav", 16], ["p299_135.wav", 1], ["p269_085.wav", 45], ["p238_137.wav", 10], ["p285_016.wav", 156], ["p326_327.wav", 13], ["p294_051.wav", 8], ["p274_066.wav", 11]] \ No newline at end of file diff --git a/environment.yml b/environment.yml deleted file mode 100644 index 09a8595b..00000000 --- a/environment.yml +++ /dev/null @@ -1,138 +0,0 @@ -name: torch38 -channels: - - defaults -dependencies: - - _libgcc_mutex=0.1=main - - _openmp_mutex=5.1=1_gnu - - ca-certificates=2023.08.22=h06a4308_0 - - ld_impl_linux-64=2.38=h1181459_1 - - libffi=3.4.4=h6a678d5_0 - - libgcc-ng=11.2.0=h1234567_1 - - libgomp=11.2.0=h1234567_1 - - libstdcxx-ng=11.2.0=h1234567_1 - - ncurses=6.4=h6a678d5_0 - - openssl=3.0.12=h7f8727e_0 - - pip=23.3=py38h06a4308_0 - - python=3.8.18=h955ad1f_0 - - readline=8.2=h5eee18b_0 - - setuptools=68.0.0=py38h06a4308_0 - - sqlite=3.41.2=h5eee18b_0 - - tk=8.6.12=h1ccaba5_0 - - wheel=0.41.2=py38h06a4308_0 - - xz=5.4.2=h5eee18b_0 - - zlib=1.2.13=h5eee18b_0 - - pip: - - absl-py==2.0.0 - - addict==2.4.0 - - aiosignal==1.3.1 - - appdirs==1.4.4 - - attrs==23.1.0 - - audioread==3.0.1 - - basicsr==1.3.4.7 - - cachetools==5.3.2 - - certifi==2020.12.5 - - cffi==1.16.0 - - charset-normalizer==3.3.2 - - click==8.1.7 - - cloudpickle==3.0.0 - - colorama==0.4.6 - - colorlog==6.7.0 - - contourpy==1.1.1 - - cycler==0.12.1 - - decorator==5.1.1 - - dlib==19.22.1 - - docker-pycreds==0.4.0 - - face-alignment==1.3.5 - - ffmpeg==1.4 - - filelock==3.13.1 - - fonttools==4.44.0 - - frozenlist==1.4.0 - - future==0.18.3 - - gitdb==4.0.11 - - gitpython==3.1.40 - - glob2==0.7 - - google-auth==2.23.4 - - google-auth-oauthlib==0.4.6 - - grpcio==1.59.2 - - hyperopt==0.2.5 - - idna==3.4 - - imageio==2.9.0 - - imageio-ffmpeg==0.4.5 - - importlib-metadata==6.8.0 - - importlib-resources==6.1.0 - - joblib==1.3.2 - - jsonschema==4.19.2 - - jsonschema-specifications==2023.7.1 - - kiwisolver==1.4.5 - - kornia==0.5.5 - - lazy-loader==0.3 - - librosa==0.10.1 - - llvmlite==0.37.0 - - lmdb==1.2.1 - - lws==1.2.7 - - markdown==3.5.1 - - markupsafe==2.1.3 - - matplotlib==3.6.3 - - msgpack==1.0.7 - - networkx==3.1 - - numba==0.54.1 - - numpy==1.20.3 - - oauthlib==3.2.2 - - opencv-python==3.4.9.33 - - packaging==23.2 - - pandas==1.3.4 - - pathtools==0.1.2 - - pillow==6.2.1 - - pkgutil-resolve-name==1.3.10 - - platformdirs==3.11.0 - - plotly==5.18.0 - - pooch==1.8.0 - - protobuf==4.25.0 - - psutil==5.9.6 - - pyasn1==0.5.0 - - pyasn1-modules==0.3.0 - - pycparser==2.21 - - pyparsing==3.1.1 - - python-dateutil==2.8.2 - - python-speech-features==0.6 - - pytorch-fid==0.3.0 - - pytz==2023.3.post1 - - pywavelets==1.4.1 - - pyyaml==5.3.1 - - ray==2.6.3 - - referencing==0.30.2 - - requests==2.31.0 - - requests-oauthlib==1.3.1 - - rpds-py==0.12.0 - - rsa==4.9 - - scikit-image==0.16.2 - - scikit-learn==1.3.2 - - scipy==1.5.0 - - sentry-sdk==1.34.0 - - setproctitle==1.3.3 - - six==1.16.0 - - smmap==5.0.1 - - soundfile==0.12.1 - - soxr==0.3.7 - - tabulate==0.9.0 - - tb-nightly==2.12.0a20230126 - - tenacity==8.2.3 - - tensorboard==2.7.0 - - tensorboard-data-server==0.6.1 - - tensorboard-plugin-wit==1.8.1 - - texttable==1.7.0 - - thop==0.1.1-2209072238 - - threadpoolctl==3.2.0 - - tomli==2.0.1 - - torch==1.13.1+cu116 - - torchaudio==0.13.1+cu116 - - torchvision==0.14.1+cu116 - - tqdm==4.66.1 - - trimesh==3.9.20 - - typing-extensions==4.8.0 - - tzdata==2023.3 - - urllib3==2.0.7 - - wandb==0.15.12 - - werkzeug==3.0.1 - - yapf==0.40.2 - - zipp==3.17.0 diff --git a/requirement.txt b/requirement.txt new file mode 100644 index 00000000..2df89bb9 --- /dev/null +++ b/requirement.txt @@ -0,0 +1,51 @@ +absl-py==1.4.0 +astor==0.8.1 +audioread==3.0.1 +cached-property==1.5.2 +certifi==2020.6.20 +cffi==1.15.1 +click==8.0.4 +dataclasses==0.8 +decorator==5.1.1 +editdistance==0.6.2 +gast==0.5.4 +grpcio==1.48.2 +h5py==3.1.0 +importlib-metadata==4.8.3 +joblib==1.1.1 +Keras-Applications==1.0.8 +Keras-Preprocessing==1.1.2 +librosa==0.7.2 +llvmlite==0.31.0 +Markdown==3.3.7 +mock==5.1.0 +numba==0.48.0 +numpy==1.19.5 +packaging==21.3 +Pillow==8.4.0 +pip==21.3.1 +protobuf==4.21.0 +pycparser==2.21 +pyparsing==3.1.1 +PyYAML==6.0.1 +resampy==0.3.1 +scikit-learn==0.24.2 +scipy==1.5.4 +setuptools==36.4.0 +six==1.16.0 +SoundFile==0.10.2 +svgwrite==1.4.3 +tensorboard==1.13.1 +tensorboardX==2.6.2.2 +tensorflow==1.13.1 +tensorflow-estimator==1.13.0 +termcolor==1.1.0 +threadpoolctl==3.1.0 +torch==1.0.1 +torchvision==0.2.2 +tqdm==4.19.9 +Tree==0.2.4 +typing_extensions==4.1.1 +Werkzeug==2.0.3 +wheel==0.37.1 +zipp==3.6.0 diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 1605c1fe..00000000 --- a/requirements.txt +++ /dev/null @@ -1,114 +0,0 @@ -absl-py==2.0.0 -addict==2.4.0 -aiosignal==1.3.1 -appdirs==1.4.4 -attrs==23.1.0 -audioread==3.0.1 -basicsr==1.3.4.7 -cachetools==5.3.2 -certifi==2020.12.5 -cffi==1.16.0 -charset-normalizer==3.3.2 -click==8.1.7 -cloudpickle==3.0.0 -colorama==0.4.6 -colorlog==6.7.0 -contourpy==1.1.1 -cycler==0.12.1 -decorator==5.1.1 -dlib==19.22.1 -docker-pycreds==0.4.0 -face-alignment==1.3.5 -ffmpeg==1.4 -filelock==3.13.1 -fonttools==4.44.0 -frozenlist==1.4.0 -future==0.18.3 -gitdb==4.0.11 -GitPython==3.1.40 -glob2==0.7 -google-auth==2.23.4 -google-auth-oauthlib==0.4.6 -grpcio==1.59.2 -hyperopt==0.2.5 -idna==3.4 -imageio==2.9.0 -imageio-ffmpeg==0.4.5 -importlib-metadata==6.8.0 -importlib-resources==6.1.0 -joblib==1.3.2 -jsonschema==4.19.2 -jsonschema-specifications==2023.7.1 -kiwisolver==1.4.5 -kornia==0.5.5 -lazy_loader==0.3 -librosa==0.10.1 -llvmlite==0.37.0 -lmdb==1.2.1 -lws==1.2.7 -Markdown==3.5.1 -MarkupSafe==2.1.3 -matplotlib==3.6.3 -msgpack==1.0.7 -networkx==3.1 -numba==0.54.1 -numpy==1.20.3 -oauthlib==3.2.2 -opencv-python==3.4.9.33 -packaging==23.2 -pandas==1.3.4 -pathtools==0.1.2 -Pillow==6.2.1 -pkgutil_resolve_name==1.3.10 -platformdirs==3.11.0 -plotly==5.18.0 -pooch==1.8.0 -protobuf==4.25.0 -psutil==5.9.6 -pyasn1==0.5.0 -pyasn1-modules==0.3.0 -pycparser==2.21 -pyparsing==3.1.1 -python-dateutil==2.8.2 -python-speech-features==0.6 -pytorch-fid==0.3.0 -pytz==2023.3.post1 -PyWavelets==1.4.1 -PyYAML==5.3.1 -ray==2.6.3 -referencing==0.30.2 -requests==2.31.0 -requests-oauthlib==1.3.1 -rpds-py==0.12.0 -rsa==4.9 -scikit-image==0.16.2 -scikit-learn==1.3.2 -scipy==1.5.0 -sentry-sdk==1.34.0 -setproctitle==1.3.3 -six==1.16.0 -smmap==5.0.1 -soundfile==0.12.1 -soxr==0.3.7 -tabulate==0.9.0 -tb-nightly==2.12.0a20230126 -tenacity==8.2.3 -tensorboard==2.7.0 -tensorboard-data-server==0.6.1 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+ + + diff --git a/result/train/init_ae_train_loss_kl.csv b/result/train/init_ae_train_loss_kl.csv new file mode 100644 index 00000000..b966a615 --- /dev/null +++ b/result/train/init_ae_train_loss_kl.csv @@ -0,0 +1,26 @@ + + + + + + + AutoPanel + + + + + + + +
+ + + diff --git a/result/train/init_ae_train_loss_rec.csv b/result/train/init_ae_train_loss_rec.csv new file mode 100644 index 00000000..b966a615 --- /dev/null +++ b/result/train/init_ae_train_loss_rec.csv @@ -0,0 +1,26 @@ + + + + + + + AutoPanel + + + + + + + +
+ + + diff --git a/result/train/train_output.log b/result/train/train_output.log new file mode 100644 index 00000000..c19b2f98 --- /dev/null +++ b/result/train/train_output.log @@ -0,0 +1,136 @@ +{'SpeakerEncoder': {'c_in': 512, 'c_h': 128, 'c_out': 128, 'kernel_size': 5, 'bank_size': 8, 'bank_scale': 1, 'c_bank': 128, 'n_conv_blocks': 6, 'n_dense_blocks': 6, 'subsample': [1, 2, 1, 2, 1, 2], 'act': 'relu', 'dropout_rate': 0}, 'ContentEncoder': {'c_in': 512, 'c_h': 128, 'c_out': 128, 'kernel_size': 5, 'bank_size': 8, 'bank_scale': 1, 'c_bank': 128, 'n_conv_blocks': 6, 'subsample': [1, 2, 1, 2, 1, 2], 'act': 'relu', 'dropout_rate': 0}, 'Decoder': {'c_in': 128, 'c_cond': 128, 'c_h': 128, 'c_out': 512, 'kernel_size': 5, 'n_conv_blocks': 6, 'upsample': [2, 1, 2, 1, 2, 1], 'act': 'relu', 'sn': False, 'dropout_rate': 0}, 'data_loader': {'segment_size': 128, 'frame_size': 1, 'batch_size': 128, 'shuffle': True}, 'optimizer': {'lr': 0.0005, 'beta1': 0.9, 'beta2': 0.999, 'amsgrad': True, 'weight_decay': 0.0001, 'grad_norm': 5}, 'lambda': {'lambda_rec': 10, 'lambda_kl': 1}, 'annealing_iters': 20000} +Namespace(config='./talkingface/properties/model/adaptive-VC.yaml', data_dir='./autodl-fs/preprocessed_data', dataset='VCTK', iters=20000, load_model=False, load_model_path='./saved/adaptive_vc/vctk_model', load_opt=False, logdir='./tf-logs/', model='Adaptive_VC', save_steps=5000, store_model_path='./saved/adaptive_vc/vctk_model', summary_steps=500, tag='init', train_index_file='train_samples_128.json', train_set='train_128') +AE( + (speaker_encoder): SpeakerEncoder( + (act): ReLU() + (conv_bank): ModuleList( + (0): Conv1d(512, 128, kernel_size=(1,), stride=(1,)) + (1): Conv1d(512, 128, kernel_size=(2,), stride=(1,)) + (2): Conv1d(512, 128, kernel_size=(3,), stride=(1,)) + (3): Conv1d(512, 128, kernel_size=(4,), stride=(1,)) + (4): Conv1d(512, 128, kernel_size=(5,), stride=(1,)) + (5): Conv1d(512, 128, kernel_size=(6,), stride=(1,)) + (6): Conv1d(512, 128, kernel_size=(7,), stride=(1,)) + (7): Conv1d(512, 128, kernel_size=(8,), stride=(1,)) + ) + (in_conv_layer): Conv1d(1536, 128, kernel_size=(1,), stride=(1,)) + (first_conv_layers): ModuleList( + (0): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (1): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (2): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (3): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (4): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (5): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + ) + (second_conv_layers): ModuleList( + (0): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (1): Conv1d(128, 128, kernel_size=(5,), stride=(2,)) + (2): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (3): Conv1d(128, 128, kernel_size=(5,), stride=(2,)) + (4): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (5): Conv1d(128, 128, kernel_size=(5,), stride=(2,)) + ) + (pooling_layer): AdaptiveAvgPool1d(output_size=1) + (first_dense_layers): ModuleList( + (0): Linear(in_features=128, out_features=128, bias=True) + (1): Linear(in_features=128, out_features=128, bias=True) + (2): Linear(in_features=128, out_features=128, bias=True) + (3): Linear(in_features=128, out_features=128, bias=True) + (4): Linear(in_features=128, out_features=128, bias=True) + (5): Linear(in_features=128, out_features=128, bias=True) + ) + (second_dense_layers): ModuleList( + (0): Linear(in_features=128, out_features=128, bias=True) + (1): Linear(in_features=128, out_features=128, bias=True) + (2): Linear(in_features=128, out_features=128, bias=True) + (3): Linear(in_features=128, out_features=128, bias=True) + (4): Linear(in_features=128, out_features=128, bias=True) + (5): Linear(in_features=128, out_features=128, bias=True) + ) + (output_layer): Linear(in_features=128, out_features=128, bias=True) + (dropout_layer): Dropout(p=0) + ) + (content_encoder): ContentEncoder( + (act): ReLU() + (conv_bank): ModuleList( + (0): Conv1d(512, 128, kernel_size=(1,), stride=(1,)) + (1): Conv1d(512, 128, kernel_size=(2,), stride=(1,)) + (2): Conv1d(512, 128, kernel_size=(3,), stride=(1,)) + (3): Conv1d(512, 128, kernel_size=(4,), stride=(1,)) + (4): Conv1d(512, 128, kernel_size=(5,), stride=(1,)) + (5): Conv1d(512, 128, kernel_size=(6,), stride=(1,)) + (6): Conv1d(512, 128, kernel_size=(7,), stride=(1,)) + (7): Conv1d(512, 128, kernel_size=(8,), stride=(1,)) + ) + (in_conv_layer): Conv1d(1536, 128, kernel_size=(1,), stride=(1,)) + (first_conv_layers): ModuleList( + (0): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (1): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (2): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (3): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (4): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (5): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + ) + (second_conv_layers): ModuleList( + (0): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (1): Conv1d(128, 128, kernel_size=(5,), stride=(2,)) + (2): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (3): Conv1d(128, 128, kernel_size=(5,), stride=(2,)) + (4): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (5): Conv1d(128, 128, kernel_size=(5,), stride=(2,)) + ) + (norm_layer): InstanceNorm1d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) + (mean_layer): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) + (std_layer): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) + (dropout_layer): Dropout(p=0) + ) + (decoder): Decoder( + (act): ReLU() + (in_conv_layer): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) + (first_conv_layers): ModuleList( + (0): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (1): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (2): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (3): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (4): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (5): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + ) + (second_conv_layers): ModuleList( + (0): Conv1d(128, 256, kernel_size=(5,), stride=(1,)) + (1): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (2): Conv1d(128, 256, kernel_size=(5,), stride=(1,)) + (3): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + (4): Conv1d(128, 256, kernel_size=(5,), stride=(1,)) + (5): Conv1d(128, 128, kernel_size=(5,), stride=(1,)) + ) + (norm_layer): InstanceNorm1d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) + (conv_affine_layers): ModuleList( + (0): Linear(in_features=128, out_features=256, bias=True) + (1): Linear(in_features=128, out_features=256, bias=True) + (2): Linear(in_features=128, out_features=256, bias=True) + (3): Linear(in_features=128, out_features=256, bias=True) + (4): Linear(in_features=128, out_features=256, bias=True) + (5): Linear(in_features=128, out_features=256, bias=True) + (6): Linear(in_features=128, out_features=256, bias=True) + (7): Linear(in_features=128, out_features=256, bias=True) + (8): Linear(in_features=128, out_features=256, bias=True) + (9): Linear(in_features=128, out_features=256, bias=True) + (10): Linear(in_features=128, out_features=256, bias=True) + (11): Linear(in_features=128, out_features=256, bias=True) + ) + (out_conv_layer): Conv1d(128, 512, kernel_size=(1,), stride=(1,)) + (dropout_layer): Dropout(p=0) + ) +) +Adam ( +Parameter Group 0 + amsgrad: True + betas: (0.9, 0.999) + eps: 1e-08 + lr: 0.0005 + weight_decay: 0.0001 +) +AE:[5000/20000], loss_rec=0.29, loss_kl=0.82, lambda=2.5e-01 +AE:[10000/20000], loss_rec=0.29, loss_kl=0.58, lambda=5.0e-01 +AE:[15000/20000], loss_rec=0.28, loss_kl=0.46, lambda=7.5e-01 +AE:[20000/20000], loss_rec=0.29, loss_kl=0.38, lambda=1.0e+00 \ No newline at end of file diff --git a/run_evaluator.py b/run_evaluator.py new file mode 100644 index 00000000..f8aa9a5e --- /dev/null +++ b/run_evaluator.py @@ -0,0 +1,22 @@ +from argparse import ArgumentParser, Namespace +import torch +from talkingface.evaluator.evaluator import Evaluator +import yaml +import sys + + +if __name__ == '__main__': + parser = ArgumentParser() + parser.add_argument('-attr', '-a', default="./autodl-fs/preprocessed_data/attr.pkl") + parser.add_argument('-config', '-c', default='./saved/adaptive_vc/vctk_model.config.yaml', help='config file path') + parser.add_argument('-model', '-m', default ='./saved/adaptive_vc/vctk_model.ckpt', help='model path') + parser.add_argument('-source', '-s', default='./autodl-fs/VCTK-Corpus/wav48/p231/p231_006.wav') + parser.add_argument('-target', '-t', default='./autodl-fs/VCTK-Corpus/wav48/p252/p253_032.wav') + parser.add_argument('-output', '-o', default='./result/evaluate/eval.wav') + parser.add_argument('-sample_rate', '-sr', help='sample rate', default=24000, type=int) + args = parser.parse_args() + # load config file + with open(args.config) as f: + config = yaml.safe_load(f) + evaluator = Evaluator(config=config, args=args) + evaluator.inference_from_path() \ No newline at end of file diff --git a/run_talkingface.py b/run_talkingface.py index 3989d566..ea698f32 100644 --- a/run_talkingface.py +++ b/run_talkingface.py @@ -1,24 +1,40 @@ -import argparse -from talkingface.quick_start import run +''' +Description: +Author: Fu Yuxuan +Date: 2024-01-15 20:16:39 +LastEditTime: 2024-01-19 22:21:57 +''' +from argparse import ArgumentParser, Namespace +import torch +from talkingface.trainer.trainer import Trainer +import yaml +import sys -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--model", "-m", type=str, default="BPR", help="name of models") - parser.add_argument( - "--dataset", "-d", type=str, default=None, help="name of datasets" - ) - parser.add_argument("--evaluate_model_file", type=str, default=None, help="The model file you want to evaluate") - parser.add_argument("--config_files", type=str, default=None, help="config files") +if __name__ == '__main__': + parser = ArgumentParser() + parser.add_argument("--model", "-m", type=str, default="Adaptive_VC", help="name of models") + parser.add_argument("--dataset", type=str, default="VCTK", help="name of datasets") + parser.add_argument('-config', '-c', default='./talkingface/properties/model/adaptive-VC.yaml') + parser.add_argument('-data_dir', '-d', default='./autodl-fs/preprocessed_data') + parser.add_argument('-train_set', default='train_128') + parser.add_argument('-train_index_file', default='train_samples_128.json') + parser.add_argument('-logdir', default='./tf-logs/') + parser.add_argument('--load_model', action='store_true') + parser.add_argument('--load_opt', action='store_true') + parser.add_argument('-store_model_path', default='./saved/adaptive_vc/vctk_model') + parser.add_argument('-load_model_path', default='./saved/adaptive_vc/vctk_model') + parser.add_argument('-summary_steps', default=500, type=int) + parser.add_argument('-save_steps', default=5000, type=int) + parser.add_argument('-tag', '-t', default='init') + parser.add_argument('-iters', default=20000, type=int) + args = parser.parse_args() + + # load config file + with open(args.config) as f: + config = yaml.safe_load(f) - args, _ = parser.parse_known_args() + trainer = Trainer(config=config, args=args) - config_file_list = ( - args.config_files.strip().split(" ") if args.config_files else None - ) - run( - args.model, - args.dataset, - config_file_list=config_file_list, - evaluate_model_file=args.evaluate_model_file - ) \ No newline at end of file + if args.iters > 0: + trainer.train(n_iterations=args.iters) \ No newline at end of file diff --git a/saved/adaptive_vc/vctk_model.args.yaml b/saved/adaptive_vc/vctk_model.args.yaml new file mode 100644 index 00000000..2e1f78be --- /dev/null +++ b/saved/adaptive_vc/vctk_model.args.yaml @@ -0,0 +1,15 @@ +config: ./talkingface/properties/model/adaptive-VC.yaml +data_dir: ./autodl-fs/preprocessed_data +dataset: VCTK +iters: 20000 +load_model: false +load_model_path: ./saved/adaptive_vc/vctk_model +load_opt: false +logdir: ./tf-logs/ +model: Adaptive_VC +save_steps: 5000 +store_model_path: ./saved/adaptive_vc/vctk_model +summary_steps: 500 +tag: init +train_index_file: train_samples_128.json +train_set: train_128 diff --git a/saved/adaptive_vc/vctk_model.config.yaml b/saved/adaptive_vc/vctk_model.config.yaml new file mode 100644 index 00000000..b3d44217 --- /dev/null +++ b/saved/adaptive_vc/vctk_model.config.yaml @@ -0,0 +1,70 @@ +ContentEncoder: + act: relu + bank_scale: 1 + bank_size: 8 + c_bank: 128 + c_h: 128 + c_in: 512 + c_out: 128 + dropout_rate: 0 + kernel_size: 5 + n_conv_blocks: 6 + subsample: + - 1 + - 2 + - 1 + - 2 + - 1 + - 2 +Decoder: + act: relu + c_cond: 128 + c_h: 128 + c_in: 128 + c_out: 512 + dropout_rate: 0 + kernel_size: 5 + n_conv_blocks: 6 + sn: false + upsample: + - 2 + - 1 + - 2 + - 1 + - 2 + - 1 +SpeakerEncoder: + act: relu + bank_scale: 1 + bank_size: 8 + c_bank: 128 + c_h: 128 + c_in: 512 + c_out: 128 + dropout_rate: 0 + kernel_size: 5 + n_conv_blocks: 6 + n_dense_blocks: 6 + subsample: + - 1 + - 2 + - 1 + - 2 + - 1 + - 2 +annealing_iters: 20000 +data_loader: + batch_size: 128 + frame_size: 1 + segment_size: 128 + shuffle: true +lambda: + lambda_kl: 1 + lambda_rec: 10 +optimizer: + amsgrad: true + beta1: 0.9 + beta2: 0.999 + grad_norm: 5 + lr: 0.0005 + weight_decay: 0.0001 diff --git a/talkingface/data/dataprocess/__init__.py b/talkingface/data/dataprocess/__init__.py index 7c7aef87..e69de29b 100644 --- a/talkingface/data/dataprocess/__init__.py +++ b/talkingface/data/dataprocess/__init__.py @@ -1 +0,0 @@ -from talkingface.data.dataprocess.wav2lip_process import Wav2LipAudio, Wav2LipPreprocessForInference \ No newline at end of file diff --git a/talkingface/data/dataprocess/make_datasets_vctk.py b/talkingface/data/dataprocess/make_datasets_vctk.py new file mode 100644 index 00000000..8351a084 --- /dev/null +++ b/talkingface/data/dataprocess/make_datasets_vctk.py @@ -0,0 +1,104 @@ +import pickle +import librosa +import sys +import glob +import random +import os +from collections import defaultdict +import re +import numpy as np +import json +from tacotron.utils import get_spectrograms + +def read_speaker_info(speaker_info_path): + speaker_ids = [] + with open(speaker_info_path, 'r') as f: + for i, line in enumerate(f): + if i == 0: + continue + speaker_id = line.strip().split()[0] + speaker_ids.append(speaker_id) + return speaker_ids + +def read_filenames(root_dir): + speaker2filenames = defaultdict(lambda : []) + for path in sorted(glob.glob(os.path.join(root_dir, '*/*'))): + filename = path.strip().split('/')[-1] + speaker_id, utt_id = re.match(r'p(\d+)_(\d+)\.wav', filename).groups() + speaker2filenames[speaker_id].append(path) + return speaker2filenames + +def wave_feature_extraction(wav_file, sr): + y, sr = librosa.load(wav_file, sr) + y, _ = librosa.effects.trim(y, top_db=20) + return y + +def spec_feature_extraction(wav_file): + mel, mag = get_spectrograms(wav_file) + return mel, mag + +if __name__ == '__main__': + data_dir = sys.argv[1] + speaker_info_path = sys.argv[2] + output_dir = sys.argv[3] + test_speakers = int(sys.argv[4]) + test_proportion = float(sys.argv[5]) + sample_rate = int(sys.argv[6]) + n_utts_attr = int(sys.argv[7]) + + speaker_ids = read_speaker_info(speaker_info_path) + random.shuffle(speaker_ids) + + train_speaker_ids = speaker_ids[:-test_speakers] + test_speaker_ids = speaker_ids[-test_speakers:] + + speaker2filenames = read_filenames(data_dir) + + train_path_list, in_test_path_list, out_test_path_list = [], [], [] + + for speaker in train_speaker_ids: + path_list = speaker2filenames[speaker] + random.shuffle(path_list) + test_data_size = int(len(path_list) * test_proportion) + train_path_list += path_list[:-test_data_size] + in_test_path_list += path_list[-test_data_size:] + + with open(os.path.join(output_dir, 'in_test_files.txt'), 'w') as f: + for path in in_test_path_list: + f.write(f'{path}\n') + + for speaker in test_speaker_ids: + path_list = speaker2filenames[speaker] + out_test_path_list += path_list + + with open(os.path.join(output_dir, 'out_test_files.txt'), 'w') as f: + for path in out_test_path_list: + f.write(f'{path}\n') + + for dset, path_list in zip(['train', 'in_test', 'out_test'], \ + [train_path_list, in_test_path_list, out_test_path_list]): + print(f'processing {dset} set, {len(path_list)} files') + data = {} + output_path = os.path.join(output_dir, f'{dset}.pkl') + all_train_data = [] + for i, path in enumerate(sorted(path_list)): + if i % 500 == 0 or i == len(path_list) - 1: + print(f'processing {i} files') + filename = path.strip().split('/')[-1] + mel, mag = spec_feature_extraction(path) + data[filename] = mel + if dset == 'train' and i < n_utts_attr: + all_train_data.append(mel) + if dset == 'train': + all_train_data = np.concatenate(all_train_data) + mean = np.mean(all_train_data, axis=0) + std = np.std(all_train_data, axis=0) + attr = {'mean': mean, 'std': std} + with open(os.path.join(output_dir, 'attr.pkl'), 'wb') as f: + pickle.dump(attr, f) + for key, val in data.items(): + val = (val - mean) / std + data[key] = val + with open(output_path, 'wb') as f: + pickle.dump(data, f) + diff --git a/talkingface/data/dataprocess/preprocess_vctk.sh b/talkingface/data/dataprocess/preprocess_vctk.sh new file mode 100644 index 00000000..2df3974a --- /dev/null +++ b/talkingface/data/dataprocess/preprocess_vctk.sh @@ -0,0 +1,19 @@ +. vctk.config + +if [ $stage -le 0 ]; then + python3 make_datasets_vctk.py $raw_data_dir/wav48 $raw_data_dir/speaker-info.txt $data_dir $n_out_speakers $test_prop $sample_rate $n_utt_attr +fi + +if [ $stage -le 1 ]; then + python3 reduce_dataset.py $data_dir/train.pkl $data_dir/train_$segment_size.pkl $segment_size +fi + +if [ $stage -le 2 ]; then + # sample training samples + python3 sample_single_segments.py $data_dir/train.pkl $data_dir/train_samples_$segment_size.json $training_samples $segment_size +fi +if [ $stage -le 3 ]; then + # sample testing samples + python3 sample_single_segments.py $data_dir/in_test.pkl $data_dir/in_test_samples_$segment_size.json $testing_samples $segment_size + python3 sample_single_segments.py $data_dir/out_test.pkl $data_dir/out_test_samples_$segment_size.json $testing_samples $segment_size +fi diff --git a/talkingface/data/dataprocess/reduce_dataset.py b/talkingface/data/dataprocess/reduce_dataset.py new file mode 100644 index 00000000..1672ee25 --- /dev/null +++ b/talkingface/data/dataprocess/reduce_dataset.py @@ -0,0 +1,15 @@ +import pickle +import sys + +if __name__ == '__main__': + pkl_path = sys.argv[1] + output_path = sys.argv[2] + segment_size = int(sys.argv[3]) + + with open(pkl_path, 'rb') as f: + data = pickle.load(f) + + reduced_data = {key:val for key, val in data.items() if val.shape[0] > segment_size} + + with open(output_path, 'wb') as f: + pickle.dump(reduced_data, f) diff --git a/talkingface/data/dataprocess/sample_segments.py b/talkingface/data/dataprocess/sample_segments.py new file mode 100644 index 00000000..c9412dd6 --- /dev/null +++ b/talkingface/data/dataprocess/sample_segments.py @@ -0,0 +1,40 @@ +import json +import pickle +import sys +import os +import random + + +if __name__ == '__main__': + pickle_path = sys.argv[1] + sample_path = sys.argv[2] + n_samples = int(sys.argv[3]) + segment_size = int(sys.argv[4]) + + with open(pickle_path, 'rb') as f: + data = pickle.load(f) + + # (utt_id, timestep_1, timestep_2, neg_utt_id, neg_timestep) + samples = [] + + # filter length > 2 * segment_size + utt_list = [key for key in data] + utt_list = sorted(list(filter(lambda u : len(data[u]) > 2 * segment_size, utt_list))) + print(f'{len(utt_list)} utterances') + sample_utt_index_list = random.choices(range(len(utt_list)), k=n_samples) + + for i, utt_ind in enumerate(sample_utt_index_list): + if i % 500 == 0: + print(f'sample {i} samples') + pos_utt_id = utt_list[utt_ind] + neg_utt_id = random.choice(utt_list[:utt_ind] + utt_list[utt_ind + 1:]) + t1 = random.randint(0, len(data[pos_utt_id]) - 2 * segment_size) + t2 = random.randint(t1 + segment_size, len(data[pos_utt_id]) - segment_size) + # random swap t1, t2 + t1, t2 = random.sample([t1, t2], k=2) + t_neg = random.randint(0, len(data[neg_utt_id]) - segment_size) + samples.append((pos_utt_id, t1, t2, neg_utt_id, t_neg)) + + with open(sample_path, 'w') as f: + json.dump(samples, f) + diff --git a/talkingface/data/dataprocess/sample_single_segments.py b/talkingface/data/dataprocess/sample_single_segments.py new file mode 100644 index 00000000..8bc3cf7c --- /dev/null +++ b/talkingface/data/dataprocess/sample_single_segments.py @@ -0,0 +1,34 @@ +import json +import pickle +import sys +import os +import random + +if __name__ == '__main__': + pickle_path = sys.argv[1] + sample_path = sys.argv[2] + n_samples = int(sys.argv[3]) + segment_size = int(sys.argv[4]) + + with open(pickle_path, 'rb') as f: + data = pickle.load(f) + + # (utt_id, timestep, neg_utt_id, neg_timestep) + samples = [] + + # filter length > segment_size + utt_list = [key for key in data] + utt_list = sorted(list(filter(lambda u : len(data[u]) > segment_size, utt_list))) + print(f'{len(utt_list)} utterances') + sample_utt_index_list = random.choices(range(len(utt_list)), k=n_samples) + + for i, utt_ind in enumerate(sample_utt_index_list): + if i % 500 == 0: + print(f'sample {i} samples') + utt_id = utt_list[utt_ind] + t = random.randint(0, len(data[utt_id]) - segment_size) + samples.append((utt_id, t)) + + with open(sample_path, 'w') as f: + json.dump(samples, f) + diff --git a/talkingface/data/dataprocess/tacotron/hyperparams.py b/talkingface/data/dataprocess/tacotron/hyperparams.py new file mode 100644 index 00000000..988774db --- /dev/null +++ b/talkingface/data/dataprocess/tacotron/hyperparams.py @@ -0,0 +1,52 @@ +# -*- coding: utf-8 -*- +#/usr/bin/python2 +''' +By kyubyong park. kbpark.linguist@gmail.com. +https://www.github.com/kyubyong/tacotron +''' +class Hyperparams: + '''Hyper parameters''' + + # pipeline + prepro = False # if True, run `python prepro.py` first before running `python train.py`. + + vocab = "PE abcdefghijklmnopqrstuvwxyz'.?" # P: Padding E: End of Sentence + + # data + data = "/data/private/voice/LJSpeech-1.0" + # data = "/data/private/voice/nick" + test_data = 'harvard_sentences.txt' + max_duration = 10.0 + top_db = 15 + + # signal processing + sr = 24000 # Sample rate. + n_fft = 2048 # fft points (samples) + frame_shift = 0.0125 # seconds + frame_length = 0.05 # seconds + hop_length = int(sr*frame_shift) # samples. + win_length = int(sr*frame_length) # samples. + n_mels = 512 # Number of Mel banks to generate + power = 1.2 # Exponent for amplifying the predicted magnitude + n_iter = 100 # Number of inversion iterations + preemphasis = .97 # or None + max_db = 100 + ref_db = 20 + + # model + embed_size = 256 # alias = E + encoder_num_banks = 16 + decoder_num_banks = 8 + num_highwaynet_blocks = 4 + r = 5 # Reduction factor. Paper => 2, 3, 5 + dropout_rate = .5 + + # training scheme + lr = 0.001 # Initial learning rate. + logdir = "logdir/01" + sampledir = 'samples' + batch_size = 32 + + + + diff --git a/talkingface/data/dataprocess/tacotron/utils.py b/talkingface/data/dataprocess/tacotron/utils.py new file mode 100644 index 00000000..256a2862 --- /dev/null +++ b/talkingface/data/dataprocess/tacotron/utils.py @@ -0,0 +1,183 @@ +# -*- coding: utf-8 -*- +# /usr/bin/python2 +''' +By kyubyong park. kbpark.linguist@gmail.com. +https://www.github.com/kyubyong/dc_tts +''' +from __future__ import print_function, division + +from .hyperparams import Hyperparams as hp +import numpy as np +import tensorflow as tf +import librosa +import copy +#import matplotlib +#matplotlib.use('pdf') +#import matplotlib.pyplot as plt +from scipy import signal +import os + +def _mel_to_linear_matrix(sr, n_fft, n_mels): + m = librosa.filters.mel(sr, n_fft, n_mels) + m_t = np.transpose(m) + p = np.matmul(m, m_t) + d = [1.0 / x if np.abs(x) > 1.0e-8 else x for x in np.sum(p, axis=0)] + return np.matmul(m_t, np.diag(d)) + +def get_spectrograms(fpath): + '''Returns normalized log(melspectrogram) and log(magnitude) from `sound_file`. + Args: + sound_file: A string. The full path of a sound file. + + Returns: + mel: A 2d array of shape (T, n_mels) <- Transposed + mag: A 2d array of shape (T, 1+n_fft/2) <- Transposed + ''' + # num = np.random.randn() + # if num < .2: + # y, sr = librosa.load(fpath, sr=hp.sr) + # else: + # if num < .4: + # tempo = 1.1 + # elif num < .6: + # tempo = 1.2 + # elif num < .8: + # tempo = 0.9 + # else: + # tempo = 0.8 + # cmd = "ffmpeg -i {} -y ar {} -hide_banner -loglevel panic -ac 1 -filter:a atempo={} -vn temp.wav".format(fpath, hp.sr, tempo) + # os.system(cmd) + # y, sr = librosa.load('temp.wav', sr=hp.sr) + + # Loading sound file + y, sr = librosa.load(fpath, sr=hp.sr) + + + # Trimming + y, _ = librosa.effects.trim(y, top_db=hp.top_db) + + # Preemphasis + y = np.append(y[0], y[1:] - hp.preemphasis * y[:-1]) + + # stft + linear = librosa.stft(y=y, + n_fft=hp.n_fft, + hop_length=hp.hop_length, + win_length=hp.win_length) + + # magnitude spectrogram + mag = np.abs(linear) # (1+n_fft//2, T) + + # mel spectrogram + mel_basis = librosa.filters.mel(hp.sr, hp.n_fft, hp.n_mels) # (n_mels, 1+n_fft//2) + mel = np.dot(mel_basis, mag) # (n_mels, t) + + # to decibel + mel = 20 * np.log10(np.maximum(1e-5, mel)) + mag = 20 * np.log10(np.maximum(1e-5, mag)) + + # normalize + mel = np.clip((mel - hp.ref_db + hp.max_db) / hp.max_db, 1e-8, 1) + mag = np.clip((mag - hp.ref_db + hp.max_db) / hp.max_db, 1e-8, 1) + + # Transpose + mel = mel.T.astype(np.float32) # (T, n_mels) + mag = mag.T.astype(np.float32) # (T, 1+n_fft//2) + + return mel, mag + +def melspectrogram2wav(mel): + '''# Generate wave file from spectrogram''' + # transpose + mel = mel.T + + # de-noramlize + mel = (np.clip(mel, 0, 1) * hp.max_db) - hp.max_db + hp.ref_db + + # to amplitude + mel = np.power(10.0, mel * 0.05) + m = _mel_to_linear_matrix(hp.sr, hp.n_fft, hp.n_mels) + mag = np.dot(m, mel) + + # wav reconstruction + wav = griffin_lim(mag) + + # de-preemphasis + wav = signal.lfilter([1], [1, -hp.preemphasis], wav) + + # trim + wav, _ = librosa.effects.trim(wav) + + return wav.astype(np.float32) + +def spectrogram2wav(mag): + '''# Generate wave file from spectrogram''' + # transpose + mag = mag.T + + # de-noramlize + mag = (np.clip(mag, 0, 1) * hp.max_db) - hp.max_db + hp.ref_db + + # to amplitude + mag = np.power(10.0, mag * 0.05) + + # wav reconstruction + wav = griffin_lim(mag) + + # de-preemphasis + wav = signal.lfilter([1], [1, -hp.preemphasis], wav) + + # trim + wav, _ = librosa.effects.trim(wav) + + return wav.astype(np.float32) + + +def griffin_lim(spectrogram): + '''Applies Griffin-Lim's raw. + ''' + X_best = copy.deepcopy(spectrogram) + for i in range(hp.n_iter): + X_t = invert_spectrogram(X_best) + est = librosa.stft(X_t, hp.n_fft, hp.hop_length, win_length=hp.win_length) + phase = est / np.maximum(1e-8, np.abs(est)) + X_best = spectrogram * phase + X_t = invert_spectrogram(X_best) + y = np.real(X_t) + + return y + + +def invert_spectrogram(spectrogram): + ''' + spectrogram: [f, t] + ''' + return librosa.istft(spectrogram, hp.hop_length, win_length=hp.win_length, window="hann") + + +def plot_alignment(alignment, gs): + """Plots the alignment + alignments: A list of (numpy) matrix of shape (encoder_steps, decoder_steps) + gs : (int) global step + """ + fig, ax = plt.subplots() + im = ax.imshow(alignment) + + # cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7]) + fig.colorbar(im) + plt.title('{} Steps'.format(gs)) + plt.savefig('{}/alignment_{}k.png'.format(hp.logdir, gs//1000), format='png') + +def learning_rate_decay(init_lr, global_step, warmup_steps=4000.): + '''Noam scheme from tensor2tensor''' + step = tf.cast(global_step + 1, dtype=tf.float32) + return init_lr * warmup_steps ** 0.5 * tf.minimum(step * warmup_steps ** -1.5, step ** -0.5) + +def load_spectrograms(fpath): + fname = os.path.basename(fpath) + mel, mag = get_spectrograms(fpath) + t = mel.shape[0] + num_paddings = hp.r - (t % hp.r) if t % hp.r != 0 else 0 # for reduction + mel = np.pad(mel, [[0, num_paddings], [0, 0]], mode="constant") + mag = np.pad(mag, [[0, num_paddings], [0, 0]], mode="constant") + return fname, mel.reshape((-1, hp.n_mels*hp.r)), mag diff --git a/talkingface/data/dataprocess/vctk.config b/talkingface/data/dataprocess/vctk.config new file mode 100644 index 00000000..053e1830 --- /dev/null +++ b/talkingface/data/dataprocess/vctk.config @@ -0,0 +1,10 @@ +stage=0 +segment_size=128 +data_dir=/root/dataset/preprocessed_data +raw_data_dir=/root/dataset/VCTK-Corpus +n_out_speakers=20 +test_prop=0.1 +sample_rate=24000 +training_samples=10000000 +testing_samples=10000 +n_utt_attr=5000 diff --git a/talkingface/data/dataprocess/wav2lip_process.py b/talkingface/data/dataprocess/wav2lip_process.py deleted file mode 100644 index e2279a42..00000000 --- a/talkingface/data/dataprocess/wav2lip_process.py +++ /dev/null @@ -1,253 +0,0 @@ -import os -import cv2 -import numpy as np -import subprocess -from tqdm import tqdm -from glob import glob -from concurrent.futures import ThreadPoolExecutor, as_completed -from talkingface.utils import face_detection -import librosa -import librosa.filters -from scipy import signal -from scipy.io import wavfile - -class Wav2LipAudio: - """This class is used for audio processing of wav2lip - - 这个类提供了从音频到mel谱的方法 - """ - - def __init__(self, config): - self.config = config - # Conversions - self._mel_basis = None - - def load_wav(self, path, sr): - return librosa.core.load(path, sr=sr)[0] - - def save_wav(self, wav, path, sr): - wav *= 32767 / max(0.01, np.max(np.abs(wav))) - #proposed by @dsmiller - wavfile.write(path, sr, wav.astype(np.int16)) - - def save_wavenet_wav(self, wav, path, sr): - librosa.output.write_wav(path, wav, sr=sr) - - def preemphasis(self, wav, k, preemphasize=True): - if preemphasize: - return signal.lfilter([1, -k], [1], wav) - return wav - - def inv_preemphasis(self, wav, k, inv_preemphasize=True): - if inv_preemphasize: - return signal.lfilter([1], [1, -k], wav) - return wav - - def get_hop_size(self): - hop_size = self.config['hop_size'] - if hop_size is None: - assert self.config['frame_shift_ms'] is not None - hop_size = int(self.config['frame_shift_ms'] / 1000 * self.config['sample_rate']) - return hop_size - - def linearspectrogram(self, wav): - D = self._stft(self.preemphasis(wav, self.config['preemphasis'], self.config['preemphasize'])) - S = self._amp_to_db(np.abs(D)) - self.config['ref_level_db'] - - if self.config['signal_normalization']: - return self._normalize(S) - return S - - def melspectrogram(self, wav): - D = self._stft(self.preemphasis(wav, self.config['preemphasis'], self.config['preemphasize'])) - S = self._amp_to_db(self._linear_to_mel(np.abs(D))) - self.config['ref_level_db'] - - if self.config['signal_normalization']: - return self._normalize(S) - return S - - def _lws_processor(self): - import lws - return lws.lws(self.config['n_fft'], self.get_hop_size(), fftsize=self.config['win_size'], mode="speech") - - def _stft(self, y): - if self.config['use_lws']: - return self._lws_processor().stft(y).T - else: - return librosa.stft(y=y, n_fft=self.config['n_fft'], hop_length=self.get_hop_size(), win_length=self.config['win_size']) - - ########################################################## - #Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!) - def num_frames(self, length, fsize, fshift): - """Compute number of time frames of spectrogram - """ - pad = (fsize - fshift) - if length % fshift == 0: - M = (length + pad * 2 - fsize) // fshift + 1 - else: - M = (length + pad * 2 - fsize) // fshift + 2 - return M - - - def pad_lr(self, x, fsize, fshift): - """Compute left and right padding - """ - M = self.num_frames(len(x), fsize, fshift) - pad = (fsize - fshift) - T = len(x) + 2 * pad - r = (M - 1) * fshift + fsize - T - return pad, pad + r - ########################################################## - #Librosa correct padding - def librosa_pad_lr(self, x, fsize, fshift): - return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0] - - def _linear_to_mel(self, spectogram): - if self._mel_basis is None: - self._mel_basis = self._build_mel_basis() - return np.dot(self._mel_basis, spectogram) - - def _build_mel_basis(self): - assert self.config['fmax'] <= self.config['sample_rate'] // 2 - return librosa.filters.mel(sr=self.config['sample_rate'], n_fft=self.config['n_fft'], n_mels=self.config['num_mels'], - fmin=self.config['fmin'], fmax=self.config['fmax']) - - def _amp_to_db(self, x): - min_level = np.exp(self.config['min_level_db'] / 20 * np.log(10)) - return 20 * np.log10(np.maximum(min_level, x)) - - def _db_to_amp(x): - return np.power(10.0, (x) * 0.05) - - def _normalize(self, S): - if self.config['allow_clipping_in_normalization']: - if self.config['symmetric_mels']: - return np.clip((2 * self.config['max_abs_value']) * ((S - self.config['min_level_db']) / (-self.config['min_level_db'])) - self.config['max_abs_value'], - -self.config['max_abs_value'], self.config['max_abs_value']) - else: - return np.clip(self.config['max_abs_value'] * ((S - self.config['min_level_db']) / (-self.config['min_level_db'])), 0, self.config['max_abs_value']) - - assert S.max() <= 0 and S.min() - self.config['min_level_db'] >= 0 - if self.config['symmetric_mels']: - return (2 * self.config['max_abs_value']) * ((S - self.config['min_level_db']) / (-self.config['min_level_db'])) - self.config['max_abs_value'] - else: - return self.config['max_abs_value'] * ((S - self.config['min_level_db']) / (-self.config['min_level_db'])) - - def _denormalize(self, D): - if self.config['allow_clipping_in_normalization']: - if self.config['symmetric_mels']: - return (((np.clip(D, -self.config['max_abs_value'], - self.config['max_abs_value']) + self.config['max_abs_value']) * -self.config['min_level_db'] / (2 * self.config['max_abs_value'])) - + self.config['symmetric_mels']) - else: - return ((np.clip(D, 0, self.config['max_abs_value']) * -self.config['min_level_db'] / self.config['max_abs_value']) + self.config['min_level_db']) - - if self.config['symmetric_mels']: - return (((D + self.config['max_abs_value']) * -self.config['min_level_db'] / (2 * self.config['max_abs_value'])) + self.config['min_level_db']) - else: - return ((D * -self.config['min_level_db'] / self.config['max_abs_value']) + self.config['min_level_db']) - - - -class Wav2LipPreprocessForInference: - """This class is used for preprocessing of wav2lip inference - - face_detect: detect face in the image - datagen: generate data for inference - - """ - def __init__(self, config): - self.config = config - self.fa = face_detection.FaceAlignment(face_detection.LandmarksType._2D, flip_input=False, - device=config['device']) - - def get_smoothened_boxes(self, boxes, T): - for i in range(len(boxes)): - if i + T > len(boxes): - window = boxes[len(boxes) - T:] - else: - window = boxes[i : i + T] - boxes[i] = np.mean(window, axis=0) - return boxes - - def face_detect(self, images): - batch_size = self.config['batch_size'] - while 1: - predictions = [] - try: - for i in tqdm(range(0, len(images), batch_size), desc='Running face detection', leave=False): - predictions.extend(self.fa.get_detections_for_batch(np.array(images[i:i + batch_size]))) - except RuntimeError: - if batch_size == 1: - raise RuntimeError('Image too big to run face detection on GPU. Please use the --resize_factor argument') - batch_size //= 2 - print('Recovering from OOM error; New batch size: {}'.format(batch_size)) - continue - break - results = [] - pady1, pady2, padx1, padx2 = self.config['pads'] - for rect, image in zip(predictions, images): - if rect is None: - cv2.imwrite('temp/faulty_frame.jpg', image) # check this frame where the face was not detected. - raise ValueError('Face not detected! Ensure the video contains a face in all the frames.') - - y1 = max(0, rect[1] - pady1) - y2 = min(image.shape[0], rect[3] + pady2) - x1 = max(0, rect[0] - padx1) - x2 = min(image.shape[1], rect[2] + padx2) - - results.append([x1, y1, x2, y2]) - boxes = np.array(results) - if not self.config['nosmooth']: boxes = self.get_smoothened_boxes(boxes, T=5) - results = [[image[y1: y2, x1:x2], (y1, y2, x1, x2)] for image, (x1, y1, x2, y2) in zip(images, boxes)] - - return results - - - def datagen(self, frames, face_det_results, mels): - img_batch, mel_batch, frame_batch, coords_batch = [], [], [], [] - - if self.config['box'][0] == -1: - if not self.config['static']: - face_det_results = self.face_detect(frames) # BGR2RGB for CNN face detection - else: - face_det_results = self.face_detect([frames[0]]) - else: - print('Using the specified bounding box instead of face detection...') - y1, y2, x1, x2 = self.config['box'] - face_det_results = [[f[y1: y2, x1:x2], (y1, y2, x1, x2)] for f in frames] - - for i, m in enumerate(mels): - idx = 0 if self.config['static'] else i%len(frames) - frame_to_save = frames[idx].copy() - face, coords = face_det_results[idx].copy() - - face = cv2.resize(face, (self.config['img_size'], self.config['img_size'])) - - img_batch.append(face) - mel_batch.append(m) - frame_batch.append(frame_to_save) - coords_batch.append(coords) - - if len(img_batch) >= self.config['wav2lip_batch_size']: - img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch) - - img_masked = img_batch.copy() - img_masked[:, self.config['img_size']//2:] = 0 - - img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255. - mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1]) - - yield img_batch, mel_batch, frame_batch, coords_batch - img_batch, mel_batch, frame_batch, coords_batch = [], [], [], [] - - if len(img_batch) > 0: - img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch) - - img_masked = img_batch.copy() - img_masked[:, self.config['img_size']//2:] = 0 - - img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255. - mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1]) - - yield img_batch, mel_batch, frame_batch, coords_batch \ No newline at end of file diff --git a/talkingface/data/dataset/__init__.py b/talkingface/data/dataset/__init__.py index 3fd37538..e69de29b 100644 --- a/talkingface/data/dataset/__init__.py +++ b/talkingface/data/dataset/__init__.py @@ -1,2 +0,0 @@ -from talkingface.data.dataset.wav2lip_dataset import Wav2LipDataset -from talkingface.data.dataset.dataset import Dataset \ No newline at end of file diff --git a/talkingface/data/dataset/dataset.py b/talkingface/data/dataset/dataset.py index 2de27bd7..e8b175d9 100644 --- a/talkingface/data/dataset/dataset.py +++ b/talkingface/data/dataset/dataset.py @@ -21,5 +21,4 @@ def __getitem__(self): """ - raise NotImplementedError \ No newline at end of file diff --git a/talkingface/data/dataset/vctk_dataset.py b/talkingface/data/dataset/vctk_dataset.py new file mode 100644 index 00000000..b5ab839b --- /dev/null +++ b/talkingface/data/dataset/vctk_dataset.py @@ -0,0 +1,57 @@ +import torch +from torch.utils.data import Dataset +import os +import pickle +import json +import numpy as np +import torch +from torch.utils.data import DataLoader + +class SequenceDataset(Dataset): + def __init__(self, data): + self.data = data + self.utt_ids = list(self.data.keys()) + + def __getitem__(self, ind): + utt_id = self.utt_ids[ind] + ret = self.data[utt_id].transpose() + return ret + + def __len__(self): + return len(self.utt_ids) + +class PickleDataset(Dataset): + def __init__(self, pickle_path, sample_index_path, segment_size): + with open(pickle_path, 'rb') as f: + self.data = pickle.load(f) + with open(sample_index_path, 'r') as f: + self.indexes = json.load(f) + self.segment_size = segment_size + + def __getitem__(self, ind): + utt_id, t = self.indexes[ind] + segment = self.data[utt_id][t:t + self.segment_size] + return segment + + def __len__(self): + return len(self.indexes) + +def get_data_loader(dataset, batch_size, frame_size, shuffle=True, num_workers=4, drop_last=False): + _collate_fn = CollateFn(frame_size=frame_size) + dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, + num_workers=num_workers, collate_fn=_collate_fn, pin_memory=True) + return dataloader + +class CollateFn(object): + def __init__(self, frame_size): + self.frame_size = frame_size + + def make_frames(self, tensor): + out = tensor.view(tensor.size(0), tensor.size(1) // self.frame_size, self.frame_size * tensor.size(2)) + out = out.transpose(1, 2) + return out + + def __call__(self, l): + data_tensor = torch.from_numpy(np.array(l)) + segment = self.make_frames(data_tensor) + return segment \ No newline at end of file diff --git a/talkingface/data/dataset/wav2lip_dataset.py b/talkingface/data/dataset/wav2lip_dataset.py deleted file mode 100644 index e52ae28d..00000000 --- a/talkingface/data/dataset/wav2lip_dataset.py +++ /dev/null @@ -1,158 +0,0 @@ -from os.path import dirname, join, basename, isfile -from tqdm import tqdm -from talkingface.data.dataprocess.wav2lip_process import Wav2LipAudio -import python_speech_features - -import torch -from torch import nn -from torch import optim -import torch.backends.cudnn as cudnn -from torch.utils import data as data_utils -import numpy as np -import librosa -from talkingface.data.dataset.dataset import Dataset - -from glob import glob - -import os, random, cv2, argparse - -class Wav2LipDataset(Dataset): - def __init__(self, config, datasplit): - super().__init__(config, datasplit) - self.all_videos = self.get_image_list(self.config['preprocessed_root'], datasplit) - self.audio_processor = Wav2LipAudio(self.config) - - - def get_image_list(self, data_root, split_path): - filelist = [] - - with open(split_path) as f: - for line in f: - line = line.strip() - if ' ' in line: line = line.split()[0] - filelist.append(os.path.join(data_root, line)) - - return filelist - - - def get_frame_id(self, frame): - return int(basename(frame).split('.')[0]) - - def get_window(self, start_frame): - start_id = self.get_frame_id(start_frame) - vidname = dirname(start_frame) - - window_fnames = [] - for frame_id in range(start_id, start_id + self.config['syncnet_T']): - frame = join(vidname, '{}.jpg'.format(frame_id)) - if not isfile(frame): - return None - window_fnames.append(frame) - return window_fnames - - def read_window(self, window_fnames): - if window_fnames is None: return None - window = [] - for fname in window_fnames: - img = cv2.imread(fname) - if img is None: - return None - try: - img = cv2.resize(img, (self.config['img_size'], self.config['img_size'])) - except Exception as e: - return None - - window.append(img) - - return window - - def crop_audio_window(self, spec, start_frame): - if type(start_frame) == int: - start_frame_num = start_frame - else: - start_frame_num = self.get_frame_id(start_frame) # 0-indexing ---> 1-indexing - start_idx = int(80. * (start_frame_num / float(self.config['fps']))) - - end_idx = start_idx + self.config['syncnet_mel_step_size'] - - return spec[start_idx : end_idx, :] - - def get_segmented_mels(self, spec, start_frame): - mels = [] - assert self.config['syncnet_T'] == 5 - start_frame_num = self.get_frame_id(start_frame) + 1 # 0-indexing ---> 1-indexing - if start_frame_num - 2 < 0: return None - for i in range(start_frame_num, start_frame_num + self.config['syncnet_T']): - m = self.crop_audio_window(spec, i - 2) - if m.shape[0] != self.config['syncnet_mel_step_size']: - return None - mels.append(m.T) - - mels = np.asarray(mels) - - return mels - - def prepare_window(self, window): - # 3 x T x H x W - x = np.asarray(window) / 255. - x = np.transpose(x, (3, 0, 1, 2)) - - return x - - def __len__(self): - return len(self.all_videos) - - def __getitem__(self, idx): - while 1: - idx = random.randint(0, len(self.all_videos) - 1) - vidname = self.all_videos[idx] - img_names = list(glob(join(vidname, '*.jpg'))) - if len(img_names) <= 3 * self.config['syncnet_T']: - continue - - img_name = random.choice(img_names) - wrong_img_name = random.choice(img_names) - while wrong_img_name == img_name: - wrong_img_name = random.choice(img_names) - - window_fnames = self.get_window(img_name) - wrong_window_fnames = self.get_window(wrong_img_name) - if window_fnames is None or wrong_window_fnames is None: - continue - - window = self.read_window(window_fnames) - if window is None: - continue - - wrong_window = self.read_window(wrong_window_fnames) - if wrong_window is None: - continue - - try: - wavpath = join(vidname, "audio.wav") - wav = self.audio_processor.load_wav(wavpath, self.config['sample_rate']) - orig_mel = self.audio_processor.melspectrogram(wav).T - except Exception as e: - continue - - mel = self.crop_audio_window(orig_mel.copy(), img_name) - - if (mel.shape[0] != self.config['syncnet_mel_step_size']): - continue - - indiv_mels = self.get_segmented_mels(orig_mel.copy(), img_name) - if indiv_mels is None: continue - - window = self.prepare_window(window) - y = window.copy() - window[:, :, window.shape[2]//2:] = 0. - - wrong_window = self.prepare_window(wrong_window) - x = np.concatenate([window, wrong_window], axis=0) - - x = torch.FloatTensor(x) - mel = torch.FloatTensor(mel.T).unsqueeze(0) - # breakpoint() - indiv_mels = torch.FloatTensor(indiv_mels).unsqueeze(1) - y = torch.FloatTensor(y) - return {"input_frames":x, "indiv_mels":indiv_mels, "mels":mel, "gt":y} diff --git a/talkingface/evaluator/__init__.py b/talkingface/evaluator/__init__.py index d5eeefda..8b137891 100644 --- a/talkingface/evaluator/__init__.py +++ b/talkingface/evaluator/__init__.py @@ -1,4 +1 @@ -from talkingface.evaluator.metric_models import * -from talkingface.evaluator.metrics import * -from talkingface.evaluator.register import * -from talkingface.evaluator.evaluator import * \ No newline at end of file + diff --git a/talkingface/evaluator/base_metric.py b/talkingface/evaluator/base_metric.py deleted file mode 100644 index 81e15721..00000000 --- a/talkingface/evaluator/base_metric.py +++ /dev/null @@ -1,98 +0,0 @@ -import torch -from talkingface.utils import EvaluatorType - -class AbstractMetric(object): - """:class:`AbstractMetric` is the base object of all metrics. If you want to - implement a metric, you should inherit this class. - - Args: - config (Config): the config of evaluator. - """ - - smaller = False - - def __init__(self, config): - self.decimal_place = config["metric_decimal_place"] - - def calculate_metric(self, dataobject): - """Get the dictionary of a metric. - - Args: - dataobject: (dict): it contains all the information needed to calculate metrics. - - Returns: - dict: such as ``{LSE-C': 0.0000}`` - """ - raise NotImplementedError("Method [calculate_metric] should be implemented.") - - -class SyncMetric(AbstractMetric): - """Base class for all Sync metrics. If you want to implement a sync metric, you can inherit this class. - """ - - metric_type = EvaluatorType.SYNC - metric_need = ["generated_video"] - def __init__(self, config): - super(SyncMetric, self).__init__(config) - - def get_videolist(self, dataobject): - """Get the list of videos. - - Args: - dataobject(DataStruct): (dict): it contains all the information needed to calculate metrics. - - Returns: - list: a list of videos. - """ - return dataobject["generated_video"] - - def metric_info(self, dataobject): - """Calculate the value of the metric. - - Args: - dataobject(DataStruct): it contains all the information needed to calculate metrics. - - Returns: - dict: {"LSE-C": LSE_C, "LSE-D": LSE_D} - """ - raise NotImplementedError("Method [metric_info] should be implemented.") - -class VideoQMetric(AbstractMetric): - """Base class for all Video Quality metrics. If you want to implement a Video Quality metric, you can inherit this class. - """ - - metric_type = EvaluatorType.VIDEOQ - def __init__(self, config): - super(VideoQMetric, self).__init__(config) - - def get_videopair(self, dataobject): - return list(zip(dataobject["generated_video"], dataobject["real_video"])) - - def metric_info(self, dataobject): - """Calculate the value of the metric. - - Args: - dataobject(DataStruct): (dict): it contains all the information needed to calculate metrics. - - Returns: - float: the value of the metric. - """ - raise NotImplementedError("Method [metric_info] should be implemented.") - -# class AudioQMetric(AbstractMetric): -# """Base class for all Audio Quality metrics. If you want to implement a Audio Quality metric, you can inherit this class. -# """ -# def __init__(self, config): -# super(SyncMetric, self).__init__(config) - -# def metric_info(self, dataobject): -# """Calculate the value of the metric. - -# Args: -# dataobject(DataStruct): it contains all the information needed to calculate metrics. - -# Returns: -# float: the value of the metric. -# """ -# raise NotImplementedError("Method [metric_info] should be implemented.") - diff --git a/talkingface/evaluator/evaluator.py b/talkingface/evaluator/evaluator.py index d2592bee..b792440e 100644 --- a/talkingface/evaluator/evaluator.py +++ b/talkingface/evaluator/evaluator.py @@ -1,22 +1,93 @@ -from talkingface.evaluator.register import metrics_dict +import torch +import numpy as np +import sys +import os +import torch.nn as nn +import torch.nn.functional as F +import yaml +import pickle +from talkingface.model.model_adaptive_VC import AE +from talkingface.utils.utils import * +from functools import reduce +import json +from collections import defaultdict +from torch.utils.data import Dataset +from torch.utils.data import TensorDataset +from torch.utils.data import DataLoader +from argparse import ArgumentParser, Namespace +from scipy.io.wavfile import write +import random +from talkingface.data.dataprocess.tacotron.utils import melspectrogram2wav +from talkingface.data.dataprocess.tacotron.utils import get_spectrograms +import librosa class Evaluator(object): - def __init__(self, config): + def __init__(self, config, args): + # config store the value of hyperparameters, turn to attr by AttrDict self.config = config - self.metrics = [metric.lower() for metric in self.config["metrics"]] - self.metric_class = {} - - for metric in self.metrics: - if metric not in metrics_dict: - raise ValueError(f"Metric '{metric}' is not defined.") - self.metric_class[metric] = metrics_dict[metric](self.config) - - def evaluate(self, datadict): - - result_dict = {} - - for metric in self.metrics: - metric_val = self.metric_class[metric].calculate_metric(datadict) - result_dict[metric] = metric_val - - return result_dict + print(config) + # args store other information + self.args = args + print(self.args) + + # init the model with config + self.build_model() + + # load model + self.load_model() + + with open(self.args.attr, 'rb') as f: + self.attr = pickle.load(f) + + def load_model(self): + print(f'Load model from {self.args.model}') + self.model.load_state_dict(torch.load(f'{self.args.model}')) + return + + def build_model(self): + # create model, discriminator, optimizers + self.model = cc(AE(self.config)) + print(self.model) + self.model.eval() + return + + def utt_make_frames(self, x): + frame_size = self.config['data_loader']['frame_size'] + remains = x.size(0) % frame_size + if remains != 0: + x = F.pad(x, (0, remains)) + out = x.view(1, x.size(0) // frame_size, frame_size * x.size(1)).transpose(1, 2) + return out + + def inference_one_utterance(self, x, x_cond): + x = self.utt_make_frames(x) + x_cond = self.utt_make_frames(x_cond) + dec = self.model.inference(x, x_cond) + dec = dec.transpose(1, 2).squeeze(0) + dec = dec.detach().cpu().numpy() + dec = self.denormalize(dec) + wav_data = melspectrogram2wav(dec) + return wav_data, dec + + def denormalize(self, x): + m, s = self.attr['mean'], self.attr['std'] + ret = x * s + m + return ret + + def normalize(self, x): + m, s = self.attr['mean'], self.attr['std'] + ret = (x - m) / s + return ret + + def write_wav_to_file(self, wav_data, output_path): + write(output_path, rate=self.args.sample_rate, data=wav_data) + return + + def inference_from_path(self): + src_mel, _ = get_spectrograms(self.args.source) + tar_mel, _ = get_spectrograms(self.args.target) + src_mel = torch.from_numpy(self.normalize(src_mel)).cuda() + tar_mel = torch.from_numpy(self.normalize(tar_mel)).cuda() + conv_wav, conv_mel = self.inference_one_utterance(src_mel, tar_mel) + self.write_wav_to_file(conv_wav, self.args.output) + return diff --git a/talkingface/evaluator/metric_models.py b/talkingface/evaluator/metric_models.py deleted file mode 100644 index 3a38ccba..00000000 --- a/talkingface/evaluator/metric_models.py +++ /dev/null @@ -1,105 +0,0 @@ -import torch -import torch.nn as nn - -class S(nn.Module): - def __init__(self, num_layers_in_fc_layers = 1024): - super(S, self).__init__() - - self.__nFeatures__ = 24 - self.__nChs__ = 32 - self.__midChs__ = 32 - - self.netcnnaud = nn.Sequential( - nn.Conv2d(1, 64, kernel_size=(3,3), stride=(1,1), padding=(1,1)), - nn.BatchNorm2d(64), - nn.ReLU(inplace=True), - nn.MaxPool2d(kernel_size=(1,1), stride=(1,1)), - - nn.Conv2d(64, 192, kernel_size=(3,3), stride=(1,1), padding=(1,1)), - nn.BatchNorm2d(192), - nn.ReLU(inplace=True), - nn.MaxPool2d(kernel_size=(3,3), stride=(1,2)), - - nn.Conv2d(192, 384, kernel_size=(3,3), padding=(1,1)), - nn.BatchNorm2d(384), - nn.ReLU(inplace=True), - - nn.Conv2d(384, 256, kernel_size=(3,3), padding=(1,1)), - nn.BatchNorm2d(256), - nn.ReLU(inplace=True), - - nn.Conv2d(256, 256, kernel_size=(3,3), padding=(1,1)), - nn.BatchNorm2d(256), - nn.ReLU(inplace=True), - nn.MaxPool2d(kernel_size=(3,3), stride=(2,2)), - - nn.Conv2d(256, 512, kernel_size=(5,4), padding=(0,0)), - nn.BatchNorm2d(512), - nn.ReLU(), - ) - - self.netfcaud = nn.Sequential( - nn.Linear(512, 512), - nn.BatchNorm1d(512), - nn.ReLU(), - nn.Linear(512, num_layers_in_fc_layers), - ) - - self.netfclip = nn.Sequential( - nn.Linear(512, 512), - nn.BatchNorm1d(512), - nn.ReLU(), - nn.Linear(512, num_layers_in_fc_layers), - ) - - self.netcnnlip = nn.Sequential( - nn.Conv3d(3, 96, kernel_size=(5,7,7), stride=(1,2,2), padding=0), - nn.BatchNorm3d(96), - nn.ReLU(inplace=True), - nn.MaxPool3d(kernel_size=(1,3,3), stride=(1,2,2)), - - nn.Conv3d(96, 256, kernel_size=(1,5,5), stride=(1,2,2), padding=(0,1,1)), - nn.BatchNorm3d(256), - nn.ReLU(inplace=True), - nn.MaxPool3d(kernel_size=(1,3,3), stride=(1,2,2), padding=(0,1,1)), - - nn.Conv3d(256, 256, kernel_size=(1,3,3), padding=(0,1,1)), - nn.BatchNorm3d(256), - nn.ReLU(inplace=True), - - nn.Conv3d(256, 256, kernel_size=(1,3,3), padding=(0,1,1)), - nn.BatchNorm3d(256), - nn.ReLU(inplace=True), - - nn.Conv3d(256, 256, kernel_size=(1,3,3), padding=(0,1,1)), - nn.BatchNorm3d(256), - nn.ReLU(inplace=True), - nn.MaxPool3d(kernel_size=(1,3,3), stride=(1,2,2)), - - nn.Conv3d(256, 512, kernel_size=(1,6,6), padding=0), - nn.BatchNorm3d(512), - nn.ReLU(inplace=True), - ) - - def forward_aud(self, x): - mid = self.netcnnaud(x); # N x ch x 24 x M - mid = mid.view((mid.size()[0], -1)); # N x (ch x 24) - out = self.netfcaud(mid) - - return out - - def forward_lip(self, x): - - mid = self.netcnnlip(x); - mid = mid.view((mid.size()[0], -1)); # N x (ch x 24) - out = self.netfclip(mid) - - return out - - def forward_lipfeat(self, x): - - mid = self.netcnnlip(x) - out = mid.view((mid.size()[0], -1)); # N x (ch x 24) - - return out - \ No newline at end of file diff --git a/talkingface/evaluator/metrics.py b/talkingface/evaluator/metrics.py deleted file mode 100644 index eb2692c7..00000000 --- a/talkingface/evaluator/metrics.py +++ /dev/null @@ -1,243 +0,0 @@ -import torch -import numpy -import time, pdb, argparse, subprocess, os, math, glob -import cv2 -import python_speech_features -from tqdm import tqdm -from scipy import signal -from scipy.io import wavfile -from talkingface.evaluator.metric_models import * -from shutil import rmtree -from skimage.metrics import structural_similarity as ssim -from talkingface.evaluator.base_metric import AbstractMetric, SyncMetric, VideoQMetric -from talkingface.utils.logger import set_color - -class LSE(SyncMetric): - - ''' - ''' - def __init__(self, config, num_layers_in_fc_layers = 1024): - super(LSE, self).__init__(config) - self.config = config - self.syncnet = S(num_layers_in_fc_layers = num_layers_in_fc_layers) - - def metric_info(self, videofile): - self.loadParameters(self.config['lse_checkpoint_path']) - self.syncnet.to(self.config["device"]) - self.syncnet.eval() - - if os.path.exists(os.path.join(self.config['temp_dir'], self.config['lse_reference_dir'])): - rmtree(os.path.join(self.config['temp_dir'], self.config['lse_reference_dir'])) - - os.makedirs(os.path.join(self.config['temp_dir'], self.config['lse_reference_dir'])) - - command = ("ffmpeg -loglevel error -y -i %s -threads 1 -f image2 %s" % (videofile,os.path.join(self.config['temp_dir'], self.config['lse_reference_dir'],'%06d.jpg'))) - output = subprocess.call(command, shell=True, stdout=None) - - command = ("ffmpeg -loglevel error -y -i %s -async 1 -ac 1 -vn -acodec pcm_s16le -ar 16000 %s" % (videofile,os.path.join(self.config['temp_dir'], self.config['lse_reference_dir'],'audio.wav'))) - output = subprocess.call(command, shell=True, stdout=None) - - images = [] - - flist = glob.glob(os.path.join(self.config['temp_dir'], self.config['lse_reference_dir'],'*.jpg')) - flist.sort() - - for fname in flist: - img_input = cv2.imread(fname) - img_input = cv2.resize(img_input, (224, 224)) - images.append(img_input) - - im = numpy.stack(images, axis=3) - im = numpy.expand_dims(im, axis=0) - im = numpy.transpose(im, (0, 3, 4, 1, 2)) - - imtv = torch.autograd.Variable(torch.from_numpy(im.astype(float)).float()) - - sample_rate, audio = wavfile.read(os.path.join(self.config['temp_dir'], self.config['lse_reference_dir'],'audio.wav')) - mfcc = zip(*python_speech_features.mfcc(audio,sample_rate)) - mfcc = numpy.stack([numpy.array(i) for i in mfcc]) - - cc = numpy.expand_dims(numpy.expand_dims(mfcc,axis=0),axis=0) - cct = torch.autograd.Variable(torch.from_numpy(cc.astype(float)).float()) - - # ========== ========== - # Check audio and video input length - # ========== ========== - - #if (float(len(audio))/16000) != (float(len(images))/25) : - # print("WARNING: Audio (%.4fs) and video (%.4fs) lengths are different."%(float(len(audio))/16000,float(len(images))/25)) - - min_length = min(len(images),math.floor(len(audio)/640)) - - # ========== ========== - # Generate video and audio feats - # ========== ========== - - lastframe = min_length-5 - im_feat = [] - cc_feat = [] - - tS = time.time() - for i in range(0,lastframe,self.config['evaluate_batch_size']): - - im_batch = [ imtv[:,:,vframe:vframe+5,:,:] for vframe in range(i,min(lastframe, i+self.config['evaluate_batch_size'])) ] - im_in = torch.cat(im_batch,0) - im_out = self.syncnet.forward_lip(im_in.cuda()) - im_feat.append(im_out.data.cpu()) - - cc_batch = [ cct[:,:,:,vframe*4:vframe*4+20] for vframe in range(i,min(lastframe, i+self.config['evaluate_batch_size'])) ] - cc_in = torch.cat(cc_batch,0) - cc_out = self.syncnet.forward_aud(cc_in.cuda()) - cc_feat.append(cc_out.data.cpu()) - - im_feat = torch.cat(im_feat,0) - cc_feat = torch.cat(cc_feat,0) - - # ========== ========== - # Compute offset - # ========== ========== - - #print('Compute time %.3f sec.' % (time.time()-tS)) - - dists = self.calc_pdist(im_feat,cc_feat,vshift=self.config['vshift']) - mdist = torch.mean(torch.stack(dists,1),1) - - minval, minidx = torch.min(mdist,0) - - offset = self.config['vshift']-minidx - conf = torch.median(mdist) - minval - - fdist = numpy.stack([dist[minidx].numpy() for dist in dists]) - # fdist = numpy.pad(fdist, (3,3), 'constant', constant_values=15) - fconf = torch.median(mdist).numpy() - fdist - fconfm = signal.medfilt(fconf,kernel_size=9) - - numpy.set_printoptions(formatter={'float': '{: 0.3f}'.format}) - - return conf.numpy(), minval.numpy() - - - - def calc_pdist(self, feat1, feat2, vshift=10): - - win_size = vshift*2+1 - - feat2p = torch.nn.functional.pad(feat2,(0,0,vshift,vshift)) - - dists = [] - - for i in range(0,len(feat1)): - - dists.append(torch.nn.functional.pairwise_distance(feat1[[i],:].repeat(win_size, 1), feat2p[i:i+win_size,:])) - - return dists - - def calculate_metric(self, dataobject): - video_list = self.get_videolist(dataobject) - - LSE_dict = {} - - iter_data = ( - tqdm( - video_list, - total=len(video_list), - desc=set_color("calculate for lip-audio sync", "yellow") - ) - if self.config['show_progress'] - else video_list - ) - - for video in iter_data: - LSE_C, LSE_D = self.metric_info(video) - if "LSE_C" not in LSE_dict: - LSE_dict["LSE_C"] = [LSE_C] - else: - LSE_dict["LSE_C"].append(LSE_C) - if "LSE_D" not in LSE_dict: - LSE_dict["LSE_D"] = [LSE_D] - else: - LSE_dict["LSE_D"].append(LSE_D) - - return {"LSE-C: {}".format(sum(LSE_dict["LSE_C"])/len(LSE_dict["LSE_C"])), "LSE-D: {}".format(sum(LSE_dict["LSE_D"])/len(LSE_dict["LSE_D"]))} - - def loadParameters(self, path): - loaded_state = torch.load(path, map_location=lambda storage, loc: storage) - - self_state = self.syncnet.state_dict() - - for name, param in loaded_state.items(): - - self_state[name].copy_(param) - - -class SSIM(VideoQMetric): - - metric_need = ["generated_video", "real_video"] - - def __init__(self, config): - super(SSIM, self).__init__(config) - self.config = config - - def metric_info(self, g_videofile, r_videofile): - g_frames = [] - g_video = cv2.VideoCapture(g_videofile) - while g_video.isOpened(): - ret, frame = g_video.read() - if not ret: - break - frame = cv2.resize(frame, (224, 224)) - gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) - g_frames.append(gray) - g_video.release() - - r_frames = [] - r_video = cv2.VideoCapture(r_videofile) - while r_video.isOpened(): - ret, frame = r_video.read() - if not ret: - break - frame = cv2.resize(frame, (224, 224)) - gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) - r_frames.append(gray) - r_video.release() - - min_frames = min(len(g_frames), len(r_frames)) - - g_frames = g_frames[:min_frames] - r_frames = r_frames[:min_frames] - - ssim_scores = [] - for frame1, frame2 in zip(g_frames, r_frames): - # 计算两帧之间的SSIM - score, _ = ssim(frame1, frame2, full=True) - ssim_scores.append(score) - - return numpy.mean(ssim_scores) - - - - - - - def calculate_metric(self, dataobject): - - pair_list = self.get_videopair(dataobject) - - ssim_score_total = [] - - iter_data = ( - tqdm( - pair_list, - total=len(pair_list), - desc=set_color("calculate for video quality ssim", "yellow") - ) - if self.config['show_progress'] - else pair_list - ) - for pair in iter_data: - g_video = pair[0] - r_video = pair[1] - ssim_score = self.metric_info(g_video, r_video) - ssim_score_total.append(ssim_score) - - return sum(ssim_score_total)/len(ssim_score_total) diff --git a/talkingface/evaluator/register.py b/talkingface/evaluator/register.py deleted file mode 100644 index 2ff33c3b..00000000 --- a/talkingface/evaluator/register.py +++ /dev/null @@ -1,46 +0,0 @@ -import inspect -import sys - -def cluster_info(module_name): - """Collect information of all metrics, including: - - - ``metric_need``: Information needed to calculate this metric, the combination of ``rec.items, rec.topk, - rec.meanrank, rec.score, data.num_items, data.num_users, data.count_items, data.count_users, data.label``. - - ``metric_type``: Whether the scores required by metric are grouped by user, range in ``EvaluatorType.RANKING`` - and ``EvaluatorType.VALUE``. - - ``smaller``: Whether the smaller metric value represents better performance, - range in ``True`` and ``False``, default to ``False``. - - Args: - module_name (str): the name of module ``recbole.evaluator.metrics``. - - Returns: - dict: Three dictionaries containing the above information - and a dictionary matching metric names to metric classes. - """ - smaller_m = [] - m_dict, m_info, m_types = {}, {}, {} - metric_class = inspect.getmembers( - sys.modules[module_name], - lambda x: inspect.isclass(x) and x.__module__ == module_name, - ) - for name, metric_cls in metric_class: - name = name.lower() - m_dict[name] = metric_cls - if hasattr(metric_cls, "metric_need"): - m_info[name] = metric_cls.metric_need - else: - raise AttributeError(f"Metric '{name}' has no attribute [metric_need].") - if hasattr(metric_cls, "metric_type"): - m_types[name] = metric_cls.metric_type - else: - raise AttributeError(f"Metric '{name}' has no attribute [metric_type].") - if metric_cls.smaller is True: - smaller_m.append(name) - return smaller_m, m_info, m_types, m_dict - - -metric_module_name = "talkingface.evaluator.metrics" -smaller_metrics, metric_information, metric_types, metrics_dict = cluster_info( - metric_module_name -) \ No newline at end of file diff --git a/talkingface/model/audio_driven_talkingface/__init__.py b/talkingface/model/audio_driven_talkingface/__init__.py deleted file mode 100644 index 04a35f33..00000000 --- a/talkingface/model/audio_driven_talkingface/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from talkingface.model.audio_driven_talkingface.wav2lip import Wav2Lip, SyncNet_color \ No newline at end of file diff --git a/talkingface/model/audio_driven_talkingface/wav2lip.py b/talkingface/model/audio_driven_talkingface/wav2lip.py deleted file mode 100644 index ff3dfb46..00000000 --- a/talkingface/model/audio_driven_talkingface/wav2lip.py +++ /dev/null @@ -1,358 +0,0 @@ -import torch -import torch.nn as nn -from torch.nn import functional as F -from torch.nn.init import xavier_normal_, constant_ -from tqdm import tqdm -from os import listdir, path -import numpy as np -import os, subprocess -from glob import glob -import cv2 - -from talkingface.model.layers import Conv2d, Conv2dTranspose, nonorm_Conv2d -from talkingface.model.abstract_talkingface import AbstractTalkingFace -from talkingface.data.dataprocess.wav2lip_process import Wav2LipPreprocessForInference, Wav2LipAudio -from talkingface.utils import ensure_dir - -class SyncNet_color(nn.Module): - def __init__(self): - super(SyncNet_color, self).__init__() - - self.face_encoder = nn.Sequential( - Conv2d(15, 32, kernel_size=(7, 7), stride=1, padding=3), - - Conv2d(32, 64, kernel_size=5, stride=(1, 2), padding=1), - Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), - Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), - - Conv2d(64, 128, kernel_size=3, stride=2, padding=1), - Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), - Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), - Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), - - Conv2d(128, 256, kernel_size=3, stride=2, padding=1), - Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), - Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), - - Conv2d(256, 512, kernel_size=3, stride=2, padding=1), - Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), - Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), - - Conv2d(512, 512, kernel_size=3, stride=2, padding=1), - Conv2d(512, 512, kernel_size=3, stride=1, padding=0), - Conv2d(512, 512, kernel_size=1, stride=1, padding=0),) - - self.audio_encoder = nn.Sequential( - Conv2d(1, 32, kernel_size=3, stride=1, padding=1), - Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), - Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), - - Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1), - Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), - Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), - - Conv2d(64, 128, kernel_size=3, stride=3, padding=1), - Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), - Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), - - Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1), - Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), - Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), - - Conv2d(256, 512, kernel_size=3, stride=1, padding=0), - Conv2d(512, 512, kernel_size=1, stride=1, padding=0),) - - def forward(self, audio_sequences, face_sequences): # audio_sequences := (B, dim, T) - face_embedding = self.face_encoder(face_sequences) - audio_embedding = self.audio_encoder(audio_sequences) - - audio_embedding = audio_embedding.view(audio_embedding.size(0), -1) - face_embedding = face_embedding.view(face_embedding.size(0), -1) - - audio_embedding = F.normalize(audio_embedding, p=2, dim=1) - face_embedding = F.normalize(face_embedding, p=2, dim=1) - - - return audio_embedding, face_embedding - - - - - - - -class Wav2Lip(AbstractTalkingFace): - """wav2lip is a GAN-based model that predict the final with audio and image""" - def __init__(self, config): - super(Wav2Lip, self).__init__() - - self.face_encoder_blocks = nn.ModuleList([ - nn.Sequential(Conv2d(6, 16, kernel_size=7, stride=1, padding=3)), # 96,96 - - nn.Sequential(Conv2d(16, 32, kernel_size=3, stride=2, padding=1), # 48,48 - Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), - Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True)), - - nn.Sequential(Conv2d(32, 64, kernel_size=3, stride=2, padding=1), # 24,24 - Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), - Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), - Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True)), - - nn.Sequential(Conv2d(64, 128, kernel_size=3, stride=2, padding=1), # 12,12 - Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), - Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True)), - - nn.Sequential(Conv2d(128, 256, kernel_size=3, stride=2, padding=1), # 6,6 - Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), - Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True)), - - nn.Sequential(Conv2d(256, 512, kernel_size=3, stride=2, padding=1), # 3,3 - Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),), - - nn.Sequential(Conv2d(512, 512, kernel_size=3, stride=1, padding=0), # 1, 1 - Conv2d(512, 512, kernel_size=1, stride=1, padding=0)),]) - - self.audio_encoder = nn.Sequential( - Conv2d(1, 32, kernel_size=3, stride=1, padding=1), - Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), - Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), - - Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1), - Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), - Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), - - Conv2d(64, 128, kernel_size=3, stride=3, padding=1), - Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), - Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), - - Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1), - Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), - - Conv2d(256, 512, kernel_size=3, stride=1, padding=0), - Conv2d(512, 512, kernel_size=1, stride=1, padding=0),) - - self.face_decoder_blocks = nn.ModuleList([ - nn.Sequential(Conv2d(512, 512, kernel_size=1, stride=1, padding=0),), - - nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=3, stride=1, padding=0), # 3,3 - Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),), - - nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=3, stride=2, padding=1, output_padding=1), - Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), - Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),), # 6, 6 - - nn.Sequential(Conv2dTranspose(768, 384, kernel_size=3, stride=2, padding=1, output_padding=1), - Conv2d(384, 384, kernel_size=3, stride=1, padding=1, residual=True), - Conv2d(384, 384, kernel_size=3, stride=1, padding=1, residual=True),), # 12, 12 - - nn.Sequential(Conv2dTranspose(512, 256, kernel_size=3, stride=2, padding=1, output_padding=1), - Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), - Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),), # 24, 24 - - nn.Sequential(Conv2dTranspose(320, 128, kernel_size=3, stride=2, padding=1, output_padding=1), - Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), - Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),), # 48, 48 - - nn.Sequential(Conv2dTranspose(160, 64, kernel_size=3, stride=2, padding=1, output_padding=1), - Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), - Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),),]) # 96,96 - - self.output_block = nn.Sequential(Conv2d(80, 32, kernel_size=3, stride=1, padding=1), - nn.Conv2d(32, 3, kernel_size=1, stride=1, padding=0), - nn.Sigmoid()) - self.config = config - self.l1loss = nn.L1Loss() - self.bceloss = nn.BCELoss() - def forward(self, audio_sequences, face_sequences): - # audio_sequences = (B, T, 1, 80, 16) - B = audio_sequences.size(0) - - input_dim_size = len(face_sequences.size()) - if input_dim_size > 4: - audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0) - face_sequences = torch.cat([face_sequences[:, :, i] for i in range(face_sequences.size(2))], dim=0) - - audio_embedding = self.audio_encoder(audio_sequences) # B, 512, 1, 1 - - feats = [] - x = face_sequences - for f in self.face_encoder_blocks: - x = f(x) - feats.append(x) - - x = audio_embedding - for f in self.face_decoder_blocks: - x = f(x) - try: - x = torch.cat((x, feats[-1]), dim=1) - except Exception as e: - print(x.size()) - print(feats[-1].size()) - raise e - - feats.pop() - - x = self.output_block(x) - - if input_dim_size > 4: - x = torch.split(x, B, dim=0) # [(B, C, H, W)] - outputs = torch.stack(x, dim=2) # (B, C, T, H, W) - - else: - outputs = x - - return outputs - - def predict(self, audio_sequences, face_sequences): - return self.forward(audio_sequences, face_sequences) - - def calculate_loss(self, interaction, valid=False): - r"""Calculate the training loss for a batch data. - - Args: - interaction (Interaction): Interaction class of the batch. - - Returns: - torch.Tensor: Training loss, shape: [] - """ - indiv_mels = interaction['indiv_mels'].to(self.config['device']) - input_frames = interaction['input_frames'].to(self.config['device']) - mel = interaction['mels'].to(self.config['device']) - gt = interaction['gt'].to(self.config['device']) - g_frames = self.forward(indiv_mels, input_frames) - l1loss = self.l1loss(g_frames, gt) - if self.config['syncnet_wt'] > 0 or valid: - sync_loss = self.syncnet_loss(mel, g_frames) - else: - sync_loss = 0 - - loss = self.config['syncnet_wt'] * sync_loss + (1- self.config['syncnet_wt']) * l1loss - return {"loss":loss, "l1loss":l1loss, "sync_loss":sync_loss} - - def syncnet_loss(self, mel, g_frames): - syncnet = self.load_syncnet() - syncnet.eval() - g = g_frames[:, :, :, g_frames.size(3)//2:] - g = torch.cat([g[:, :, i] for i in range(self.config['syncnet_T'])], dim=1) - # B, 3 * T, H//2, W - a, v = syncnet(mel, g) - y = torch.ones(g.size(0), 1).float().to(self.config['device']) - return self.cosine_loss(a, v, y) - - def cosine_loss(self, a, v, y): - d = nn.functional.cosine_similarity(a, v) - loss = self.bceloss(d.unsqueeze(1), y) - - return loss - - def load_syncnet(self): - syncnet = SyncNet_color().to(self.config['device']) - for p in syncnet.parameters(): - p.requires_grad = False - checkpoint = torch.load(self.config["syncnet_checkpoint_path"]) - s = checkpoint["state_dict"] - new_s = {} - for k, v in s.items(): - new_s[k.replace('module.', '')] = v - syncnet.load_state_dict(new_s) - return syncnet - - def generate_batch(self): - audio_processor = Wav2LipAudio(self.config) - video_processor = Wav2LipPreprocessForInference(self.config) - - with open(self.config['test_filelist'], 'r') as filelist: - lines = filelist.readlines() - - file_dict = {'generated_video': [], 'real_video': []} - for idx, line in enumerate(tqdm(lines, desc='generate video')): - file_src = line.split()[0] - - audio_src = os.path.join(self.config['data_root'], file_src) + '.mp4' - video = os.path.join(self.config['data_root'], file_src) + '.mp4' - - - ensure_dir(os.path.join(self.config['temp_dir'])) - - command = 'ffmpeg -loglevel panic -y -i {} -strict -2 {}'.format(audio_src, os.path.join(self.config['temp_dir'], 'temp')+'.wav') - subprocess.call(command, shell=True) - - temp_audio = os.path.join(self.config['temp_dir'], 'temp')+'.wav' - wav = audio_processor.load_wav(temp_audio, 16000) - mel = audio_processor.melspectrogram(wav) - - if np.isnan(mel.reshape(-1)).sum() > 0: - continue - - mel_idx_multiplier = 80./self.config['fps'] - mel_chunks = [] - i = 0 - while 1: - start_idx = int(i * mel_idx_multiplier) - if start_idx + self.config['mel_step_size'] > len(mel[0]): - break - mel_chunks.append(mel[:, start_idx : start_idx + self.config['mel_step_size']]) - i += 1 - - video_stream = cv2.VideoCapture(video) - full_frames = [] - while 1: - still_reading, frame = video_stream.read() - if not still_reading or len(full_frames) > len(mel_chunks): - video_stream.release() - break - full_frames.append(frame) - - if len(full_frames) < len(mel_chunks): - continue - - full_frames = full_frames[:len(mel_chunks)] - - try: - face_det_results = video_processor.face_detect(full_frames.copy()) - except ValueError as e: - continue - - batch_size = self.config['wav2lip_batch_size'] - gen = video_processor.datagen(full_frames.copy(), face_det_results, mel_chunks) - - for i, (img_batch, mel_batch, frames, coords) in enumerate(gen): - if i == 0: - frame_h, frame_w = full_frames[0].shape[:-1] - output_video_path = os.path.join(self.config['temp_dir'], 'temp')+'.mp4' - fourcc = cv2.VideoWriter_fourcc(*'mp4v') # 或者尝试 'avc1' - out = cv2.VideoWriter(output_video_path, fourcc, 25, (frame_w, frame_h)) - - - img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(self.config['device']) - mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).to(self.config['device']) - - with torch.no_grad(): - pred = self.predict(mel_batch, img_batch) - - - pred = pred.cpu().numpy().transpose(0, 2, 3, 1) * 255. - - for pl, f, c in zip(pred, frames, coords): - y1, y2, x1, x2 = c - pl = cv2.resize(pl.astype(np.uint8), (x2 - x1, y2 - y1)) - f[y1:y2, x1:x2] = pl - out.write(f) - - out.release() - - vid = os.path.join(self.config['temp_dir'], file_src) + '.mp4' - vid_directory = os.path.dirname(vid) - if not os.path.exists(vid_directory): - os.makedirs(vid_directory) - - command = 'ffmpeg -loglevel panic -y -i {} -i {} -strict -2 -q:v 1 {}'.format(temp_audio, - os.path.join(self.config['temp_dir'], 'temp')+'.mp4', vid) - process_status = subprocess.call(command, shell=True) - if process_status == 0: - file_dict['generated_video'].append(vid) - file_dict['real_video'].append(video) - else: - continue - return file_dict - diff --git a/talkingface/model/image_driven_talkingface/__init__.py b/talkingface/model/image_driven_talkingface/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/talkingface/model/layers.py b/talkingface/model/layers.py deleted file mode 100644 index ed83da00..00000000 --- a/talkingface/model/layers.py +++ /dev/null @@ -1,44 +0,0 @@ -import torch -from torch import nn -from torch.nn import functional as F - -class Conv2d(nn.Module): - def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs): - super().__init__(*args, **kwargs) - self.conv_block = nn.Sequential( - nn.Conv2d(cin, cout, kernel_size, stride, padding), - nn.BatchNorm2d(cout) - ) - self.act = nn.ReLU() - self.residual = residual - - def forward(self, x): - out = self.conv_block(x) - if self.residual: - out += x - return self.act(out) - -class nonorm_Conv2d(nn.Module): - def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs): - super().__init__(*args, **kwargs) - self.conv_block = nn.Sequential( - nn.Conv2d(cin, cout, kernel_size, stride, padding), - ) - self.act = nn.LeakyReLU(0.01, inplace=True) - - def forward(self, x): - out = self.conv_block(x) - return self.act(out) - -class Conv2dTranspose(nn.Module): - def __init__(self, cin, cout, kernel_size, stride, padding, output_padding=0, *args, **kwargs): - super().__init__(*args, **kwargs) - self.conv_block = nn.Sequential( - nn.ConvTranspose2d(cin, cout, kernel_size, stride, padding, output_padding), - nn.BatchNorm2d(cout) - ) - self.act = nn.ReLU() - - def forward(self, x): - out = self.conv_block(x) - return self.act(out) diff --git a/talkingface/model/model_adaptive_VC.py b/talkingface/model/model_adaptive_VC.py new file mode 100644 index 00000000..b43990f6 --- /dev/null +++ b/talkingface/model/model_adaptive_VC.py @@ -0,0 +1,396 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.autograd as ag +import numpy as np +from math import ceil +from functools import reduce +from torch.nn.utils import spectral_norm +from talkingface.utils.utils import cc +from model.abstract_talkingface import AbstractTalkingFace + +class MLP(AbstractTalkingFace): + def __init__(self, c_in, c_h, n_blocks, act, sn): + super(MLP, self).__init__() + self.act = get_act(act) + self.n_blocks = n_blocks + f = spectral_norm if sn else lambda x: x + self.in_dense_layer = f(nn.Linear(c_in, c_h)) + self.first_dense_layers = nn.ModuleList([f(nn.Linear(c_h, c_h)) for _ in range(n_blocks)]) + self.second_dense_layers = nn.ModuleList([f(nn.Linear(c_h, c_h)) for _ in range(n_blocks)]) + + def forward(self, x): + h = self.in_dense_layer(x) + for l in range(self.n_blocks): + y = self.first_dense_layers[l](h) + y = self.act(y) + y = self.second_dense_layers[l](y) + y = self.act(y) + h = h + y + return h + +class Prenet(AbstractTalkingFace): + def __init__(self, c_in, c_h, c_out, + kernel_size, n_conv_blocks, + subsample, act, dropout_rate): + super(Prenet, self).__init__() + self.act = get_act(act) + self.subsample = subsample + self.n_conv_blocks = n_conv_blocks + self.in_conv_layer = nn.Conv2d(1, c_h, kernel_size=kernel_size) + self.first_conv_layers = nn.ModuleList([nn.Conv2d(c_h, c_h, kernel_size=kernel_size) for _ \ + in range(n_conv_blocks)]) + self.second_conv_layers = nn.ModuleList([nn.Conv2d(c_h, c_h, kernel_size=kernel_size, stride=sub) + for sub, _ in zip(subsample, range(n_conv_blocks))]) + output_size = c_in + for l, sub in zip(range(n_conv_blocks), self.subsample): + output_size = ceil(output_size / sub) + self.out_conv_layer = nn.Conv1d(c_h * output_size, c_out, kernel_size=1) + self.dropout_layer = nn.Dropout(p=dropout_rate) + self.norm_layer = nn.InstanceNorm2d(c_h, affine=False) + + def forward(self, x): + # reshape x to 4D + x = x.contiguous().view(x.size(0), 1, x.size(1), x.size(2)) + out = pad_layer_2d(x, self.in_conv_layer) + out = self.act(out) + out = self.norm_layer(out) + for l in range(self.n_conv_blocks): + y = pad_layer_2d(out, self.first_conv_layers[l]) + y = self.act(y) + y = self.norm_layer(y) + y = self.dropout_layer(y) + y = pad_layer_2d(y, self.second_conv_layers[l]) + y = self.act(y) + y = self.norm_layer(y) + y = self.dropout_layer(y) + if self.subsample[l] > 1: + out = F.avg_pool2d(out, kernel_size=self.subsample[l], ceil_mode=True) + out = y + out + out = out.contiguous().view(out.size(0), out.size(1) * out.size(2), out.size(3)) + out = pad_layer(out, self.out_conv_layer) + out = self.act(out) + return out + +class Postnet(AbstractTalkingFace): + def __init__(self, c_in, c_h, c_out, c_cond, + kernel_size, n_conv_blocks, + upsample, act, sn): + super(Postnet, self).__init__() + self.act = get_act(act) + self.upsample = upsample + self.c_h = c_h + self.n_conv_blocks = n_conv_blocks + f = spectral_norm if sn else lambda x: x + total_upsample = reduce(lambda x, y: x*y, upsample) + self.in_conv_layer = f(nn.Conv1d(c_in, c_h * c_out // total_upsample, kernel_size=1)) + self.first_conv_layers = nn.ModuleList([f(nn.Conv2d(c_h, c_h, kernel_size=kernel_size)) for _ \ + in range(n_conv_blocks)]) + self.second_conv_layers = nn.ModuleList([f(nn.Conv2d(c_h, c_h*up*up, kernel_size=kernel_size)) + for up, _ in zip(upsample, range(n_conv_blocks))]) + self.out_conv_layer = f(nn.Conv2d(c_h, 1, kernel_size=1)) + self.conv_affine_layers = nn.ModuleList( + [f(nn.Linear(c_cond, c_h * 2)) for _ in range(n_conv_blocks*2)]) + self.norm_layer = nn.InstanceNorm2d(c_h, affine=False) + self.ps = nn.PixelShuffle(max(upsample)) + + def forward(self, x, cond): + out = pad_layer(x, self.in_conv_layer) + out = out.contiguous().view(out.size(0), self.c_h, out.size(1) // self.c_h, out.size(2)) + for l in range(self.n_conv_blocks): + y = pad_layer_2d(out, self.first_conv_layers[l]) + y = self.act(y) + y = self.norm_layer(y) + y = append_cond(y, self.conv_affine_layers[l*2](cond)) + y = pad_layer_2d(y, self.second_conv_layers[l]) + y = self.act(y) + if self.upsample[l] > 1: + y = self.ps(y) + y = self.norm_layer(y) + y = append_cond(y, self.conv_affine_layers[l*2+1](cond)) + out = y + upsample(out, scale_factor=(self.upsample[l], self.upsample[l])) + else: + y = self.norm_layer(y) + y = append_cond(y, self.conv_affine_layers[l*2+1](cond)) + out = y + out + out = self.out_conv_layer(out) + out = out.squeeze(dim=1) + return out + +class SpeakerEncoder(AbstractTalkingFace): + def __init__(self, c_in, c_h, c_out, kernel_size, + bank_size, bank_scale, c_bank, + n_conv_blocks, n_dense_blocks, + subsample, act, dropout_rate): + super(SpeakerEncoder, self).__init__() + self.c_in = c_in + self.c_h = c_h + self.c_out = c_out + self.kernel_size = kernel_size + self.n_conv_blocks = n_conv_blocks + self.n_dense_blocks = n_dense_blocks + self.subsample = subsample + self.act = get_act(act) + self.conv_bank = nn.ModuleList( + [nn.Conv1d(c_in, c_bank, kernel_size=k) for k in range(bank_scale, bank_size + 1, bank_scale)]) + in_channels = c_bank * (bank_size // bank_scale) + c_in + self.in_conv_layer = nn.Conv1d(in_channels, c_h, kernel_size=1) + self.first_conv_layers = nn.ModuleList([nn.Conv1d(c_h, c_h, kernel_size=kernel_size) for _ \ + in range(n_conv_blocks)]) + self.second_conv_layers = nn.ModuleList([nn.Conv1d(c_h, c_h, kernel_size=kernel_size, stride=sub) + for sub, _ in zip(subsample, range(n_conv_blocks))]) + self.pooling_layer = nn.AdaptiveAvgPool1d(1) + self.first_dense_layers = nn.ModuleList([nn.Linear(c_h, c_h) for _ in range(n_dense_blocks)]) + self.second_dense_layers = nn.ModuleList([nn.Linear(c_h, c_h) for _ in range(n_dense_blocks)]) + self.output_layer = nn.Linear(c_h, c_out) + self.dropout_layer = nn.Dropout(p=dropout_rate) + + def conv_blocks(self, inp): + out = inp + # convolution blocks + for l in range(self.n_conv_blocks): + y = pad_layer(out, self.first_conv_layers[l]) + y = self.act(y) + y = self.dropout_layer(y) + y = pad_layer(y, self.second_conv_layers[l]) + y = self.act(y) + y = self.dropout_layer(y) + if self.subsample[l] > 1: + out = F.avg_pool1d(out, kernel_size=self.subsample[l], ceil_mode=True) + out = y + out + return out + + def dense_blocks(self, inp): + out = inp + # dense layers + for l in range(self.n_dense_blocks): + y = self.first_dense_layers[l](out) + y = self.act(y) + y = self.dropout_layer(y) + y = self.second_dense_layers[l](y) + y = self.act(y) + y = self.dropout_layer(y) + out = y + out + return out + + def forward(self, x): + out = conv_bank(x, self.conv_bank, act=self.act) + # dimension reduction layer + out = pad_layer(out, self.in_conv_layer) + out = self.act(out) + # conv blocks + out = self.conv_blocks(out) + # avg pooling + out = self.pooling_layer(out).squeeze(2) + # dense blocks + out = self.dense_blocks(out) + out = self.output_layer(out) + return out + +class ContentEncoder(AbstractTalkingFace): + def __init__(self, c_in, c_h, c_out, kernel_size, + bank_size, bank_scale, c_bank, + n_conv_blocks, subsample, + act, dropout_rate): + super(ContentEncoder, self).__init__() + self.n_conv_blocks = n_conv_blocks + self.subsample = subsample + self.act = get_act(act) + self.conv_bank = nn.ModuleList( + [nn.Conv1d(c_in, c_bank, kernel_size=k) for k in range(bank_scale, bank_size + 1, bank_scale)]) + in_channels = c_bank * (bank_size // bank_scale) + c_in + self.in_conv_layer = nn.Conv1d(in_channels, c_h, kernel_size=1) + self.first_conv_layers = nn.ModuleList([nn.Conv1d(c_h, c_h, kernel_size=kernel_size) for _ \ + in range(n_conv_blocks)]) + self.second_conv_layers = nn.ModuleList([nn.Conv1d(c_h, c_h, kernel_size=kernel_size, stride=sub) + for sub, _ in zip(subsample, range(n_conv_blocks))]) + self.norm_layer = nn.InstanceNorm1d(c_h, affine=False) + self.mean_layer = nn.Conv1d(c_h, c_out, kernel_size=1) + self.std_layer = nn.Conv1d(c_h, c_out, kernel_size=1) + self.dropout_layer = nn.Dropout(p=dropout_rate) + + def forward(self, x): + out = conv_bank(x, self.conv_bank, act=self.act) + # dimension reduction layer + out = pad_layer(out, self.in_conv_layer) + out = self.norm_layer(out) + out = self.act(out) + out = self.dropout_layer(out) + # convolution blocks + for l in range(self.n_conv_blocks): + y = pad_layer(out, self.first_conv_layers[l]) + y = self.norm_layer(y) + y = self.act(y) + y = self.dropout_layer(y) + y = pad_layer(y, self.second_conv_layers[l]) + y = self.norm_layer(y) + y = self.act(y) + y = self.dropout_layer(y) + if self.subsample[l] > 1: + out = F.avg_pool1d(out, kernel_size=self.subsample[l], ceil_mode=True) + out = y + out + mu = pad_layer(out, self.mean_layer) + log_sigma = pad_layer(out, self.std_layer) + return mu, log_sigma + +class Decoder(AbstractTalkingFace): + def __init__(self, + c_in, c_cond, c_h, c_out, + kernel_size, + n_conv_blocks, upsample, act, sn, dropout_rate): + super(Decoder, self).__init__() + self.n_conv_blocks = n_conv_blocks + self.upsample = upsample + self.act = get_act(act) + f = spectral_norm if sn else lambda x: x + self.in_conv_layer = f(nn.Conv1d(c_in, c_h, kernel_size=1)) + self.first_conv_layers = nn.ModuleList([f(nn.Conv1d(c_h, c_h, kernel_size=kernel_size)) for _ \ + in range(n_conv_blocks)]) + self.second_conv_layers = nn.ModuleList(\ + [f(nn.Conv1d(c_h, c_h * up, kernel_size=kernel_size)) \ + for _, up in zip(range(n_conv_blocks), self.upsample)]) + self.norm_layer = nn.InstanceNorm1d(c_h, affine=False) + self.conv_affine_layers = nn.ModuleList( + [f(nn.Linear(c_cond, c_h * 2)) for _ in range(n_conv_blocks*2)]) + self.out_conv_layer = f(nn.Conv1d(c_h, c_out, kernel_size=1)) + self.dropout_layer = nn.Dropout(p=dropout_rate) + + def forward(self, z, cond): + out = pad_layer(z, self.in_conv_layer) + out = self.norm_layer(out) + out = self.act(out) + out = self.dropout_layer(out) + # convolution blocks + for l in range(self.n_conv_blocks): + y = pad_layer(out, self.first_conv_layers[l]) + y = self.norm_layer(y) + y = append_cond(y, self.conv_affine_layers[l*2](cond)) + y = self.act(y) + y = self.dropout_layer(y) + y = pad_layer(y, self.second_conv_layers[l]) + if self.upsample[l] > 1: + y = pixel_shuffle_1d(y, scale_factor=self.upsample[l]) + y = self.norm_layer(y) + y = append_cond(y, self.conv_affine_layers[l*2+1](cond)) + y = self.act(y) + y = self.dropout_layer(y) + if self.upsample[l] > 1: + out = y + upsample(out, scale_factor=self.upsample[l]) + else: + out = y + out + out = pad_layer(out, self.out_conv_layer) + return out + +class AE(AbstractTalkingFace): + def __init__(self, config): + super(AE, self).__init__() + self.speaker_encoder = SpeakerEncoder(**config['SpeakerEncoder']) + self.content_encoder = ContentEncoder(**config['ContentEncoder']) + self.decoder = Decoder(**config['Decoder']) + + def forward(self, x): + emb = self.speaker_encoder(x) + mu, log_sigma = self.content_encoder(x) + eps = log_sigma.new(*log_sigma.size()).normal_(0, 1) + dec = self.decoder(mu + torch.exp(log_sigma / 2) * eps, emb) + return mu, log_sigma, emb, dec + + def inference(self, x, x_cond): + emb = self.speaker_encoder(x_cond) + mu, _ = self.content_encoder(x) + dec = self.decoder(mu, emb) + return dec + + def get_speaker_embeddings(self, x): + emb = self.speaker_encoder(x) + return emb + +class DummyEncoder(object): + def __init__(self, encoder): + self.encoder = encoder + + def load(self, target_network): + self.encoder.load_state_dict(target_network.state_dict()) + + def __call__(self, x): + return self.encoder(x) + +def pad_layer(inp, layer, pad_type='reflect'): + kernel_size = layer.kernel_size[0] + if kernel_size % 2 == 0: + pad = (kernel_size//2, kernel_size//2 - 1) + else: + pad = (kernel_size//2, kernel_size//2) + # padding + inp = F.pad(inp, + pad=pad, + mode=pad_type) + out = layer(inp) + return out + +def pad_layer_2d(inp, layer, pad_type='reflect'): + kernel_size = layer.kernel_size + if kernel_size[0] % 2 == 0: + pad_lr = [kernel_size[0]//2, kernel_size[0]//2 - 1] + else: + pad_lr = [kernel_size[0]//2, kernel_size[0]//2] + if kernel_size[1] % 2 == 0: + pad_ud = [kernel_size[1]//2, kernel_size[1]//2 - 1] + else: + pad_ud = [kernel_size[1]//2, kernel_size[1]//2] + pad = tuple(pad_lr + pad_ud) + # padding + inp = F.pad(inp, + pad=pad, + mode=pad_type) + out = layer(inp) + return out + +def pixel_shuffle_1d(inp, scale_factor=2): + batch_size, channels, in_width = inp.size() + channels //= scale_factor + out_width = in_width * scale_factor + inp_view = inp.contiguous().view(batch_size, channels, scale_factor, in_width) + shuffle_out = inp_view.permute(0, 1, 3, 2).contiguous() + shuffle_out = shuffle_out.view(batch_size, channels, out_width) + return shuffle_out + +def upsample(x, scale_factor=2): + x_up = F.interpolate(x, scale_factor=scale_factor, mode='nearest') + return x_up + +def flatten(x): + out = x.contiguous().view(x.size(0), x.size(1) * x.size(2)) + return out + +def concat_cond(x, cond): + # x = [batch_size, x_channels, length] + # cond = [batch_size, c_channels] + cond = cond.unsqueeze(dim=2) + cond = cond.expand(*cond.size()[:-1], x.size(-1)) + out = torch.cat([x, cond], dim=1) + return out + +def append_cond(x, cond): + # x = [batch_size, x_channels, length] + # cond = [batch_size, x_channels * 2] + p = cond.size(1) // 2 + mean, std = cond[:, :p], cond[:, p:] + out = x * std.unsqueeze(dim=2) + mean.unsqueeze(dim=2) + return out + +def conv_bank(x, module_list, act, pad_type='reflect'): + outs = [] + for layer in module_list: + out = act(pad_layer(x, layer, pad_type)) + outs.append(out) + out = torch.cat(outs + [x], dim=1) + return out + +def get_act(act): + if act == 'relu': + return nn.ReLU() + elif act == 'lrelu': + return nn.LeakyReLU() + else: + return nn.ReLU() \ No newline at end of file diff --git a/talkingface/model/nerf_based_talkingface/__init__.py b/talkingface/model/nerf_based_talkingface/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/talkingface/properties/dataset/lrs2.yaml b/talkingface/properties/dataset/lrs2.yaml deleted file mode 100644 index 3afa074f..00000000 --- a/talkingface/properties/dataset/lrs2.yaml +++ /dev/null @@ -1,10 +0,0 @@ -train_filelist: 'dataset/lrs2/filelist/train.txt' # 当前数据集的数据划分文件 train -test_filelist: 'dataset/lrs2/filelist/test.txt' # 当前数据集的数据划分文件 test -val_filelist: 'dataset/lrs2/filelist/val.txt' # 当前数据集的数据划分文件 val - -data_root: 'dataset/lrs2/data/main' # 当前数据集的数据根目录 -preprocessed_root: 'dataset/lrs2/preprocessed_data' # 当前数据集的预处理数据根目录 - -need_preprocess: True # 数据集是否需要预处理，如抽帧、抽音频等 - -preprocess_batch_size: 32 \ No newline at end of file diff --git a/talkingface/properties/dataset/vctk.config b/talkingface/properties/dataset/vctk.config new file mode 100644 index 00000000..6e6ca21a --- /dev/null +++ b/talkingface/properties/dataset/vctk.config @@ -0,0 +1,10 @@ +stage=0 +segment_size=128 +data_dir=/root/autodl-fs/preprocessed_data +raw_data_dir=/root/autodl-fs/VCTK-Corpus +n_out_speakers=20 +test_prop=0.1 +sample_rate=24000 +training_samples=10000000 +testing_samples=10000 +n_utt_attr=5000 \ No newline at end of file diff --git a/talkingface/properties/model/Wav2Lip.yaml b/talkingface/properties/model/Wav2Lip.yaml deleted file mode 100644 index 50b044cc..00000000 --- a/talkingface/properties/model/Wav2Lip.yaml +++ /dev/null @@ -1,55 +0,0 @@ -# Syncnet -syncnet_wt: 0.03 # (int) is initially zero, will be set automatically to 0.03 later.Leads to faster convergence. -syncnet_batch_size: 64 # (int) batch_size for syncnet train -syncnet_lr: 0.0001 #(float) learning rate for syncnet train -syncnet_eval_interval: 10000 -syncnet_checkpoint_interval: 10000 -syncnet_T: 5 -syncnet_mel_step_size: 16 -syncnet_checkpoint_path: "checkpoints/wav2lip/lipsync_expert.pth" - -# Data preprocessing for Wav2lip -num_mels: 80 -rescale: True -rescaling_max: 0.9 -use_lws: False -n_fft: 800 -hop_size: 200 -win_size: 800 -sample_rate: 16000 -frame_shift_ms: None -signal_normalization: True -allow_clipping_in_normalization: True -symmetric_mels: True -max_abs_value: 4 -preemphasize: True -preemphasis: 0.97 -min_level_db: -100 -ref_level_db: 20 -fmin: 55 -fmax: 7600 -img_size: 96 -fps: 25 -mel_step_size: 16 - -batch_size: 16 -ngpu: 1 - - -# Train -checkpoint_sub_dir: "/wav2lip" # 和overall.yaml里checkpoint_dir拼起来作为最终目录 - -temp_sub_dir: "/wav2lip" # 和overall.yaml里temp_dir拼起来作为最终目录 - - -# Inference -pads: [0, 10, 0, 0] -static: False -face_det_batch_size: 16 -resize_factor: 1 -crop: [0, -1, 0, -1] -box: [-1, -1, -1, -1] -rotate: False -nosmooth: False -wav2lip_batch_size: 128 -vshift: 15 \ No newline at end of file diff --git a/talkingface/properties/model/adaptive-VC.yaml b/talkingface/properties/model/adaptive-VC.yaml new file mode 100644 index 00000000..2ec1c408 --- /dev/null +++ b/talkingface/properties/model/adaptive-VC.yaml @@ -0,0 +1,52 @@ +SpeakerEncoder: + c_in: 512 + c_h: 128 + c_out: 128 + kernel_size: 5 + bank_size: 8 + bank_scale: 1 + c_bank: 128 + n_conv_blocks: 6 + n_dense_blocks: 6 + subsample: [1, 2, 1, 2, 1, 2] + act: 'relu' + dropout_rate: 0 +ContentEncoder: + c_in: 512 + c_h: 128 + c_out: 128 + kernel_size: 5 + bank_size: 8 + bank_scale: 1 + c_bank: 128 + n_conv_blocks: 6 + subsample: [1, 2, 1, 2, 1, 2] + act: 'relu' + dropout_rate: 0 +Decoder: + c_in: 128 + c_cond: 128 + c_h: 128 + c_out: 512 + kernel_size: 5 + n_conv_blocks: 6 + upsample: [2, 1, 2, 1, 2, 1] + act: 'relu' + sn: False + dropout_rate: 0 +data_loader: + segment_size: 128 + frame_size: 1 + batch_size: 128 + shuffle: True +optimizer: + lr: 0.0005 + beta1: 0.9 + beta2: 0.999 + amsgrad: True + weight_decay: 0.0001 + grad_norm: 5 +lambda: + lambda_rec: 10 + lambda_kl: 1 +annealing_iters: 20000 diff --git a/talkingface/trainer/__init__.py b/talkingface/trainer/__init__.py index 819cacbf..e69de29b 100644 --- a/talkingface/trainer/__init__.py +++ b/talkingface/trainer/__init__.py @@ -1 +0,0 @@ -from talkingface.trainer.trainer import * \ No newline at end of file diff --git a/talkingface/trainer/trainer.py b/talkingface/trainer/trainer.py index 2c34717b..0043a869 100644 --- a/talkingface/trainer/trainer.py +++ b/talkingface/trainer/trainer.py @@ -1,557 +1,119 @@ -import os - -from logging import getLogger -from time import time -import dlib, json, subprocess -import torch.nn.functional as F -import glob -import numpy as np import torch -import torch.optim as optim -from torch.nn.utils.clip_grad import clip_grad_norm_ -from tqdm import tqdm -import torch.cuda.amp as amp -from torch import nn -from pathlib import Path - -from talkingface.utils import( - ensure_dir, - get_local_time, - early_stopping, - calculate_valid_score, - dict2str, - get_tensorboard, - set_color, - get_gpu_usage, - WandbLogger -) -from talkingface.data.dataprocess.wav2lip_process import Wav2LipAudio -from talkingface.evaluator import Evaluator - - -class AbstractTrainer(object): - r"""Trainer Class is used to manage the training and evaluation processes of recommender system models. - AbstractTrainer is an abstract class in which the fit() and evaluate() method should be implemented according - to different training and evaluation strategies. - """ - - def __init__(self, config, model): +import numpy as np +import sys +import os +import torch.nn as nn +import torch.nn.functional as F +import yaml +import pickle +from talkingface.model.model_adaptive_VC import AE +from talkingface.utils.data_utils import get_data_loader +from talkingface.utils.data_utils import PickleDataset +from talkingface.utils.utils import * +from functools import reduce +from collections import defaultdict + +class Trainer(object): + def __init__(self, config, args): + # config store the value of hyperparameters, turn to attr by AttrDict self.config = config - self.model = model - - def fit(self, train_data): - r"""Train the model based on the train data.""" - raise NotImplementedError("Method [next] should be implemented.") - - def evaluate(self, eval_data): - r"""Evaluate the model based on the eval data.""" - - raise NotImplementedError("Method [next] should be implemented.") - - -class Trainer(AbstractTrainer): - r"""The basic Trainer for basic training and evaluation strategies in talkingface systems. This class defines common - functions for training and evaluation processes of most recommender system models, including fit(), evaluate(), - resume_checkpoint() and some other features helpful for model training and evaluation. - - Generally speaking, this class can serve most recommender system models, If the training process of the model is to - simply optimize a single loss without involving any complex training strategies. - - Initializing the Trainer needs two parameters: `config` and `model`. `config` records the parameters information - for controlling training and evaluation, such as `learning_rate`, `epochs`, `eval_step` and so on. - `model` is the instantiated object of a Model Class. - - """ - def __init__(self, config, model): - super(Trainer, self).__init__(config, model) - self.logger = getLogger() - self.tensorboard = get_tensorboard(self.logger) - self.wandblogger = WandbLogger(config) - # self.enable_amp = config["enable_amp"] - # self.enable_scaler = torch.cuda.is_available() and config["enable_scaler"] - - # config for train - self.learner = config["learner"] - self.learning_rate = config["learning_rate"] - self.epochs = config["epochs"] - self.eval_step = min(config["eval_step"], self.epochs) - self.stopping_step = config["stopping_step"] - self.test_batch_size = config["eval_batch_size"] - self.gpu_available = torch.cuda.is_available() and config["use_gpu"] - self.device = config["device"] - self.checkpoint_dir = config["checkpoint_dir"] - ensure_dir(self.checkpoint_dir) - saved_model_file = "{}-{}.pth".format(self.config["model"], get_local_time()) - self.saved_model_file = os.path.join(self.checkpoint_dir, saved_model_file) - self.weight_decay = config["weight_decay"] - self.start_epoch = 0 - self.cur_step = 0 - self.train_loss_dict = dict() - self.optimizer = self._build_optimizer() - self.evaluator = Evaluator(config) - - self.valid_metric_bigger = config["valid_metric_bigger"] - self.best_valid_score = -np.inf if self.valid_metric_bigger else np.inf - self.best_valid_result = None - - def _build_optimizer(self, **kwargs): - params = kwargs.pop("params", self.model.parameters()) - learner = kwargs.pop("learner", self.learner) - learning_rate = kwargs.pop("learning_rate", self.learning_rate) - weight_decay = kwargs.pop("weight_decay", self.weight_decay) - if (self.config["reg_weight"] and weight_decay and weight_decay * self.config["reg_weight"] > 0): - self.logger.warning( - "The parameters [weight_decay] and [reg_weight] are specified simultaneously, " - "which may lead to double regularization." - ) - - if learner.lower() == "adam": - optimizer = optim.Adam(params, lr=learning_rate, weight_decay=weight_decay) - elif learner.lower() == "adamw": - optimizer = optim.AdamW(params, lr=learning_rate, weight_decay=weight_decay) - elif learner.lower() == "sgd": - optimizer = optim.SGD(params, lr=learning_rate, weight_decay=weight_decay) - elif learner.lower() == "adagrad": - optimizer = optim.Adagrad( - params, lr=learning_rate, weight_decay=weight_decay - ) - elif learner.lower() == "rmsprop": - optimizer = optim.RMSprop( - params, lr=learning_rate, weight_decay=weight_decay - ) - elif learner.lower() == "sparse_adam": - optimizer = optim.SparseAdam(params, lr=learning_rate) - if weight_decay > 0: - self.logger.warning( - "Sparse Adam cannot argument received argument [{weight_decay}]" - ) - else: - self.logger.warning( - "Received unrecognized optimizer, set default Adam optimizer" - ) - optimizer = optim.Adam(params, lr=learning_rate) - return optimizer - - def _train_epoch(self, train_data, epoch_idx, loss_func=None, show_progress=False): - r"""Train the model in an epoch - - Args: - train_data (DataLoader): The train data. - epoch_idx (int): The current epoch id. - loss_func (function): The loss function of :attr:`model`. If it is ``None``, the loss function will be - :attr:`self.model.calculate_loss`. Defaults to ``None``. - show_progress (bool): Show the progress of training epoch. Defaults to ``False``. - - Returns: - the averaged loss of this epoch - """ - self.model.train() - loss_func = loss_func or self.model.calculate_loss - total_loss_dict = {} - step = 0 - iter_data = ( - tqdm( - train_data, - total=len(train_data), - ncols=None, - ) - if show_progress - else train_data - ) - - for batch_idx, interaction in enumerate(iter_data): - self.optimizer.zero_grad() - step += 1 - losses_dict = loss_func(interaction) - loss = losses_dict["loss"] - - for key, value in losses_dict.items(): - if key in total_loss_dict: - if not torch.is_tensor(value): - total_loss_dict[key] += value - # 如果键已经在总和字典中，累加当前值 - else: - losses_dict[key] = value.item() - total_loss_dict[key] += value.item() - else: - if not torch.is_tensor(value): - total_loss_dict[key] = value - # 否则，将当前值添加到字典中 - else: - losses_dict[key] = value.item() - total_loss_dict[key] = value.item() - iter_data.set_description(set_color(f"train {epoch_idx} {losses_dict}", "pink")) - - self._check_nan(loss) - loss.backward() - self.optimizer.step() - average_loss_dict = {} - for key, value in total_loss_dict.items(): - average_loss_dict[key] = value/step - - return average_loss_dict - - - - def _valid_epoch(self, valid_data, show_progress=False): - r"""Valid the model with valid data. Different from the evaluate, this is use for training. - - Args: - valid_data (DataLoader): the valid data. - show_progress (bool): Show the progress of evaluate epoch. Defaults to ``False``. - - Returns: - loss - """ - print('Valid for {} steps'.format(self.eval_steps)) - self.model.eval() - total_loss_dict = {} - iter_data = ( - tqdm(valid_data, - total=len(valid_data), - ncols=None, - ) - if show_progress - else valid_data - ) - step = 0 - for batch_idx, batched_data in enumerate(iter_data): - step += 1 - batched_data.to(self.device) - losses_dict = self.model.calculate_loss(batched_data, valid=True) - for key, value in losses_dict.items(): - if key in total_loss_dict: - if not torch.is_tensor(value): - total_loss_dict[key] += value - # 如果键已经在总和字典中，累加当前值 - else: - losses_dict[key] = value.item() - total_loss_dict[key] += value.item() - else: - if not torch.is_tensor(value): - total_loss_dict[key] = value - # 否则，将当前值添加到字典中 - else: - losses_dict[key] = value.item() - total_loss_dict[key] = value.item() - iter_data.set_description(set_color(f"Valid {losses_dict}", "pink")) - average_loss_dict = {} - for key, value in total_loss_dict.items(): - average_loss_dict[key] = value/step - - return average_loss_dict - - - - - def _save_checkpoint(self, epoch, verbose=True, **kwargs): - r"""Store the model parameters information and training information. - - Args: - epoch (int): the current epoch id - - """ - saved_model_file = kwargs.pop("saved_model_file", self.saved_model_file) - state = { - "config": self.config, - "epoch": epoch, - "cur_step": self.cur_step, - "best_valid_score": self.best_valid_score, - "state_dict": self.model.state_dict(), - "other_parameter": self.model.other_parameter(), - "optimizer": self.optimizer.state_dict(), - } - torch.save(state, saved_model_file, pickle_protocol=4) - if verbose: - self.logger.info( - set_color("Saving current", "blue") + f": {saved_model_file}" - ) - - def resume_checkpoint(self, resume_file): - r"""Load the model parameters information and training information. - - Args: - resume_file (file): the checkpoint file - - """ - resume_file = str(resume_file) - self.saved_model_file = resume_file - checkpoint = torch.load(resume_file, map_location=self.device) - self.start_epoch = checkpoint["epoch"] + 1 - self.cur_step = checkpoint["cur_step"] - # self.best_valid_score = checkpoint["best_valid_score"] - - # load architecture params from checkpoint - if checkpoint["config"]["model"].lower() != self.config["model"].lower(): - self.logger.warning( - "Architecture configuration given in config file is different from that of checkpoint. " - "This may yield an exception while state_dict is being loaded." - ) - self.model.load_state_dict(checkpoint["state_dict"]) - self.model.load_other_parameter(checkpoint.get("other_parameter")) - - # load optimizer state from checkpoint only when optimizer type is not changed - self.optimizer.load_state_dict(checkpoint["optimizer"]) - message_output = "Checkpoint loaded. Resume training from epoch {}".format( - self.start_epoch - ) - self.logger.info(message_output) - - def _check_nan(self, loss): - if torch.isnan(loss): - raise ValueError("Training loss is nan") - - def _generate_train_loss_output(self, epoch_idx, s_time, e_time, losses): - des = self.config["loss_decimal_place"] or 4 - train_loss_output = ( - set_color(f"epoch {epoch_idx} training", "green") - + " [" - + set_color("time", "blue") - + f": {e_time - s_time:.2f}s, " - ) - # 遍历字典，格式化并添加每个损失项 - loss_items = [ - set_color(f"{key}", "blue") + f": {value:.{des}f}" - for key, value in losses.items() - ] - # 将所有损失项连接成一个字符串，并与前面的输出拼接 - train_loss_output += ", ".join(loss_items) - return train_loss_output + "]" - - def _add_hparam_to_tensorboard(self, best_valid_result): - # base hparam - hparam_dict = { - "learner": self.config["learner"], - "learning_rate": self.config["learning_rate"], - "train_batch_size": self.config["train_batch_size"], - } - # unrecorded parameter - unrecorded_parameter = { - parameter - for parameters in self.config.parameters.values() - for parameter in parameters - }.union({"model", "dataset", "config_files", "device"}) - # other model-specific hparam - hparam_dict.update( - { - para: val - for para, val in self.config.final_config_dict.items() - if para not in unrecorded_parameter - } - ) - for k in hparam_dict: - if hparam_dict[k] is not None and not isinstance( - hparam_dict[k], (bool, str, float, int) - ): - hparam_dict[k] = str(hparam_dict[k]) - - self.tensorboard.add_hparams( - hparam_dict, {"hparam/best_valid_result": best_valid_result} - ) - - def fit(self, train_data, valid_data=None, verbose=True, saved=True, show_progress=False, callback_fn=None): - r"""Train the model based on the train data and the valid data. - - Args: - train_data (DataLoader): the train data - valid_data (DataLoader, optional): the valid data, default: None. - If it's None, the early_stopping is invalid. - verbose (bool, optional): whether to write training and evaluation information to logger, default: True - saved (bool, optional): whether to save the model parameters, default: True - show_progress (bool): Show the progress of training epoch and evaluate epoch. Defaults to ``False``. - callback_fn (callable): Optional callback function executed at end of epoch. - Includes (epoch_idx, valid_score) input arguments. - - Returns: - best result - """ - if saved and self.start_epoch >= self.epochs: - self._save_checkpoint(-1, verbose=verbose) - - if not (self.config['resume_checkpoint_path'] == None ) and self.config['resume']: - self.resume_checkpoint(self.config['resume_checkpoint_path']) - - for epoch_idx in range(self.start_epoch, self.epochs): - training_start_time = time() - train_loss = self._train_epoch(train_data, epoch_idx, show_progress=show_progress) - self.train_loss_dict[epoch_idx] = ( - sum(train_loss) if isinstance(train_loss, tuple) else train_loss - ) - training_end_time = time() - train_loss_output = self._generate_train_loss_output( - epoch_idx, training_start_time, training_end_time, train_loss) - - if verbose: - self.logger.info(train_loss_output) - # self._add_train_loss_to_tensorboard(epoch_idx, train_loss) - - if self.eval_step <= 0 or not valid_data: - if saved: - self._save_checkpoint(epoch_idx, verbose=verbose) - continue - - if (epoch_idx + 1) % self.eval_step == 0: - valid_start_time = time() - valid_loss = self._valid_epoch(valid_data=valid_data, show_progress=show_progress) - - (self.best_valid_score, self.cur_step, stop_flag,update_flag,) = early_stopping( - valid_loss['loss'], - self.best_valid_score, - self.cur_step, - max_step=self.stopping_step, - bigger=self.valid_metric_bigger, - ) - valid_end_time = time() - - valid_loss_output = ( - set_color("valid result", "blue") + ": \n" + dict2str(valid_loss) - ) - if verbose: - self.logger.info(valid_loss_output) - - - if update_flag: - if saved: - self._save_checkpoint(epoch_idx, verbose=verbose) - self.best_valid_result = valid_loss['loss'] - - if stop_flag: - stop_output = "Finished training, best eval result in epoch %d" % ( - epoch_idx - self.cur_step * self.eval_step - ) - if verbose: - self.logger.info(stop_output) - break - @torch.no_grad() - def evaluate(self, load_best_model=True, model_file=None): - """ - Evaluate the model based on the test data. - - args: load_best_model: bool, whether to load the best model in the training process. - model_file: str, the model file you want to evaluate. - - """ - if load_best_model: - checkpoint_file = model_file or self.saved_model_file - checkpoint = torch.load(checkpoint_file, map_location=self.device) - self.model.load_state_dict(checkpoint["state_dict"]) - self.model.load_other_parameter(checkpoint.get("other_parameter")) - message_output = "Loading model structure and parameters from {}".format( - checkpoint_file - ) - self.logger.info(message_output) - self.model.eval() - - datadict = self.model.generate_batch() - eval_result = self.evaluator.evaluate(datadict) - self.logger.info(eval_result) - - - -class Wav2LipTrainer(Trainer): - def __init__(self, config, model): - super(Wav2LipTrainer, self).__init__(config, model) - - def _train_epoch(self, train_data, epoch_idx, loss_func=None, show_progress=False): - r"""Train the model in an epoch - - Args: - train_data (DataLoader): The train data. - epoch_idx (int): The current epoch id. - loss_func (function): The loss function of :attr:`model`. If it is ``None``, the loss function will be - :attr:`self.model.calculate_loss`. Defaults to ``None``. - show_progress (bool): Show the progress of training epoch. Defaults to ``False``. - - Returns: - the averaged loss of this epoch - """ - self.model.train() - - - - loss_func = loss_func or self.model.calculate_loss - total_loss_dict = {} - step = 0 - iter_data = ( - tqdm( - train_data, - total=len(train_data), - ncols=None, - ) - if show_progress - else train_data - ) - - for batch_idx, interaction in enumerate(iter_data): - self.optimizer.zero_grad() - step += 1 - losses_dict = loss_func(interaction) - loss = losses_dict["loss"] - - for key, value in losses_dict.items(): - if key in total_loss_dict: - if not torch.is_tensor(value): - total_loss_dict[key] += value - # 如果键已经在总和字典中，累加当前值 - else: - losses_dict[key] = value.item() - total_loss_dict[key] += value.item() - else: - if not torch.is_tensor(value): - total_loss_dict[key] = value - # 否则，将当前值添加到字典中 - else: - losses_dict[key] = value.item() - total_loss_dict[key] = value.item() - iter_data.set_description(set_color(f"train {epoch_idx} {losses_dict}", "pink")) - - self._check_nan(loss) - loss.backward() - self.optimizer.step() - average_loss_dict = {} - for key, value in total_loss_dict.items(): - average_loss_dict[key] = value/step - - return average_loss_dict + print(config) + + # args store other information + self.args = args + print(self.args) + + # logger to use tensorboard + self.logger = Logger(self.args.logdir) + + # get dataloader + self.get_data_loaders() + + # init the model with config + self.build_model() + self.save_config() + + if args.load_model: + self.load_model() + + def save_model(self, iteration): + # save model and discriminator and their optimizer + torch.save(self.model.state_dict(), f'{self.args.store_model_path}.ckpt') + torch.save(self.opt.state_dict(), f'{self.args.store_model_path}.opt') + + def save_config(self): + with open(f'{self.args.store_model_path}.config.yaml', 'w') as f: + yaml.dump(self.config, f) + with open(f'{self.args.store_model_path}.args.yaml', 'w') as f: + yaml.dump(vars(self.args), f) + return + + def load_model(self): + print(f'Load model from {self.args.load_model_path}') + self.model.load_state_dict(torch.load(f'{self.args.load_model_path}.ckpt')) + self.opt.load_state_dict(torch.load(f'{self.args.load_model_path}.opt')) + return + + def get_data_loaders(self): + data_dir = self.args.data_dir + self.train_dataset = PickleDataset(os.path.join(data_dir, f'{self.args.train_set}.pkl'), + os.path.join(data_dir, self.args.train_index_file), + segment_size=self.config['data_loader']['segment_size']) + self.train_loader = get_data_loader(self.train_dataset, + frame_size=self.config['data_loader']['frame_size'], + batch_size=self.config['data_loader']['batch_size'], + shuffle=self.config['data_loader']['shuffle'], + num_workers=4, drop_last=False) + self.train_iter = infinite_iter(self.train_loader) + return + + def build_model(self): + # create model, discriminator, optimizers + self.model = cc(AE(self.config)) + print(self.model) + optimizer = self.config['optimizer'] + self.opt = torch.optim.Adam(self.model.parameters(), + lr=optimizer['lr'], betas=(optimizer['beta1'], optimizer['beta2']), + amsgrad=optimizer['amsgrad'], weight_decay=optimizer['weight_decay']) + print(self.opt) + return + + def ae_step(self, data, lambda_kl): + x = cc(data) + mu, log_sigma, emb, dec = self.model(x) + criterion = nn.L1Loss() + loss_rec = criterion(dec, x) + loss_kl = 0.5 * torch.mean(torch.exp(log_sigma) + mu ** 2 - 1 - log_sigma) + loss = self.config['lambda']['lambda_rec'] * loss_rec + \ + lambda_kl * loss_kl + self.opt.zero_grad() + loss.backward() + grad_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), + max_norm=self.config['optimizer']['grad_norm']) + self.opt.step() + meta = {'loss_rec': loss_rec.item(), + 'loss_kl': loss_kl.item(), + 'grad_norm': grad_norm} + return meta + + def train(self, n_iterations): + for iteration in range(n_iterations): + if iteration >= self.config['annealing_iters']: + lambda_kl = self.config['lambda']['lambda_kl'] + else: + lambda_kl = self.config['lambda']['lambda_kl'] * (iteration + 1) / self.config['annealing_iters'] + data = next(self.train_iter) + meta = self.ae_step(data, lambda_kl) + # add to logger + if iteration % self.args.summary_steps == 0: + self.logger.scalars_summary(f'{self.args.tag}/ae_train', meta, iteration) + loss_rec = meta['loss_rec'] + loss_kl = meta['loss_kl'] + + print(f'AE:[{iteration + 1}/{n_iterations}], loss_rec={loss_rec:.2f}, ' + f'loss_kl={loss_kl:.2f}, lambda={lambda_kl:.1e} ', end='\r') + if (iteration + 1) % self.args.save_steps == 0 or iteration + 1 == n_iterations: + self.save_model(iteration=iteration) + print() + return - - - def _valid_epoch(self, valid_data, loss_func=None, show_progress=False): - print('Valid'.format(self.eval_step)) - self.model.eval() - total_loss_dict = {} - iter_data = ( - tqdm(valid_data, - total=len(valid_data), - ncols=None, - desc=set_color("Valid", "pink") - ) - if show_progress - else valid_data - ) - step = 0 - for batch_idx, batched_data in enumerate(iter_data): - step += 1 - losses_dict = self.model.calculate_loss(batched_data, valid=True) - for key, value in losses_dict.items(): - if key in total_loss_dict: - if not torch.is_tensor(value): - total_loss_dict[key] += value - # 如果键已经在总和字典中，累加当前值 - else: - losses_dict[key] = value.item() - total_loss_dict[key] += value.item() - else: - if not torch.is_tensor(value): - total_loss_dict[key] = value - # 否则，将当前值添加到字典中 - else: - losses_dict[key] = value.item() - total_loss_dict[key] = value.item() - average_loss_dict = {} - for key, value in total_loss_dict.items(): - average_loss_dict[key] = value/step - if losses_dict["sync_loss"] < .75: - self.model.config["syncnet_wt"] = 0.01 - return average_loss_dict - \ No newline at end of file diff --git a/talkingface/utils/__init__.py b/talkingface/utils/__init__.py index 87132619..e69de29b 100644 --- a/talkingface/utils/__init__.py +++ b/talkingface/utils/__init__.py @@ -1,43 +0,0 @@ -from talkingface.utils.logger import init_logger, set_color -from talkingface.utils.enum_type import * -from talkingface.utils.utils import ( - get_local_time, - ensure_dir, - get_model, - get_trainer, - get_environment, - early_stopping, - calculate_valid_score, - dict2str, - init_seed, - get_tensorboard, - get_gpu_usage, - get_flops, - list_to_latex, - get_preprocess, - create_dataset -) - -from talkingface.utils.argument_list import * -from talkingface.utils.wandblogger import WandbLogger -__all__ = [ - "init_logger", - "get_local_time", - "ensure_dir", - "get_model", - "get_trainer", - "early_stopping", - "calculate_valid_score", - "dict2str", - "init_seed", - "general_arguments", - "training_arguments", - "evaluation_arguments", - "get_tensorboard", - "set_color", - "get_gpu_usage", - "get_flops", - "get_environment", - "list_to_latex", - "WandbLogger", -] \ No newline at end of file diff --git a/talkingface/utils/argument_list.py b/talkingface/utils/argument_list.py deleted file mode 100644 index 01a7dad3..00000000 --- a/talkingface/utils/argument_list.py +++ /dev/null @@ -1,48 +0,0 @@ -general_arguments = [ - 'gpu_id', 'use_gpu', - 'seed', - 'reproducibility', - 'state', - 'checkpoint_dir', - 'show_progress', - 'config_file', - 'log_wandb', -] - -training_arguments = [ - 'epochs', 'train_batch_size', - 'learner', 'learning_rate', - 'eval_step', 'stopping_step', - 'weight_decay', 'resume' - 'train' -] - -evaluation_arguments = [ - 'metrics', - 'temp_dir', - 'evaluate_batch_size', - 'lse_checkpoint_path', - 'valid_metric_bigger', -] -# evaluation_arguments = [ -# 'eval_args', 'repeatable', -# 'metrics', 'topk', 'valid_metric', 'valid_metric_bigger', -# 'eval_batch_size', -# 'metric_decimal_place', -# ] - -# dataset_arguments = [ -# 'field_separator', 'seq_separator', -# 'USER_ID_FIELD', 'ITEM_ID_FIELD', 'RATING_FIELD', 'TIME_FIELD', -# 'seq_len', -# 'LABEL_FIELD', 'threshold', -# 'NEG_PREFIX', -# 'ITEM_LIST_LENGTH_FIELD', 'LIST_SUFFIX', 'MAX_ITEM_LIST_LENGTH', 'POSITION_FIELD', -# 'HEAD_ENTITY_ID_FIELD', 'TAIL_ENTITY_ID_FIELD', 'RELATION_ID_FIELD', 'ENTITY_ID_FIELD', -# 'load_col', 'unload_col', 'unused_col', 'additional_feat_suffix', -# 'rm_dup_inter', 'val_interval', 'filter_inter_by_user_or_item', -# 'user_inter_num_interval', 'item_inter_num_interval', -# 'alias_of_user_id', 'alias_of_item_id', 'alias_of_entity_id', 'alias_of_relation_id', -# 'preload_weight', 'normalize_field', 'normalize_all', -# 'benchmark_filename', -# ] diff --git a/talkingface/utils/data_process.py b/talkingface/utils/data_process.py deleted file mode 100644 index cbc430ac..00000000 --- a/talkingface/utils/data_process.py +++ /dev/null @@ -1,95 +0,0 @@ -import os -import cv2 -import numpy as np -import subprocess -from tqdm import tqdm -from glob import glob -from concurrent.futures import ThreadPoolExecutor, as_completed -from talkingface.utils import face_detection -import traceback -import librosa -import librosa.filters -from scipy import signal -from scipy.io import wavfile - - -class lrs2Preprocess: - def __init__(self, config): - self.config = config - self.fa = [face_detection.FaceAlignment(face_detection.LandmarksType._2D, flip_input=False, - device=f'cuda:{id}') for id in range(config['ngpu'])] - self.template = 'ffmpeg -loglevel panic -y -i {} -strict -2 {}' - - def process_video_file(self, vfile, gpu_id): - video_stream = cv2.VideoCapture(vfile) - - frames = [] - while 1: - still_reading, frame = video_stream.read() - if not still_reading: - video_stream.release() - break - frames.append(frame) - - vidname = os.path.basename(vfile).split('.')[0] - dirname = vfile.split('/')[-2] - - fulldir = os.path.join(self.config['preprocessed_root'], dirname, vidname) - os.makedirs(fulldir, exist_ok=True) - - batches = [frames[i:i + self.config['preprocess_batch_size']] for i in range(0, len(frames), self.config['preprocess_batch_size'])] - - i = -1 - for fb in batches: - preds = self.fa[gpu_id].get_detections_for_batch(np.asarray(fb)) - - for j, f in enumerate(preds): - i += 1 - if f is None: - continue - - x1, y1, x2, y2 = f - cv2.imwrite(os.path.join(fulldir, '{}.jpg'.format(i)), fb[j][y1:y2, x1:x2]) - - - def process_audio_file(self, vfile): - vidname = os.path.basename(vfile).split('.')[0] - dirname = vfile.split('/')[-2] - - fulldir = os.path.join(self.config['preprocessed_root'], dirname, vidname) - os.makedirs(fulldir, exist_ok=True) - - wavpath = os.path.join(fulldir, 'audio.wav') - - command =self.template.format(vfile, wavpath) - subprocess.call(command, shell=True) - - def mp_handler(self, job): - vfile, gpu_id = job - try: - self.process_video_file(vfile, gpu_id) - except KeyboardInterrupt: - exit(0) - except: - traceback.print_exc() - - def run(self): - print(f'Started processing for {self.config["data_root"]} with {self.config["ngpu"]} GPUs') - - filelist = glob(os.path.join(self.config["data_root"], '*/*.mp4')) - - # jobs = [(vfile, i % self.config["ngpu"]) for i, vfile in enumerate(filelist)] - # with ThreadPoolExecutor(self.config["ngpu"]) as p: - # futures = [p.submit(self.mp_handler, j) for j in jobs] - # _ = [r.result() for r in tqdm(as_completed(futures), total=len(futures))] - - print('Dumping audios...') - for vfile in tqdm(filelist): - try: - self.process_audio_file(vfile) - except KeyboardInterrupt: - exit(0) - except: - traceback.print_exc() - continue - diff --git a/talkingface/utils/data_utils.py b/talkingface/utils/data_utils.py new file mode 100644 index 00000000..37bfaab1 --- /dev/null +++ b/talkingface/utils/data_utils.py @@ -0,0 +1,58 @@ +import torch +from torch.utils.data import Dataset +import os +import pickle +import json +import numpy as np +import torch +from torch.utils.data import DataLoader + +class CollateFn(object): + def __init__(self, frame_size): + self.frame_size = frame_size + + def make_frames(self, tensor): + out = tensor.view(tensor.size(0), tensor.size(1) // self.frame_size, self.frame_size * tensor.size(2)) + out = out.transpose(1, 2) + return out + + def __call__(self, l): + data_tensor = torch.from_numpy(np.array(l)) + segment = self.make_frames(data_tensor) + return segment + +def get_data_loader(dataset, batch_size, frame_size, shuffle=True, num_workers=4, drop_last=False): + _collate_fn = CollateFn(frame_size=frame_size) + dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, + num_workers=num_workers, collate_fn=_collate_fn, pin_memory=True) + return dataloader + +class SequenceDataset(Dataset): + def __init__(self, data): + self.data = data + self.utt_ids = list(self.data.keys()) + + def __getitem__(self, ind): + utt_id = self.utt_ids[ind] + ret = self.data[utt_id].transpose() + return ret + + def __len__(self): + return len(self.utt_ids) + +class PickleDataset(Dataset): + def __init__(self, pickle_path, sample_index_path, segment_size): + with open(pickle_path, 'rb') as f: + self.data = pickle.load(f) + with open(sample_index_path, 'r') as f: + self.indexes = json.load(f) + self.segment_size = segment_size + + def __getitem__(self, ind): + utt_id, t = self.indexes[ind] + segment = self.data[utt_id][t:t + self.segment_size] + return segment + + def __len__(self): + return len(self.indexes) + diff --git a/talkingface/utils/enum_type.py b/talkingface/utils/enum_type.py deleted file mode 100644 index b701b38b..00000000 --- a/talkingface/utils/enum_type.py +++ /dev/null @@ -1,13 +0,0 @@ -from enum import Enum - -class EvaluatorType(Enum): - """Type for evaluation metrics. - - - ``SYNC``: SYNC metrics like LSE-C, LSE-D, etc. - - ``VIDEOQ``: Video quality metrics like FID, etc. - - ``AUDIOQ``: Audio quality metrics like PESQ, etc. - """ - - SYNC = 1 - VIDEOQ = 2 - AUDIOQ = 3 \ No newline at end of file diff --git a/talkingface/utils/face_detection/README.md b/talkingface/utils/face_detection/README.md deleted file mode 100644 index c073376e..00000000 --- a/talkingface/utils/face_detection/README.md +++ /dev/null @@ -1 +0,0 @@ -The code for Face Detection in this folder has been taken from the wonderful [face_alignment](https://github.com/1adrianb/face-alignment) repository. This has been modified to take batches of faces at a time. \ No newline at end of file diff --git a/talkingface/utils/face_detection/__init__.py b/talkingface/utils/face_detection/__init__.py deleted file mode 100644 index 4bae29fd..00000000 --- a/talkingface/utils/face_detection/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# -*- coding: utf-8 -*- - -__author__ = """Adrian Bulat""" -__email__ = 'adrian.bulat@nottingham.ac.uk' -__version__ = '1.0.1' - -from .api import FaceAlignment, LandmarksType, NetworkSize diff --git a/talkingface/utils/face_detection/api.py b/talkingface/utils/face_detection/api.py deleted file mode 100644 index dc4dd4f2..00000000 --- a/talkingface/utils/face_detection/api.py +++ /dev/null @@ -1,79 +0,0 @@ -from __future__ import print_function -import os -import torch -from torch.utils.model_zoo import load_url -from enum import Enum -import numpy as np -import cv2 -try: - import urllib.request as request_file -except BaseException: - import urllib as request_file - -# from .models import FAN, ResNetDepth -# from .utils import * - - -class LandmarksType(Enum): - """Enum class defining the type of landmarks to detect. - - ``_2D`` - the detected points ``(x,y)`` are detected in a 2D space and follow the visible contour of the face - ``_2halfD`` - this points represent the projection of the 3D points into 3D - ``_3D`` - detect the points ``(x,y,z)``` in a 3D space - - """ - _2D = 1 - _2halfD = 2 - _3D = 3 - - -class NetworkSize(Enum): - # TINY = 1 - # SMALL = 2 - # MEDIUM = 3 - LARGE = 4 - - def __new__(cls, value): - member = object.__new__(cls) - member._value_ = value - return member - - def __int__(self): - return self.value - -ROOT = os.path.dirname(os.path.abspath(__file__)) - -class FaceAlignment: - def __init__(self, landmarks_type, network_size=NetworkSize.LARGE, - device='cuda', flip_input=False, face_detector='sfd', verbose=False): - self.device = device - self.flip_input = flip_input - self.landmarks_type = landmarks_type - self.verbose = verbose - - network_size = int(network_size) - - if 'cuda' in device: - torch.backends.cudnn.benchmark = True - - # Get the face detector - face_detector_module = __import__('talkingface.utils.face_detection.detection.' + face_detector, - globals(), locals(), [face_detector], 0) - self.face_detector = face_detector_module.FaceDetector(device=device, verbose=verbose) - - def get_detections_for_batch(self, images): - images = images[..., ::-1] - detected_faces = self.face_detector.detect_from_batch(images.copy()) - results = [] - - for i, d in enumerate(detected_faces): - if len(d) == 0: - results.append(None) - continue - d = d[0] - d = np.clip(d, 0, None) - - x1, y1, x2, y2 = map(int, d[:-1]) - results.append((x1, y1, x2, y2)) - - return results \ No newline at end of file diff --git a/talkingface/utils/face_detection/detection/__init__.py b/talkingface/utils/face_detection/detection/__init__.py deleted file mode 100644 index 1a6b0402..00000000 --- a/talkingface/utils/face_detection/detection/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .core import FaceDetector \ No newline at end of file diff --git a/talkingface/utils/face_detection/detection/core.py b/talkingface/utils/face_detection/detection/core.py deleted file mode 100644 index 0f8275e8..00000000 --- a/talkingface/utils/face_detection/detection/core.py +++ /dev/null @@ -1,130 +0,0 @@ -import logging -import glob -from tqdm import tqdm -import numpy as np -import torch -import cv2 - - -class FaceDetector(object): - """An abstract class representing a face detector. - - Any other face detection implementation must subclass it. All subclasses - must implement ``detect_from_image``, that return a list of detected - bounding boxes. Optionally, for speed considerations detect from path is - recommended. - """ - - def __init__(self, device, verbose): - self.device = device - self.verbose = verbose - - if verbose: - if 'cpu' in device: - logger = logging.getLogger(__name__) - logger.warning("Detection running on CPU, this may be potentially slow.") - - if 'cpu' not in device and 'cuda' not in device: - if verbose: - logger.error("Expected values for device are: {cpu, cuda} but got: %s", device) - raise ValueError - - def detect_from_image(self, tensor_or_path): - """Detects faces in a given image. - - This function detects the faces present in a provided BGR(usually) - image. The input can be either the image itself or the path to it. - - Arguments: - tensor_or_path {numpy.ndarray, torch.tensor or string} -- the path - to an image or the image itself. - - Example:: - - >>> path_to_image = 'data/image_01.jpg' - ... detected_faces = detect_from_image(path_to_image) - [A list of bounding boxes (x1, y1, x2, y2)] - >>> image = cv2.imread(path_to_image) - ... detected_faces = detect_from_image(image) - [A list of bounding boxes (x1, y1, x2, y2)] - - """ - raise NotImplementedError - - def detect_from_directory(self, path, extensions=['.jpg', '.png'], recursive=False, show_progress_bar=True): - """Detects faces from all the images present in a given directory. - - Arguments: - path {string} -- a string containing a path that points to the folder containing the images - - Keyword Arguments: - extensions {list} -- list of string containing the extensions to be - consider in the following format: ``.extension_name`` (default: - {['.jpg', '.png']}) recursive {bool} -- option wherever to scan the - folder recursively (default: {False}) show_progress_bar {bool} -- - display a progressbar (default: {True}) - - Example: - >>> directory = 'data' - ... detected_faces = detect_from_directory(directory) - {A dictionary of [lists containing bounding boxes(x1, y1, x2, y2)]} - - """ - if self.verbose: - logger = logging.getLogger(__name__) - - if len(extensions) == 0: - if self.verbose: - logger.error("Expected at list one extension, but none was received.") - raise ValueError - - if self.verbose: - logger.info("Constructing the list of images.") - additional_pattern = '/**/*' if recursive else '/*' - files = [] - for extension in extensions: - files.extend(glob.glob(path + additional_pattern + extension, recursive=recursive)) - - if self.verbose: - logger.info("Finished searching for images. %s images found", len(files)) - logger.info("Preparing to run the detection.") - - predictions = {} - for image_path in tqdm(files, disable=not show_progress_bar): - if self.verbose: - logger.info("Running the face detector on image: %s", image_path) - predictions[image_path] = self.detect_from_image(image_path) - - if self.verbose: - logger.info("The detector was successfully run on all %s images", len(files)) - - return predictions - - @property - def reference_scale(self): - raise NotImplementedError - - @property - def reference_x_shift(self): - raise NotImplementedError - - @property - def reference_y_shift(self): - raise NotImplementedError - - @staticmethod - def tensor_or_path_to_ndarray(tensor_or_path, rgb=True): - """Convert path (represented as a string) or torch.tensor to a numpy.ndarray - - Arguments: - tensor_or_path {numpy.ndarray, torch.tensor or string} -- path to the image, or the image itself - """ - if isinstance(tensor_or_path, str): - return cv2.imread(tensor_or_path) if not rgb else cv2.imread(tensor_or_path)[..., ::-1] - elif torch.is_tensor(tensor_or_path): - # Call cpu in case its coming from cuda - return tensor_or_path.cpu().numpy()[..., ::-1].copy() if not rgb else tensor_or_path.cpu().numpy() - elif isinstance(tensor_or_path, np.ndarray): - return tensor_or_path[..., ::-1].copy() if not rgb else tensor_or_path - else: - raise TypeError diff --git a/talkingface/utils/face_detection/detection/sfd/__init__.py b/talkingface/utils/face_detection/detection/sfd/__init__.py deleted file mode 100644 index 5a63ecd4..00000000 --- a/talkingface/utils/face_detection/detection/sfd/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .sfd_detector import SFDDetector as FaceDetector \ No newline at end of file diff --git a/talkingface/utils/face_detection/detection/sfd/bbox.py b/talkingface/utils/face_detection/detection/sfd/bbox.py deleted file mode 100644 index 4bd7222e..00000000 --- a/talkingface/utils/face_detection/detection/sfd/bbox.py +++ /dev/null @@ -1,129 +0,0 @@ -from __future__ import print_function -import os -import sys -import cv2 -import random -import datetime -import time -import math -import argparse -import numpy as np -import torch - -try: - from iou import IOU -except BaseException: - # IOU cython speedup 10x - def IOU(ax1, ay1, ax2, ay2, bx1, by1, bx2, by2): - sa = abs((ax2 - ax1) * (ay2 - ay1)) - sb = abs((bx2 - bx1) * (by2 - by1)) - x1, y1 = max(ax1, bx1), max(ay1, by1) - x2, y2 = min(ax2, bx2), min(ay2, by2) - w = x2 - x1 - h = y2 - y1 - if w < 0 or h < 0: - return 0.0 - else: - return 1.0 * w * h / (sa + sb - w * h) - - -def bboxlog(x1, y1, x2, y2, axc, ayc, aww, ahh): - xc, yc, ww, hh = (x2 + x1) / 2, (y2 + y1) / 2, x2 - x1, y2 - y1 - dx, dy = (xc - axc) / aww, (yc - ayc) / ahh - dw, dh = math.log(ww / aww), math.log(hh / ahh) - return dx, dy, dw, dh - - -def bboxloginv(dx, dy, dw, dh, axc, ayc, aww, ahh): - xc, yc = dx * aww + axc, dy * ahh + ayc - ww, hh = math.exp(dw) * aww, math.exp(dh) * ahh - x1, x2, y1, y2 = xc - ww / 2, xc + ww / 2, yc - hh / 2, yc + hh / 2 - return x1, y1, x2, y2 - - -def nms(dets, thresh): - if 0 == len(dets): - return [] - x1, y1, x2, y2, scores = dets[:, 0], dets[:, 1], dets[:, 2], dets[:, 3], dets[:, 4] - areas = (x2 - x1 + 1) * (y2 - y1 + 1) - order = scores.argsort()[::-1] - - keep = [] - while order.size > 0: - i = order[0] - keep.append(i) - xx1, yy1 = np.maximum(x1[i], x1[order[1:]]), np.maximum(y1[i], y1[order[1:]]) - xx2, yy2 = np.minimum(x2[i], x2[order[1:]]), np.minimum(y2[i], y2[order[1:]]) - - w, h = np.maximum(0.0, xx2 - xx1 + 1), np.maximum(0.0, yy2 - yy1 + 1) - ovr = w * h / (areas[i] + areas[order[1:]] - w * h) - - inds = np.where(ovr <= thresh)[0] - order = order[inds + 1] - - return keep - - -def encode(matched, priors, variances): - """Encode the variances from the priorbox layers into the ground truth boxes - we have matched (based on jaccard overlap) with the prior boxes. - Args: - matched: (tensor) Coords of ground truth for each prior in point-form - Shape: [num_priors, 4]. - priors: (tensor) Prior boxes in center-offset form - Shape: [num_priors,4]. - variances: (list[float]) Variances of priorboxes - Return: - encoded boxes (tensor), Shape: [num_priors, 4] - """ - - # dist b/t match center and prior's center - g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2] - # encode variance - g_cxcy /= (variances[0] * priors[:, 2:]) - # match wh / prior wh - g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:] - g_wh = torch.log(g_wh) / variances[1] - # return target for smooth_l1_loss - return torch.cat([g_cxcy, g_wh], 1) # [num_priors,4] - - -def decode(loc, priors, variances): - """Decode locations from predictions using priors to undo - the encoding we did for offset regression at train time. - Args: - loc (tensor): location predictions for loc layers, - Shape: [num_priors,4] - priors (tensor): Prior boxes in center-offset form. - Shape: [num_priors,4]. - variances: (list[float]) Variances of priorboxes - Return: - decoded bounding box predictions - """ - - boxes = torch.cat(( - priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:], - priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1) - boxes[:, :2] -= boxes[:, 2:] / 2 - boxes[:, 2:] += boxes[:, :2] - return boxes - -def batch_decode(loc, priors, variances): - """Decode locations from predictions using priors to undo - the encoding we did for offset regression at train time. - Args: - loc (tensor): location predictions for loc layers, - Shape: [num_priors,4] - priors (tensor): Prior boxes in center-offset form. - Shape: [num_priors,4]. - variances: (list[float]) Variances of priorboxes - Return: - decoded bounding box predictions - """ - - boxes = torch.cat(( - priors[:, :, :2] + loc[:, :, :2] * variances[0] * priors[:, :, 2:], - priors[:, :, 2:] * torch.exp(loc[:, :, 2:] * variances[1])), 2) - boxes[:, :, :2] -= boxes[:, :, 2:] / 2 - boxes[:, :, 2:] += boxes[:, :, :2] - return boxes diff --git a/talkingface/utils/face_detection/detection/sfd/detect.py b/talkingface/utils/face_detection/detection/sfd/detect.py deleted file mode 100644 index efef6273..00000000 --- a/talkingface/utils/face_detection/detection/sfd/detect.py +++ /dev/null @@ -1,112 +0,0 @@ -import torch -import torch.nn.functional as F - -import os -import sys -import cv2 -import random -import datetime -import math -import argparse -import numpy as np - -import scipy.io as sio -import zipfile -from .net_s3fd import s3fd -from .bbox import * - - -def detect(net, img, device): - img = img - np.array([104, 117, 123]) - img = img.transpose(2, 0, 1) - img = img.reshape((1,) + img.shape) - - if 'cuda' in device: - torch.backends.cudnn.benchmark = True - - img = torch.from_numpy(img).float().to(device) - BB, CC, HH, WW = img.size() - with torch.no_grad(): - olist = net(img) - - bboxlist = [] - for i in range(len(olist) // 2): - olist[i * 2] = F.softmax(olist[i * 2], dim=1) - olist = [oelem.data.cpu() for oelem in olist] - for i in range(len(olist) // 2): - ocls, oreg = olist[i * 2], olist[i * 2 + 1] - FB, FC, FH, FW = ocls.size() # feature map size - stride = 2**(i + 2) # 4,8,16,32,64,128 - anchor = stride * 4 - poss = zip(*np.where(ocls[:, 1, :, :] > 0.05)) - for Iindex, hindex, windex in poss: - axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride - score = ocls[0, 1, hindex, windex] - loc = oreg[0, :, hindex, windex].contiguous().view(1, 4) - priors = torch.Tensor([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]]) - variances = [0.1, 0.2] - box = decode(loc, priors, variances) - x1, y1, x2, y2 = box[0] * 1.0 - # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1) - bboxlist.append([x1, y1, x2, y2, score]) - bboxlist = np.array(bboxlist) - if 0 == len(bboxlist): - bboxlist = np.zeros((1, 5)) - - return bboxlist - -def batch_detect(net, imgs, device): - imgs = imgs - np.array([104, 117, 123]) - imgs = imgs.transpose(0, 3, 1, 2) - - if 'cuda' in device: - torch.backends.cudnn.benchmark = True - - imgs = torch.from_numpy(imgs).float().to(device) - BB, CC, HH, WW = imgs.size() - with torch.no_grad(): - olist = net(imgs) - - bboxlist = [] - for i in range(len(olist) // 2): - olist[i * 2] = F.softmax(olist[i * 2], dim=1) - olist = [oelem.data.cpu() for oelem in olist] - for i in range(len(olist) // 2): - ocls, oreg = olist[i * 2], olist[i * 2 + 1] - FB, FC, FH, FW = ocls.size() # feature map size - stride = 2**(i + 2) # 4,8,16,32,64,128 - anchor = stride * 4 - poss = zip(*np.where(ocls[:, 1, :, :] > 0.05)) - for Iindex, hindex, windex in poss: - axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride - score = ocls[:, 1, hindex, windex] - loc = oreg[:, :, hindex, windex].contiguous().view(BB, 1, 4) - priors = torch.Tensor([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]]).view(1, 1, 4) - variances = [0.1, 0.2] - box = batch_decode(loc, priors, variances) - box = box[:, 0] * 1.0 - # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1) - bboxlist.append(torch.cat([box, score.unsqueeze(1)], 1).cpu().numpy()) - bboxlist = np.array(bboxlist) - if 0 == len(bboxlist): - bboxlist = np.zeros((1, BB, 5)) - - return bboxlist - -def flip_detect(net, img, device): - img = cv2.flip(img, 1) - b = detect(net, img, device) - - bboxlist = np.zeros(b.shape) - bboxlist[:, 0] = img.shape[1] - b[:, 2] - bboxlist[:, 1] = b[:, 1] - bboxlist[:, 2] = img.shape[1] - b[:, 0] - bboxlist[:, 3] = b[:, 3] - bboxlist[:, 4] = b[:, 4] - return bboxlist - - -def pts_to_bb(pts): - min_x, min_y = np.min(pts, axis=0) - max_x, max_y = np.max(pts, axis=0) - return np.array([min_x, min_y, max_x, max_y]) diff --git a/talkingface/utils/face_detection/detection/sfd/net_s3fd.py b/talkingface/utils/face_detection/detection/sfd/net_s3fd.py deleted file mode 100644 index fc64313c..00000000 --- a/talkingface/utils/face_detection/detection/sfd/net_s3fd.py +++ /dev/null @@ -1,129 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class L2Norm(nn.Module): - def __init__(self, n_channels, scale=1.0): - super(L2Norm, self).__init__() - self.n_channels = n_channels - self.scale = scale - self.eps = 1e-10 - self.weight = nn.Parameter(torch.Tensor(self.n_channels)) - self.weight.data *= 0.0 - self.weight.data += self.scale - - def forward(self, x): - norm = x.pow(2).sum(dim=1, keepdim=True).sqrt() + self.eps - x = x / norm * self.weight.view(1, -1, 1, 1) - return x - - -class s3fd(nn.Module): - def __init__(self): - super(s3fd, self).__init__() - self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1) - self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1) - - self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1) - self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1) - - self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1) - self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1) - self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1) - - self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1) - self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) - self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) - - self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) - self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) - self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) - - self.fc6 = nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=3) - self.fc7 = nn.Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0) - - self.conv6_1 = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0) - self.conv6_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1) - - self.conv7_1 = nn.Conv2d(512, 128, kernel_size=1, stride=1, padding=0) - self.conv7_2 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1) - - self.conv3_3_norm = L2Norm(256, scale=10) - self.conv4_3_norm = L2Norm(512, scale=8) - self.conv5_3_norm = L2Norm(512, scale=5) - - self.conv3_3_norm_mbox_conf = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1) - self.conv3_3_norm_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1) - self.conv4_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1) - self.conv4_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1) - self.conv5_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1) - self.conv5_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1) - - self.fc7_mbox_conf = nn.Conv2d(1024, 2, kernel_size=3, stride=1, padding=1) - self.fc7_mbox_loc = nn.Conv2d(1024, 4, kernel_size=3, stride=1, padding=1) - self.conv6_2_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1) - self.conv6_2_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1) - self.conv7_2_mbox_conf = nn.Conv2d(256, 2, kernel_size=3, stride=1, padding=1) - self.conv7_2_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1) - - def forward(self, x): - h = F.relu(self.conv1_1(x)) - h = F.relu(self.conv1_2(h)) - h = F.max_pool2d(h, 2, 2) - - h = F.relu(self.conv2_1(h)) - h = F.relu(self.conv2_2(h)) - h = F.max_pool2d(h, 2, 2) - - h = F.relu(self.conv3_1(h)) - h = F.relu(self.conv3_2(h)) - h = F.relu(self.conv3_3(h)) - f3_3 = h - h = F.max_pool2d(h, 2, 2) - - h = F.relu(self.conv4_1(h)) - h = F.relu(self.conv4_2(h)) - h = F.relu(self.conv4_3(h)) - f4_3 = h - h = F.max_pool2d(h, 2, 2) - - h = F.relu(self.conv5_1(h)) - h = F.relu(self.conv5_2(h)) - h = F.relu(self.conv5_3(h)) - f5_3 = h - h = F.max_pool2d(h, 2, 2) - - h = F.relu(self.fc6(h)) - h = F.relu(self.fc7(h)) - ffc7 = h - h = F.relu(self.conv6_1(h)) - h = F.relu(self.conv6_2(h)) - f6_2 = h - h = F.relu(self.conv7_1(h)) - h = F.relu(self.conv7_2(h)) - f7_2 = h - - f3_3 = self.conv3_3_norm(f3_3) - f4_3 = self.conv4_3_norm(f4_3) - f5_3 = self.conv5_3_norm(f5_3) - - cls1 = self.conv3_3_norm_mbox_conf(f3_3) - reg1 = self.conv3_3_norm_mbox_loc(f3_3) - cls2 = self.conv4_3_norm_mbox_conf(f4_3) - reg2 = self.conv4_3_norm_mbox_loc(f4_3) - cls3 = self.conv5_3_norm_mbox_conf(f5_3) - reg3 = self.conv5_3_norm_mbox_loc(f5_3) - cls4 = self.fc7_mbox_conf(ffc7) - reg4 = self.fc7_mbox_loc(ffc7) - cls5 = self.conv6_2_mbox_conf(f6_2) - reg5 = self.conv6_2_mbox_loc(f6_2) - cls6 = self.conv7_2_mbox_conf(f7_2) - reg6 = self.conv7_2_mbox_loc(f7_2) - - # max-out background label - chunk = torch.chunk(cls1, 4, 1) - bmax = torch.max(torch.max(chunk[0], chunk[1]), chunk[2]) - cls1 = torch.cat([bmax, chunk[3]], dim=1) - - return [cls1, reg1, cls2, reg2, cls3, reg3, cls4, reg4, cls5, reg5, cls6, reg6] diff --git a/talkingface/utils/face_detection/detection/sfd/sfd_detector.py b/talkingface/utils/face_detection/detection/sfd/sfd_detector.py deleted file mode 100644 index 8fbce152..00000000 --- a/talkingface/utils/face_detection/detection/sfd/sfd_detector.py +++ /dev/null @@ -1,59 +0,0 @@ -import os -import cv2 -from torch.utils.model_zoo import load_url - -from ..core import FaceDetector - -from .net_s3fd import s3fd -from .bbox import * -from .detect import * - -models_urls = { - 's3fd': 'https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth', -} - - -class SFDDetector(FaceDetector): - def __init__(self, device, path_to_detector=os.path.join(os.path.dirname(os.path.abspath(__file__)), 's3fd.pth'), verbose=False): - super(SFDDetector, self).__init__(device, verbose) - - # Initialise the face detector - if not os.path.isfile(path_to_detector): - model_weights = load_url(models_urls['s3fd']) - else: - model_weights = torch.load(path_to_detector) - - self.face_detector = s3fd() - self.face_detector.load_state_dict(model_weights) - self.face_detector.to(device) - self.face_detector.eval() - - def detect_from_image(self, tensor_or_path): - image = self.tensor_or_path_to_ndarray(tensor_or_path) - - bboxlist = detect(self.face_detector, image, device=self.device) - keep = nms(bboxlist, 0.3) - bboxlist = bboxlist[keep, :] - bboxlist = [x for x in bboxlist if x[-1] > 0.5] - - return bboxlist - - def detect_from_batch(self, images): - bboxlists = batch_detect(self.face_detector, images, device=self.device) - keeps = [nms(bboxlists[:, i, :], 0.3) for i in range(bboxlists.shape[1])] - bboxlists = [bboxlists[keep, i, :] for i, keep in enumerate(keeps)] - bboxlists = [[x for x in bboxlist if x[-1] > 0.5] for bboxlist in bboxlists] - - return bboxlists - - @property - def reference_scale(self): - return 195 - - @property - def reference_x_shift(self): - return 0 - - @property - def reference_y_shift(self): - return 0 diff --git a/talkingface/utils/face_detection/models.py b/talkingface/utils/face_detection/models.py deleted file mode 100644 index ee2dde32..00000000 --- a/talkingface/utils/face_detection/models.py +++ /dev/null @@ -1,261 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -import math - - -def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False): - "3x3 convolution with padding" - return nn.Conv2d(in_planes, out_planes, kernel_size=3, - stride=strd, padding=padding, bias=bias) - - -class ConvBlock(nn.Module): - def __init__(self, in_planes, out_planes): - super(ConvBlock, self).__init__() - self.bn1 = nn.BatchNorm2d(in_planes) - self.conv1 = conv3x3(in_planes, int(out_planes / 2)) - self.bn2 = nn.BatchNorm2d(int(out_planes / 2)) - self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4)) - self.bn3 = nn.BatchNorm2d(int(out_planes / 4)) - self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4)) - - if in_planes != out_planes: - self.downsample = nn.Sequential( - nn.BatchNorm2d(in_planes), - nn.ReLU(True), - nn.Conv2d(in_planes, out_planes, - kernel_size=1, stride=1, bias=False), - ) - else: - self.downsample = None - - def forward(self, x): - residual = x - - out1 = self.bn1(x) - out1 = F.relu(out1, True) - out1 = self.conv1(out1) - - out2 = self.bn2(out1) - out2 = F.relu(out2, True) - out2 = self.conv2(out2) - - out3 = self.bn3(out2) - out3 = F.relu(out3, True) - out3 = self.conv3(out3) - - out3 = torch.cat((out1, out2, out3), 1) - - if self.downsample is not None: - residual = self.downsample(residual) - - out3 += residual - - return out3 - - -class Bottleneck(nn.Module): - - expansion = 4 - - def __init__(self, inplanes, planes, stride=1, downsample=None): - super(Bottleneck, self).__init__() - self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) - self.bn1 = nn.BatchNorm2d(planes) - self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, - padding=1, bias=False) - self.bn2 = nn.BatchNorm2d(planes) - self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) - self.bn3 = nn.BatchNorm2d(planes * 4) - self.relu = nn.ReLU(inplace=True) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - residual = 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: - residual = self.downsample(x) - - out += residual - out = self.relu(out) - - return out - - -class HourGlass(nn.Module): - def __init__(self, num_modules, depth, num_features): - super(HourGlass, self).__init__() - self.num_modules = num_modules - self.depth = depth - self.features = num_features - - self._generate_network(self.depth) - - def _generate_network(self, level): - self.add_module('b1_' + str(level), ConvBlock(self.features, self.features)) - - self.add_module('b2_' + str(level), ConvBlock(self.features, self.features)) - - if level > 1: - self._generate_network(level - 1) - else: - self.add_module('b2_plus_' + str(level), ConvBlock(self.features, self.features)) - - self.add_module('b3_' + str(level), ConvBlock(self.features, self.features)) - - def _forward(self, level, inp): - # Upper branch - up1 = inp - up1 = self._modules['b1_' + str(level)](up1) - - # Lower branch - low1 = F.avg_pool2d(inp, 2, stride=2) - low1 = self._modules['b2_' + str(level)](low1) - - if level > 1: - low2 = self._forward(level - 1, low1) - else: - low2 = low1 - low2 = self._modules['b2_plus_' + str(level)](low2) - - low3 = low2 - low3 = self._modules['b3_' + str(level)](low3) - - up2 = F.interpolate(low3, scale_factor=2, mode='nearest') - - return up1 + up2 - - def forward(self, x): - return self._forward(self.depth, x) - - -class FAN(nn.Module): - - def __init__(self, num_modules=1): - super(FAN, self).__init__() - self.num_modules = num_modules - - # Base part - self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3) - self.bn1 = nn.BatchNorm2d(64) - self.conv2 = ConvBlock(64, 128) - self.conv3 = ConvBlock(128, 128) - self.conv4 = ConvBlock(128, 256) - - # Stacking part - for hg_module in range(self.num_modules): - self.add_module('m' + str(hg_module), HourGlass(1, 4, 256)) - self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256)) - self.add_module('conv_last' + str(hg_module), - nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) - self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256)) - self.add_module('l' + str(hg_module), nn.Conv2d(256, - 68, kernel_size=1, stride=1, padding=0)) - - if hg_module < self.num_modules - 1: - self.add_module( - 'bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) - self.add_module('al' + str(hg_module), nn.Conv2d(68, - 256, kernel_size=1, stride=1, padding=0)) - - def forward(self, x): - x = F.relu(self.bn1(self.conv1(x)), True) - x = F.avg_pool2d(self.conv2(x), 2, stride=2) - x = self.conv3(x) - x = self.conv4(x) - - previous = x - - outputs = [] - for i in range(self.num_modules): - hg = self._modules['m' + str(i)](previous) - - ll = hg - ll = self._modules['top_m_' + str(i)](ll) - - ll = F.relu(self._modules['bn_end' + str(i)] - (self._modules['conv_last' + str(i)](ll)), True) - - # Predict heatmaps - tmp_out = self._modules['l' + str(i)](ll) - outputs.append(tmp_out) - - if i < self.num_modules - 1: - ll = self._modules['bl' + str(i)](ll) - tmp_out_ = self._modules['al' + str(i)](tmp_out) - previous = previous + ll + tmp_out_ - - return outputs - - -class ResNetDepth(nn.Module): - - def __init__(self, block=Bottleneck, layers=[3, 8, 36, 3], num_classes=68): - self.inplanes = 64 - super(ResNetDepth, self).__init__() - self.conv1 = nn.Conv2d(3 + 68, 64, kernel_size=7, stride=2, padding=3, - bias=False) - self.bn1 = nn.BatchNorm2d(64) - 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) - self.layer3 = self._make_layer(block, 256, layers[2], stride=2) - self.layer4 = self._make_layer(block, 512, layers[3], stride=2) - self.avgpool = nn.AvgPool2d(7) - self.fc = nn.Linear(512 * block.expansion, num_classes) - - for m in self.modules(): - if isinstance(m, nn.Conv2d): - n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels - m.weight.data.normal_(0, math.sqrt(2. / n)) - elif isinstance(m, nn.BatchNorm2d): - m.weight.data.fill_(1) - m.bias.data.zero_() - - def _make_layer(self, block, planes, blocks, stride=1): - downsample = None - if stride != 1 or self.inplanes != planes * block.expansion: - downsample = nn.Sequential( - nn.Conv2d(self.inplanes, planes * block.expansion, - kernel_size=1, stride=stride, bias=False), - nn.BatchNorm2d(planes * block.expansion), - ) - - layers = [] - layers.append(block(self.inplanes, planes, stride, downsample)) - self.inplanes = planes * block.expansion - for i in range(1, blocks): - layers.append(block(self.inplanes, planes)) - - 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 = x.view(x.size(0), -1) - x = self.fc(x) - - return x diff --git a/talkingface/utils/face_detection/utils.py b/talkingface/utils/face_detection/utils.py deleted file mode 100644 index 3dc4cf3e..00000000 --- a/talkingface/utils/face_detection/utils.py +++ /dev/null @@ -1,313 +0,0 @@ -from __future__ import print_function -import os -import sys -import time -import torch -import math -import numpy as np -import cv2 - - -def _gaussian( - size=3, sigma=0.25, amplitude=1, normalize=False, width=None, - height=None, sigma_horz=None, sigma_vert=None, mean_horz=0.5, - mean_vert=0.5): - # handle some defaults - if width is None: - width = size - if height is None: - height = size - if sigma_horz is None: - sigma_horz = sigma - if sigma_vert is None: - sigma_vert = sigma - center_x = mean_horz * width + 0.5 - center_y = mean_vert * height + 0.5 - gauss = np.empty((height, width), dtype=np.float32) - # generate kernel - for i in range(height): - for j in range(width): - gauss[i][j] = amplitude * math.exp(-(math.pow((j + 1 - center_x) / ( - sigma_horz * width), 2) / 2.0 + math.pow((i + 1 - center_y) / (sigma_vert * height), 2) / 2.0)) - if normalize: - gauss = gauss / np.sum(gauss) - return gauss - - -def draw_gaussian(image, point, sigma): - # Check if the gaussian is inside - ul = [math.floor(point[0] - 3 * sigma), math.floor(point[1] - 3 * sigma)] - br = [math.floor(point[0] + 3 * sigma), math.floor(point[1] + 3 * sigma)] - if (ul[0] > image.shape[1] or ul[1] > image.shape[0] or br[0] < 1 or br[1] < 1): - return image - size = 6 * sigma + 1 - g = _gaussian(size) - g_x = [int(max(1, -ul[0])), int(min(br[0], image.shape[1])) - int(max(1, ul[0])) + int(max(1, -ul[0]))] - g_y = [int(max(1, -ul[1])), int(min(br[1], image.shape[0])) - int(max(1, ul[1])) + int(max(1, -ul[1]))] - img_x = [int(max(1, ul[0])), int(min(br[0], image.shape[1]))] - img_y = [int(max(1, ul[1])), int(min(br[1], image.shape[0]))] - assert (g_x[0] > 0 and g_y[1] > 0) - image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1] - ] = image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]] + g[g_y[0] - 1:g_y[1], g_x[0] - 1:g_x[1]] - image[image > 1] = 1 - return image - - -def transform(point, center, scale, resolution, invert=False): - """Generate and affine transformation matrix. - - Given a set of points, a center, a scale and a targer resolution, the - function generates and affine transformation matrix. If invert is ``True`` - it will produce the inverse transformation. - - Arguments: - point {torch.tensor} -- the input 2D point - center {torch.tensor or numpy.array} -- the center around which to perform the transformations - scale {float} -- the scale of the face/object - resolution {float} -- the output resolution - - Keyword Arguments: - invert {bool} -- define wherever the function should produce the direct or the - inverse transformation matrix (default: {False}) - """ - _pt = torch.ones(3) - _pt[0] = point[0] - _pt[1] = point[1] - - h = 200.0 * scale - t = torch.eye(3) - t[0, 0] = resolution / h - t[1, 1] = resolution / h - t[0, 2] = resolution * (-center[0] / h + 0.5) - t[1, 2] = resolution * (-center[1] / h + 0.5) - - if invert: - t = torch.inverse(t) - - new_point = (torch.matmul(t, _pt))[0:2] - - return new_point.int() - - -def crop(image, center, scale, resolution=256.0): - """Center crops an image or set of heatmaps - - Arguments: - image {numpy.array} -- an rgb image - center {numpy.array} -- the center of the object, usually the same as of the bounding box - scale {float} -- scale of the face - - Keyword Arguments: - resolution {float} -- the size of the output cropped image (default: {256.0}) - - Returns: - [type] -- [description] - """ # Crop around the center point - """ Crops the image around the center. Input is expected to be an np.ndarray """ - ul = transform([1, 1], center, scale, resolution, True) - br = transform([resolution, resolution], center, scale, resolution, True) - # pad = math.ceil(torch.norm((ul - br).float()) / 2.0 - (br[0] - ul[0]) / 2.0) - if image.ndim > 2: - newDim = np.array([br[1] - ul[1], br[0] - ul[0], - image.shape[2]], dtype=np.int32) - newImg = np.zeros(newDim, dtype=np.uint8) - else: - newDim = np.array([br[1] - ul[1], br[0] - ul[0]], dtype=np.int) - newImg = np.zeros(newDim, dtype=np.uint8) - ht = image.shape[0] - wd = image.shape[1] - newX = np.array( - [max(1, -ul[0] + 1), min(br[0], wd) - ul[0]], dtype=np.int32) - newY = np.array( - [max(1, -ul[1] + 1), min(br[1], ht) - ul[1]], dtype=np.int32) - oldX = np.array([max(1, ul[0] + 1), min(br[0], wd)], dtype=np.int32) - oldY = np.array([max(1, ul[1] + 1), min(br[1], ht)], dtype=np.int32) - newImg[newY[0] - 1:newY[1], newX[0] - 1:newX[1] - ] = image[oldY[0] - 1:oldY[1], oldX[0] - 1:oldX[1], :] - newImg = cv2.resize(newImg, dsize=(int(resolution), int(resolution)), - interpolation=cv2.INTER_LINEAR) - return newImg - - -def get_preds_fromhm(hm, center=None, scale=None): - """Obtain (x,y) coordinates given a set of N heatmaps. If the center - and the scale is provided the function will return the points also in - the original coordinate frame. - - Arguments: - hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H] - - Keyword Arguments: - center {torch.tensor} -- the center of the bounding box (default: {None}) - scale {float} -- face scale (default: {None}) - """ - max, idx = torch.max( - hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2) - idx += 1 - preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float() - preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1) - preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1) - - for i in range(preds.size(0)): - for j in range(preds.size(1)): - hm_ = hm[i, j, :] - pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1 - if pX > 0 and pX < 63 and pY > 0 and pY < 63: - diff = torch.FloatTensor( - [hm_[pY, pX + 1] - hm_[pY, pX - 1], - hm_[pY + 1, pX] - hm_[pY - 1, pX]]) - preds[i, j].add_(diff.sign_().mul_(.25)) - - preds.add_(-.5) - - preds_orig = torch.zeros(preds.size()) - if center is not None and scale is not None: - for i in range(hm.size(0)): - for j in range(hm.size(1)): - preds_orig[i, j] = transform( - preds[i, j], center, scale, hm.size(2), True) - - return preds, preds_orig - -def get_preds_fromhm_batch(hm, centers=None, scales=None): - """Obtain (x,y) coordinates given a set of N heatmaps. If the centers - and the scales is provided the function will return the points also in - the original coordinate frame. - - Arguments: - hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H] - - Keyword Arguments: - centers {torch.tensor} -- the centers of the bounding box (default: {None}) - scales {float} -- face scales (default: {None}) - """ - max, idx = torch.max( - hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2) - idx += 1 - preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float() - preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1) - preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1) - - for i in range(preds.size(0)): - for j in range(preds.size(1)): - hm_ = hm[i, j, :] - pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1 - if pX > 0 and pX < 63 and pY > 0 and pY < 63: - diff = torch.FloatTensor( - [hm_[pY, pX + 1] - hm_[pY, pX - 1], - hm_[pY + 1, pX] - hm_[pY - 1, pX]]) - preds[i, j].add_(diff.sign_().mul_(.25)) - - preds.add_(-.5) - - preds_orig = torch.zeros(preds.size()) - if centers is not None and scales is not None: - for i in range(hm.size(0)): - for j in range(hm.size(1)): - preds_orig[i, j] = transform( - preds[i, j], centers[i], scales[i], hm.size(2), True) - - return preds, preds_orig - -def shuffle_lr(parts, pairs=None): - """Shuffle the points left-right according to the axis of symmetry - of the object. - - Arguments: - parts {torch.tensor} -- a 3D or 4D object containing the - heatmaps. - - Keyword Arguments: - pairs {list of integers} -- [order of the flipped points] (default: {None}) - """ - if pairs is None: - pairs = [16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, - 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 27, 28, 29, 30, 35, - 34, 33, 32, 31, 45, 44, 43, 42, 47, 46, 39, 38, 37, 36, 41, - 40, 54, 53, 52, 51, 50, 49, 48, 59, 58, 57, 56, 55, 64, 63, - 62, 61, 60, 67, 66, 65] - if parts.ndimension() == 3: - parts = parts[pairs, ...] - else: - parts = parts[:, pairs, ...] - - return parts - - -def flip(tensor, is_label=False): - """Flip an image or a set of heatmaps left-right - - Arguments: - tensor {numpy.array or torch.tensor} -- [the input image or heatmaps] - - Keyword Arguments: - is_label {bool} -- [denote wherever the input is an image or a set of heatmaps ] (default: {False}) - """ - if not torch.is_tensor(tensor): - tensor = torch.from_numpy(tensor) - - if is_label: - tensor = shuffle_lr(tensor).flip(tensor.ndimension() - 1) - else: - tensor = tensor.flip(tensor.ndimension() - 1) - - return tensor - -# From pyzolib/paths.py (https://bitbucket.org/pyzo/pyzolib/src/tip/paths.py) - - -def appdata_dir(appname=None, roaming=False): - """ appdata_dir(appname=None, roaming=False) - - Get the path to the application directory, where applications are allowed - to write user specific files (e.g. configurations). For non-user specific - data, consider using common_appdata_dir(). - If appname is given, a subdir is appended (and created if necessary). - If roaming is True, will prefer a roaming directory (Windows Vista/7). - """ - - # Define default user directory - userDir = os.getenv('FACEALIGNMENT_USERDIR', None) - if userDir is None: - userDir = os.path.expanduser('~') - if not os.path.isdir(userDir): # pragma: no cover - userDir = '/var/tmp' # issue #54 - - # Get system app data dir - path = None - if sys.platform.startswith('win'): - path1, path2 = os.getenv('LOCALAPPDATA'), os.getenv('APPDATA') - path = (path2 or path1) if roaming else (path1 or path2) - elif sys.platform.startswith('darwin'): - path = os.path.join(userDir, 'Library', 'Application Support') - # On Linux and as fallback - if not (path and os.path.isdir(path)): - path = userDir - - # Maybe we should store things local to the executable (in case of a - # portable distro or a frozen application that wants to be portable) - prefix = sys.prefix - if getattr(sys, 'frozen', None): - prefix = os.path.abspath(os.path.dirname(sys.executable)) - for reldir in ('settings', '../settings'): - localpath = os.path.abspath(os.path.join(prefix, reldir)) - if os.path.isdir(localpath): # pragma: no cover - try: - open(os.path.join(localpath, 'test.write'), 'wb').close() - os.remove(os.path.join(localpath, 'test.write')) - except IOError: - pass # We cannot write in this directory - else: - path = localpath - break - - # Get path specific for this app - if appname: - if path == userDir: - appname = '.' + appname.lstrip('.') # Make it a hidden directory - path = os.path.join(path, appname) - if not os.path.isdir(path): # pragma: no cover - os.mkdir(path) - - # Done - return path diff --git a/talkingface/utils/logger.py b/talkingface/utils/logger.py deleted file mode 100644 index 855dbb94..00000000 --- a/talkingface/utils/logger.py +++ /dev/null @@ -1,95 +0,0 @@ -import logging -import os -import colorlog -import re -import hashlib -from talkingface.utils.utils import get_local_time, ensure_dir -from colorama import init - -log_colors_config = { - "DEBUG": "cyan", - "WARNING": "yellow", - "ERROR": "red", - "CRITICAL": "red", -} - -class RemoveColorFilter(logging.Filter): - def filter(self, record): - if record: - ansi_escape = re.compile(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])") - record.msg = ansi_escape.sub("", str(record.msg)) - return True - -def set_color(log, color, highlight=True): - color_set = ["black", "red", "green", "yellow", "blue", "pink", "cyan", "white"] - try: - index = color_set.index(color) - except: - index = len(color_set) - 1 - prev_log = "\033[" - if highlight: - prev_log += "1;3" - else: - prev_log += "0;3" - prev_log += str(index) + "m" - return prev_log + log + "\033[0m" - -def init_logger(config): - """ - A logger that can show a message on standard output and write it into the - file named `filename` simultaneously. - All the message that you want to log MUST be str. - - Args: - config (Config): An instance object of Config, used to record parameter information. - - Example: - >>> logger = logging.getLogger(config) - >>> logger.debug(train_state) - >>> logger.info(train_result) - """ - init(autoreset=True) - LOGROOT = "./log/" - dir_name = os.path.dirname(LOGROOT) - ensure_dir(dir_name) - model_name = os.path.join(dir_name, config["model"]) - ensure_dir(model_name) - config_str = "".join([str(key) for key in config.final_config_dict.values()]) - md5 = hashlib.md5(config_str.encode(encoding="utf-8")).hexdigest()[:6] - logfilename = "{}/{}-{}-{}-{}.log".format( - config["model"], config["model"], config["dataset"], get_local_time(), md5 - ) - - logfilepath = os.path.join(LOGROOT, logfilename) - - filefmt = "%(asctime)-15s %(levelname)s %(message)s" - filedatefmt = "%a %d %b %Y %H:%M:%S" - fileformatter = logging.Formatter(filefmt, filedatefmt) - - sfmt = "%(log_color)s%(asctime)-15s %(levelname)s %(message)s" - sdatefmt = "%d %b %H:%M" - sformatter = colorlog.ColoredFormatter(sfmt, sdatefmt, log_colors=log_colors_config) - if config["state"] is None or config["state"].lower() == "info": - level = logging.INFO - elif config["state"].lower() == "debug": - level = logging.DEBUG - elif config["state"].lower() == "error": - level = logging.ERROR - elif config["state"].lower() == "warning": - level = logging.WARNING - elif config["state"].lower() == "critical": - level = logging.CRITICAL - else: - level = logging.INFO - - fh = logging.FileHandler(logfilepath) - fh.setLevel(level) - fh.setFormatter(fileformatter) - remove_color_filter = RemoveColorFilter() - fh.addFilter(remove_color_filter) - - sh = logging.StreamHandler() - sh.setLevel(level) - sh.setFormatter(sformatter) - - logging.basicConfig(level=level, handlers=[sh, fh]) \ No newline at end of file diff --git a/talkingface/utils/utils.py b/talkingface/utils/utils.py index a5019491..bc910e03 100644 --- a/talkingface/utils/utils.py +++ b/talkingface/utils/utils.py @@ -1,455 +1,35 @@ -import datetime -import importlib -import os -import random -import pandas as pd - -import numpy as np import torch +import numpy as np +from tensorboardX import SummaryWriter +import editdistance import torch.nn as nn +import torch.nn.init as init -from torch.utils.tensorboard import SummaryWriter -from texttable import Texttable - -def get_local_time(): - r"""Get current time - - Returns: - str: current time - """ - cur = datetime.datetime.now() - cur = cur.strftime("%b-%d-%Y_%H-%M-%S") - - return cur - - -def ensure_dir(dir_path): - r"""Make sure the directory exists, if it does not exist, create it - - Args: - dir_path (str): directory path - - """ - if not os.path.exists(dir_path): - os.makedirs(dir_path) - -def get_model(model_name): - r"""Automatically select model class based on model name - - Args: - model_name (str): model name - - Returns: - Recommender: model class - """ - model_submodule = [ - "audio_driven_talkingface", - "image_driven_talkingface", - "nerf_based_talkingface", - "text_to_speech", - "voice_conversion" - - ] - - model_file_name = model_name.lower() - model_module = None - for submodule in model_submodule: - module_path = ".".join(["talkingface.model", submodule, model_file_name]) - if importlib.util.find_spec(module_path, __name__): - model_module = importlib.import_module(module_path, __name__) - break - - if model_module is None: - raise ValueError( - "`model_name` [{}] is not the name of an existing model.".format(model_name) - ) - model_class = getattr(model_module, model_name) - return model_class - -def get_trainer(model_name): - r"""Automatically select trainer class based on model name - - Args: - model_name (str): model name - - Returns: - Trainer: trainer class - """ - try: - return getattr( - importlib.import_module("talkingface.trainer"), model_name + "Trainer" - ) - except AttributeError: - raise AttributeError( - "There is no trainer named `{}`".format(model_name) - ) - -def early_stopping(value, best, cur_step, max_step, bigger=False): - r"""validation-based early stopping - - Args: - value (float): current result - best (float): best result - cur_step (int): the number of consecutive steps that did not exceed the best result - max_step (int): threshold steps for stopping - bigger (bool, optional): whether the bigger the better - - Returns: - tuple: - - float, - best result after this step - - int, - the number of consecutive steps that did not exceed the best result after this step - - bool, - whether to stop - - bool, - whether to update - """ - stop_flag = False - update_flag = False - if bigger: - if value >= best: - cur_step = 0 - best = value - update_flag = True - else: - cur_step += 1 - if cur_step > max_step: - stop_flag = True - else: - if value <= best: - cur_step = 0 - best = value - update_flag = True - else: - cur_step += 1 - if cur_step > max_step: - stop_flag = True - return best, cur_step, stop_flag, update_flag - - -def calculate_valid_score(valid_result, valid_netric=None): - r"""Calculate the valid score according to the valid result and valid metric - - Args: - valid_result (dict): valid result - valid_metric (Metric or None, optional): valid metric - - Returns: - float: valid score - """ - if valid_netric is None: - return valid_result - else: - return valid_result[valid_netric] - -def dict2str(result_dict): - r"""convert result dict to str - - Args: - result_dict (dict): result dict - - Returns: - str: result str - """ - - return " ".join( - [str(metric) + " : " + str(value) for metric, value in result_dict.items()] - ) - - -def init_seed(seed, reproducibility): - r"""init random seed for random functions in numpy, torch, cuda and cudnn - - Args: - seed (int): random seed - reproducibility (bool): Whether to require reproducibility - """ - random.seed(seed) - np.random.seed(seed) - torch.manual_seed(seed) - torch.cuda.manual_seed(seed) - torch.cuda.manual_seed_all(seed) - if reproducibility: - torch.backends.cudnn.benchmark = False - torch.backends.cudnn.deterministic = True - else: - torch.backends.cudnn.benchmark = True - torch.backends.cudnn.deterministic = False - -def get_tensorboard(logger): - r"""Creates a SummaryWriter of Tensorboard that can log PyTorch models and metrics into a directory for - visualization within the TensorBoard UI. - For the convenience of the user, the naming rule of the SummaryWriter's log_dir is the same as the logger. - - Args: - logger: its output filename is used to name the SummaryWriter's log_dir. - If the filename is not available, we will name the log_dir according to the current time. - - Returns: - SummaryWriter: it will write out events and summaries to the event file. - """ - base_path = "log_tensorboard" - - dir_name = None - for handler in logger.handlers: - if hasattr(handler, "baseFilename"): - dir_name = os.path.basename(getattr(handler, "baseFilename")).split(".")[0] - break - if dir_name is None: - dir_name = "{}-{}".format("model", get_local_time()) - - dir_path = os.path.join(base_path, dir_name) - writer = SummaryWriter(dir_path) - return writer - -def get_gpu_usage(device=None): - r"""Return the reserved memory and total memory of given device in a string. - Args: - device: cuda.device. It is the device that the model run on. - - Returns: - str: it contains the info about reserved memory and total memory of given device. - """ - - reserved = torch.cuda.max_memory_reserved(device) / 1024**3 - total = torch.cuda.get_device_properties(device).total_memory / 1024**3 - - return "{:.2f} G/{:.2f} G".format(reserved, total) - -def get_flops(model, dataset, device, logger, transform, verbose=False): - r"""Given a model and dataset to the model, compute the per-operator flops - of the given model. - Args: - model: the model to compute flop counts. - dataset: dataset that are passed to `model` to count flops. - device: cuda.device. It is the device that the model run on. - verbose: whether to print information of modules. - - Returns: - total_ops: the number of flops for each operation. - """ - # if model.type == ModelType.DECISIONTREE: - # return 1 - # if model.__class__.__name__ == "Pop": - # return 1 - - import copy - - model = copy.deepcopy(model) - - def count_normalization(m, x, y): - x = x[0] - flops = torch.DoubleTensor([2 * x.numel()]) - m.total_ops += flops - - def count_embedding(m, x, y): - x = x[0] - nelements = x.numel() - hiddensize = y.shape[-1] - m.total_ops += nelements * hiddensize - - class TracingAdapter(torch.nn.Module): - def __init__(self, rec_model): - super().__init__() - self.model = rec_model - - def forward(self, interaction): - return self.model.predict(interaction) - - custom_ops = { - torch.nn.Embedding: count_embedding, - torch.nn.LayerNorm: count_normalization, - } - wrapper = TracingAdapter(model) - inter = dataset[torch.tensor([1])].to(device) - inter = transform(dataset, inter) - inputs = (inter,) - from thop.profile import register_hooks - from thop.vision.basic_hooks import count_parameters - - handler_collection = {} - fn_handles = [] - params_handles = [] - types_collection = set() - if custom_ops is None: - custom_ops = {} - - def add_hooks(m: nn.Module): - m.register_buffer("total_ops", torch.zeros(1, dtype=torch.float64)) - m.register_buffer("total_params", torch.zeros(1, dtype=torch.float64)) - - m_type = type(m) - - fn = None - if m_type in custom_ops: - fn = custom_ops[m_type] - if m_type not in types_collection and verbose: - logger.info("Customize rule %s() %s." % (fn.__qualname__, m_type)) - elif m_type in register_hooks: - fn = register_hooks[m_type] - if m_type not in types_collection and verbose: - logger.info("Register %s() for %s." % (fn.__qualname__, m_type)) - else: - if m_type not in types_collection and verbose: - logger.warning( - "[WARN] Cannot find rule for %s. Treat it as zero Macs and zero Params." - % m_type - ) - - if fn is not None: - handle_fn = m.register_forward_hook(fn) - handle_paras = m.register_forward_hook(count_parameters) - handler_collection[m] = ( - handle_fn, - handle_paras, - ) - fn_handles.append(handle_fn) - params_handles.append(handle_paras) - types_collection.add(m_type) - - prev_training_status = wrapper.training - - wrapper.eval() - wrapper.apply(add_hooks) - - with torch.no_grad(): - wrapper(*inputs) - - def dfs_count(module: nn.Module, prefix="\t"): - total_ops, total_params = module.total_ops.item(), 0 - ret_dict = {} - for n, m in module.named_children(): - next_dict = {} - if m in handler_collection and not isinstance( - m, (nn.Sequential, nn.ModuleList) - ): - m_ops, m_params = m.total_ops.item(), m.total_params.item() - else: - m_ops, m_params, next_dict = dfs_count(m, prefix=prefix + "\t") - ret_dict[n] = (m_ops, m_params, next_dict) - total_ops += m_ops - total_params += m_params - - return total_ops, total_params, ret_dict - - total_ops, total_params, ret_dict = dfs_count(wrapper) - - # reset wrapper to original status - wrapper.train(prev_training_status) - for m, (op_handler, params_handler) in handler_collection.items(): - m._buffers.pop("total_ops") - m._buffers.pop("total_params") - for i in range(len(fn_handles)): - fn_handles[i].remove() - params_handles[i].remove() - - return total_ops - -def list_to_latex(convert_list, bigger_flag=True, subset_columns=[]): - result = {} - for d in convert_list: - for key, value in d.items(): - if key in result: - result[key].append(value) - else: - result[key] = [value] - - df = pd.DataFrame.from_dict(result, orient="index").T - - if len(subset_columns) == 0: - tex = df.to_latex(index=False) - return df, tex - - def bold_func(x, bigger_flag): - if bigger_flag: - return np.where(x == np.max(x.to_numpy()), "font-weight:bold", None) - else: - return np.where(x == np.min(x.to_numpy()), "font-weight:bold", None) - - style = df.style - style.apply(bold_func, bigger_flag=bigger_flag, subset=subset_columns) - style.format(precision=4) - - num_column = len(df.columns) - column_format = "c" * num_column - tex = style.hide(axis="index").to_latex( - caption="Result Table", - label="Result Table", - convert_css=True, - hrules=True, - column_format=column_format, - ) - - return df, tex - -def get_environment(config): - gpu_usage = ( - get_gpu_usage(config["device"]) - if torch.cuda.is_available() and config["use_gpu"] - else "0.0 / 0.0" - ) - - import psutil - - memory_used = psutil.Process(os.getpid()).memory_info().rss / 1024**3 - memory_total = psutil.virtual_memory()[0] / 1024**3 - memory_usage = "{:.2f} G/{:.2f} G".format(memory_used, memory_total) - cpu_usage = "{:.2f} %".format(psutil.cpu_percent(interval=1)) - """environment_data = [ - {"Environment": "CPU", "Usage": cpu_usage,}, - {"Environment": "GPU", "Usage": gpu_usage, }, - {"Environment": "Memory", "Usage": memory_usage, }, - ]""" - - table = Texttable() - table.set_cols_align(["l", "c"]) - table.set_cols_valign(["m", "m"]) - table.add_rows( - [ - ["Environment", "Usage"], - ["CPU", cpu_usage], - ["GPU", gpu_usage], - ["Memory", memory_usage], - ] - ) - - return table - -def get_preprocess(dataset_name): - r"""Automatically select dataset preprocess class based on dataset name - """ - model_file_name = dataset_name.lower() - module_path = "talkingface.utils.data_process" - model_module = importlib.import_module(module_path, __name__) - try: - preprocess_class = getattr(model_module, dataset_name+'Preprocess') - except: - raise ValueError( - "`dataset_name` [{}] is not the name of an existing dataset.".format(dataset_name) - ) - return preprocess_class - -def create_dataset(config): - r"""Automatically select dataset class based on dataset name - """ - model_name = config['model'] - dataset_file_name = model_name.lower()+'_dataset' - module_path = ".".join(["talkingface.data.dataset", dataset_file_name]) - if importlib.util.find_spec(module_path, __name__): - dataset_module = importlib.import_module(module_path, __name__) - if dataset_module is None: - raise ValueError( - "`dataset_file_name` [{}] is not the name of an existing dataset.".format(dataset_file_name) - ) - dataset_class = getattr(dataset_module, model_name+'Dataset') - - return dataset_class(config, config['train_filelist']), dataset_class(config, config['val_filelist']) - - +def cc(net): + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + return net.to(device) +class Logger(object): + def __init__(self, logdir='./log'): + self.writer = SummaryWriter(logdir) + def scalar_summary(self, tag, value, step): + self.writer.add_scalar(tag, value, step) + def scalars_summary(self, tag, dictionary, step): + self.writer.add_scalars(tag, dictionary, step) + def text_summary(self, tag, value, step): + self.writer.add_text(tag, value, step) + def audio_summary(self, tag, value, step, sr): + writer.add_audio(tag, value, step, sample_rate=sr) +def infinite_iter(iterable): + it = iter(iterable) + while True: + try: + ret = next(it) + yield ret + except StopIteration: + it = iter(iterable) diff --git a/talkingface/utils/wandblogger.py b/talkingface/utils/wandblogger.py deleted file mode 100644 index 8d958474..00000000 --- a/talkingface/utils/wandblogger.py +++ /dev/null @@ -1,57 +0,0 @@ -class WandbLogger(object): - """WandbLogger to log metrics to Weights and Biases.""" - - def __init__(self, config): - """ - Args: - config (dict): A dictionary of parameters used by RecBole. - """ - self.config = config - self.log_wandb = config.log_wandb - self.setup() - - def setup(self): - if self.log_wandb: - try: - import wandb - - self._wandb = wandb - except ImportError: - raise ImportError( - "To use the Weights and Biases Logger please install wandb." - "Run `pip install wandb` to install it." - ) - - # Initialize a W&B run - if self._wandb.run is None: - self._wandb.init(project=self.config.wandb_project, 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vctk_model.args.yaml # 训练时的命令参数配置文件 + ├── vctk_model.ckpt # 训练后模型的checkpoint + ├── vctk_model.config.yaml # 模型的参数配置文件 + └── vctk_model.opt ``` -data/ -├── dataprocess +### tf-logs + +训练的日志文件，用于tensorboard绘制评估曲线。 -| ├── wav2lip_process.py +### talkingface模块 -| ├── xxxx_process.py +模型的核心代码部分 -├── dataset +#### config -| ├── wav2lip_dataset.py +根据模型和数据集名称自动生成所有模型、数据集、训练、评估等相关的配置信息。 + +#### data + +```python +data +├── dataprocess # 模型特有的数据处理代码 +│ ├── __init__.py +│ ├── make_datasets_vctk.py +│ ├── preprocess_vctk.sh +│ ├── reduce_dataset.py +│ ├── sample_segments.py +│ ├── sample_single_segments.py +│ ├── tacotron +│ │ ├── hyperparams.py +│ │ └── utils.py +│ └── vctk.config +└── dataset + ├── __init__.py + ├── dataset.py + └── vctk_dataset.py -| ├── xxx_dataset.py ``` -#### evaluate -主要涉及模型评估的代码 -LSE metric 需要的数据是生成的视频列表 -SSIM metric 需要的数据是生成的视频和真实的视频列表 +- dataprocess：模型特有的数据处理代码，可以通过运行preprocess_vctk.sh脚本直接进行数据的处理。 + +- dataset： + - vctk_dataset.py: 创建SequenceDataset，PickleDataset， get_data_loader等子类，继承自基类Dataset，实现基类中的方法。 #### model -实现的模型的网络和对应的方法 （核心部分） -主要分三类： -- audio-driven (音频驱动) -- image-driven （图像驱动） -- nerf-based （基于神经辐射场的方法） -``` -model/ -├── audio_driven_talkingface +#### properties -| ├── wav2lip.py -├── image_driven_talkingface -| ├── xxxx.py +#### utils -├── nerf_based_talkingface -| ├── xxxx.py -├── abstract_talkingface.py +#### trainer -``` -#### properties -保存默认配置文件，包括： -- 数据集配置文件 -- 模型配置文件 -- 通用配置文件 -需要根据对应模型和数据集增加对应的配置文件，通用配置文件`overall.yaml`一般不做修改 -``` -properties/ +#### evalutor -├── dataset -| ├── xxx.yaml -├── model +## 实验步骤 -| ├── xxx.yaml +### 1. 环境搭建 -├── overall.yaml +- 硬件环境 + - CPU ：12 核心 内存：43 GB + - GPU ：NVIDIA GeForce RTX 2080 Ti -``` +实验在Autodl平台上进行模型的训练和评估，选择使用平台提供的miniconda3镜像，按照以下步骤搭建环境。 -#### quick_start -通用的启动文件，根据传入参数自动配置数据集和模型，然后训练和评估（一般不需要修改） -``` -quick_start/ +- **创建conda虚拟环境py36并激活环境** -├── quick_start.py + ```shell + conda create -n py36 python=3.6 + conda init bash && source /root/.bashrc + conda activate py36 + ``` -``` +- **查看显卡支持的最大cuda版本，选择合适的pytorch版本安装** -#### trainer -训练、评估函数的主类。在trainer中，如果可以使用基类`Trainer`实现所有功能，则不需要写一个新的。如果模型训练有一些特有部分，则需要重载`Trainer`。需要重载部分可能主要集中于: `_train_epoch()`, `_valid_epoch()`。 重载的`Trainer`应该命名为：`{model_name}Trainer` -``` -trainer/ + ```assembly + nvidia-smi + ``` -├── trainer.py + ![image-20240119193402509](README.assets/image-20240119193402509.png) -``` +- **安装实验所用的第三方库** -#### utils -公用的工具类，包括`s3fd`人脸检测，视频抽帧、视频抽音频方法。还包括根据参数配置找对应的模型类、数据类等方法。 -一般不需要修改，但可以适当添加一些必须的且相对普遍的数据处理文件。 + ​ 实验根据文档提出的环境要求进行了尝试，使用`python=3.8`，`torch==1.13.1+cu116`，`numpy==1.20.3`，`librosa==0.10.1`进行环境配置，但是在项目的复现中遇到了非常多的版本冲突。项目源码实现的环境使用了低于0.8.0版本的librosa，低于0.50.0版本的numba，使用文档要求的环境会引发一系列与numba\llvmlite\python包版本不兼容的问题，所以实验使用了较早版本的第三方库环境，具体的第三方库配置已导出到requirement.txt中。 + + - torch=1.0.1 torchvision=0.2.2 + - numba=0.48.0 + - numpy=1.19.5 + - pip=21.3.1 + - SoundFile=0.10.2 + - librosa=0.7.2 + - llvmlite=0.31.0 -## 使用方法 -### 环境要求 -- `python=3.8` -- `torch==1.13.1+cu116`（gpu版，若设备不支持cuda可以使用cpu版） -- `numpy==1.20.3` -- `librosa==0.10.1` +### 2.数据预处理 -尽量保证上面几个包的版本一致 +对数据集进行预处理，将数据集划分为train_set、 in_test_set和out_test_set。 -提供了两种配置其他环境的方法： +```bash +bash preprocess_vctk.sh ``` -pip install -r requirements.txt -or +![image-20240118205212486](README.assets/image-20240118205212486.png) + +经过预处理后的数据架构如下： + +![image-20240119123956865](README.assets/image-20240119123956865.png) + +### 3.模型训练 -conda env create -f environment.yml +使用run_talkingface.py进行模型的训练 + +``` +python run_talkingface.py --model=Adaptive_VC --dataset=VCTK ``` -建议使用conda虚拟环境！！！ +训练参数设置如下： -### 训练和评估 +- **--model**, default="Adaptive_VC" +- **--dataset**, default="VCTK" + +- **--config**, default='./talkingface/properties/model/adaptive-VC.yaml' + +- **--data_dir**, default='./autodl-fs/preprocessed_data' + +- **--train_set**, default='train_128' + +- **--train_index_file**, default='train_samples_128.json' + +- **--logdir**, default='./tf-logs/' + +- **--store_model_path**, default='./saved/adaptive_vc/vctk_model' + +- **--load_model_path**, default='./saved/adaptive_vc/vctk_model' + +- **--summary_steps**, default=500 + +- **--save_steps**, default=5000 + +- **--iters**, default=20000 + +实验的训练结果输出在`result\train\train_output.log`，训练得到的模型在`saved\adaptive_vc\vctk_model.ckpt`。 + +模型训练过程的截图如下: + +![image-20240119181755678](README.assets/image-20240119181755678.png) + +经过20000个iter的训练之后，得到的模型效果如下： + +![image-20240121232546501](README.assets/image-20240121232546501.png) + +![image-20240121231348020](README.assets/image-20240121231348020.png) + +### 4.模型验证 + +使用run_evaluator.py进行模型的验证 -```bash -python run_talkingface.py --model=xxxx --dataset=xxxx (--other_parameters=xxxxxx) ``` +python run_evaluator.py +``` + +模型验证的参设置如下： -### 权重文件 - -- LSE评估需要的权重: syncnet_v2.model [百度网盘下载](https://pan.baidu.com/s/1vQoL9FuKlPyrHOGKihtfVA?pwd=32hc) -- wav2lip需要的lip expert 权重：lipsync_expert.pth [百度网下载](https://pan.baidu.com/s/1vQoL9FuKlPyrHOGKihtfVA?pwd=32hc) - -## 可选论文： -### Aduio_driven talkingface -| 模型简称 | 论文 | 代码仓库 | -|:--------:|:--------:|:--------:| -| MakeItTalk | [paper](https://arxiv.org/abs/2004.12992) | [code](https://github.com/yzhou359/MakeItTalk) | -| MEAD | [paper](https://wywu.github.io/projects/MEAD/support/MEAD.pdf) | [code](https://github.com/uniBruce/Mead) | -| RhythmicHead | [paper](https://arxiv.org/pdf/2007.08547v1.pdf) | [code](https://github.com/lelechen63/Talking-head-Generation-with-Rhythmic-Head-Motion) | -| PC-AVS | [paper](https://arxiv.org/abs/2104.11116) | [code](https://github.com/Hangz-nju-cuhk/Talking-Face_PC-AVS) | -| EVP | [paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Ji_Audio-Driven_Emotional_Video_Portraits_CVPR_2021_paper.pdf) | [code](https://github.com/jixinya/EVP) | -| LSP | [paper](https://arxiv.org/abs/2109.10595) | [code](https://github.com/YuanxunLu/LiveSpeechPortraits) | -| EAMM | [paper](https://arxiv.org/pdf/2205.15278.pdf) | [code](https://github.com/jixinya/EAMM/) | -| DiffTalk | [paper](https://arxiv.org/abs/2301.03786) | [code](https://github.com/sstzal/DiffTalk) | -| TalkLip | [paper](https://arxiv.org/pdf/2303.17480.pdf) | [code](https://github.com/Sxjdwang/TalkLip) | -| EmoGen | [paper](https://arxiv.org/pdf/2303.11548.pdf) | [code](https://github.com/sahilg06/EmoGen) | -| SadTalker | [paper](https://arxiv.org/abs/2211.12194) | [code](https://github.com/OpenTalker/SadTalker) | -| HyperLips | [paper](https://arxiv.org/abs/2310.05720) | [code](https://github.com/semchan/HyperLips) | -| PHADTF | [paper](http://arxiv.org/abs/2002.10137) | [code](https://github.com/yiranran/Audio-driven-TalkingFace-HeadPose) | -| VideoReTalking | [paper](https://arxiv.org/abs/2211.14758) | [code](https://github.com/OpenTalker/video-retalking#videoretalking--audio-based-lip-synchronization-for-talking-head-video-editing-in-the-wild-) -| | - - - -### Image_driven talkingface -| 模型简称 | 论文 | 代码仓库 | -|:--------:|:--------:|:--------:| -| PIRenderer | [paper](https://arxiv.org/pdf/2109.08379.pdf) | [code](https://github.com/RenYurui/PIRender) | -| StyleHEAT | [paper](https://arxiv.org/pdf/2203.04036.pdf) | [code](https://github.com/OpenTalker/StyleHEAT) | -| MetaPortrait | [paper](https://arxiv.org/abs/2212.08062) | [code](https://github.com/Meta-Portrait/MetaPortrait) | -| | -### Nerf-based talkingface -| 模型简称 | 论文 | 代码仓库 | -|:--------:|:--------:|:--------:| -| AD-NeRF | [paper](https://arxiv.org/abs/2103.11078) | [code](https://github.com/YudongGuo/AD-NeRF) | -| GeneFace | [paper](https://arxiv.org/abs/2301.13430) | [code](https://github.com/yerfor/GeneFace) | -| DFRF | [paper](https://arxiv.org/abs/2207.11770) | [code](https://github.com/sstzal/DFRF) | -| | -### text_to_speech -| 模型简称 | 论文 | 代码仓库 | -|:--------:|:--------:|:--------:| -| VITS | [paper](https://arxiv.org/abs/2106.06103) | [code](https://github.com/jaywalnut310/vits) | -| Glow TTS | [paper](https://arxiv.org/abs/2005.11129) | [code](https://github.com/jaywalnut310/glow-tts) | -| FastSpeech2 | [paper](https://arxiv.org/abs/2006.04558v1) | [code](https://github.com/ming024/FastSpeech2) | -| StyleTTS2 | [paper](https://arxiv.org/abs/2306.07691) | [code](https://github.com/yl4579/StyleTTS2) | -| Grad-TTS | [paper](https://arxiv.org/abs/2105.06337) | [code](https://github.com/huawei-noah/Speech-Backbones/tree/main/Grad-TTS) | -| FastSpeech | [paper](https://arxiv.org/abs/1905.09263) | [code](https://github.com/xcmyz/FastSpeech) | -| | -### voice_conversion -| 模型简称 | 论文 | 代码仓库 | -|:--------:|:--------:|:--------:| -| StarGAN-VC | [paper](http://www.kecl.ntt.co.jp/people/kameoka.hirokazu/Demos/stargan-vc2/index.html) | [code](https://github.com/kamepong/StarGAN-VC) | -| Emo-StarGAN | [paper](https://www.researchgate.net/publication/373161292_Emo-StarGAN_A_Semi-Supervised_Any-to-Many_Non-Parallel_Emotion-Preserving_Voice_Conversion) | [code](https://github.com/suhitaghosh10/emo-stargan) | -| adaptive-VC | [paper](https://arxiv.org/abs/1904.05742) | [code](https://github.com/jjery2243542/adaptive_voice_conversion) | -| DiffVC | [paper](https://arxiv.org/abs/2109.13821) | [code](https://github.com/huawei-noah/Speech-Backbones/tree/main/DiffVC) | -| Assem-VC | [paper](https://arxiv.org/abs/2104.00931) | [code](https://github.com/maum-ai/assem-vc) | -| | +- **-c**, the path of config file. +- **-m**:,the path of model checkpoint. +- **-a**, the attribute file for normalization ad denormalization. +- **-s**, the path of source file (.wav). +- **-t**, the path of target file (.wav). +- **-o**, the path of output converted file (.wav). + +验证使用的模型为`saved/adaptive_vc/vctk_model.ckpt`，模型验证结果的输出在`result\evaluate\evaluate_output.log`，生成的语音在`result\evaluate\eval.wav`。 ## 作业要求 + - 确保可以仅在命令行输入模型和数据集名称就可以训练、验证。（部分仓库没有提供训练代码的，可以不训练） -- 每个组都要提交一个README文件，写明完成的功能、最终实现的训练、验证截图、所使用的依赖、成员分工等。 +- 每个组都要提交一个README文件，写明==完成的功能==、最终**实现的训练、验证截图**、**所使用的依赖**、**成员分工**等。 diff --git a/checkpoints/README.md b/checkpoints/README.md index 8b137891..0a1432d6 100644 --- a/checkpoints/README.md +++ b/checkpoints/README.md @@ -1 +1,16 @@ +这个文件夹中保存的是，模型训练或验证过程中用到的一些额外的预训练权重如： +- wav2lip中用到的syncnet权重 +- 计算合成视频lip-audio同步LSE用到的syncnet-v2权重 +- ....... +目录结构为： +``` +checkpoints/ + +├── LSE +| ├── syncnet_v2.model () + +├── Wav2Lip +| ├── lipsync_expert.pth () + +``` diff --git a/checkpoints/adaptive-vc/README.md b/checkpoints/adaptive-vc/README.md new file mode 100644 index 00000000..286aaa43 --- /dev/null +++ b/checkpoints/adaptive-vc/README.md @@ -0,0 +1 @@ +You can download the pretrain model from [here](http://speech.ee.ntu.edu.tw/~jjery2243542/resource/model/is19/vctk_model.ckpt) and the coresponding normalization parameters for inference from [here](http://speech.ee.ntu.edu.tw/~jjery2243542/resource/model/is19/attr.pkl). \ No newline at end of file diff --git a/dataset/README.md b/dataset/README.md index 8a2201ad..98cd0fca 100644 --- a/dataset/README.md +++ b/dataset/README.md @@ -1,41 +1,28 @@ -这个文件夹中保存的是数据集如： -- lrw -- lrs2 -- mead -- ....... - -数据集处理的一般格式为： - -``` -dataset/ - -├── lrs2 - -| ├── data (存放数据集的原始数据) - -| ├── filelist (保存的是数据集划分) - -| │ ├── train.txt - -| │ ├── val.txt - -| │ ├── test.txt - -| ├── preprocessed_data (具体路径内容可以参考talkingface.utils.data_preprocess文件中处理lrs2时候的路径，主要存储的是视频抽帧后的图像文件和音频文件) -``` - -preprocessed_data的数据路径一般表示为： -``` -preprocessed_root (lrs2_preprocessed)/ - -├── list of folders - -| ├── Folders with five-digit numbered video IDs - -| │ ├── *.jpg - -| │ ├── audio.wav - -``` - -数据集存储尽量按照这个格式来，数据集的划分也尽量按照train.txt val.txt和test.txt文件来 +实验使用的数据集是CSTR VCTK Corpus数据集，可以在下面的地址中下载获得。 +- [CSTR VCTK Corpus](https://homepages.inf.ed.ac.uk/jyamagis/page3/page58/page58.html) + +在dataset文件夹中，VCTK-Corpus存放的是在网站下载的原始数据，preprocessed_data存放的是经过预处理之后的数据。 + +经过预处理后的dataset的架构如下： +```xml +dataset +├── preprocessed_data * +│ ├── attr.pkl +│ ├── in_test_files.txt +│ ├── in_test.pkl +│ ├── in_test_samples_128.json +│ ├── out_test_files.txt +│ ├── out_test.pkl +│ ├── out_test_samples_128.json +│ ├── train_128.pkl +│ ├── train.pkl +│ └── train_samples_128.json +├── VCTK-Corpus * +│ ├── COPYING +│ ├── NOTE +│ ├── README +│ ├── speaker-info.txt +│ ├── txt * +│ └── wav48 * +└── (*符号代表该路径名为文件夹) +``` \ No newline at end of file diff --git a/result/evaluate/evaluate_output.log b/result/evaluate/evaluate_output.log index f0cf869c..f1f6b7a2 100644 --- a/result/evaluate/evaluate_output.log +++ b/result/evaluate/evaluate_output.log @@ -123,4 +123,4 @@ AE( ) ) Load model from ./saved/adaptive_vc/vctk_model.ckpt -wav result in result/evaluate/eval1.wav \ No newline at end of file +wav result in result/evaluate/eval.wav \ No newline at end of file diff --git a/result/train/init_ae_train_grad_norm.csv b/result/train/init_ae_train_grad_norm.csv deleted file mode 100644 index b966a615..00000000 --- a/result/train/init_ae_train_grad_norm.csv +++ /dev/null @@ -1,26 +0,0 @@ - - - - - - - AutoPanel - - - - - - - -

- - - diff --git a/result/train/init_ae_train_loss_kl.csv b/result/train/init_ae_train_loss_kl.csv deleted file mode 100644 index b966a615..00000000 --- a/result/train/init_ae_train_loss_kl.csv +++ /dev/null @@ -1,26 +0,0 @@ - - - - - - - AutoPanel - - - - - - - -
- - - diff --git a/result/train/init_ae_train_loss_rec.csv b/result/train/init_ae_train_loss_rec.csv deleted file mode 100644 index b966a615..00000000 --- a/result/train/init_ae_train_loss_rec.csv +++ /dev/null @@ -1,26 +0,0 @@ - - - - - - - AutoPanel - - - - - - - -
- - - diff --git a/run_evaluator.py b/run_evaluator.py index f8aa9a5e..ec0042c8 100644 --- a/run_evaluator.py +++ b/run_evaluator.py @@ -7,11 +7,11 @@ if __name__ == '__main__': parser = ArgumentParser() - parser.add_argument('-attr', '-a', default="./autodl-fs/preprocessed_data/attr.pkl") + parser.add_argument('-attr', '-a', default="./dataset/preprocessed_data/attr.pkl") parser.add_argument('-config', '-c', default='./saved/adaptive_vc/vctk_model.config.yaml', help='config file path') parser.add_argument('-model', '-m', default ='./saved/adaptive_vc/vctk_model.ckpt', help='model path') - parser.add_argument('-source', '-s', default='./autodl-fs/VCTK-Corpus/wav48/p231/p231_006.wav') - parser.add_argument('-target', '-t', default='./autodl-fs/VCTK-Corpus/wav48/p252/p253_032.wav') + parser.add_argument('-source', '-s', default='./dataset/VCTK-Corpus/wav48/p231/p231_006.wav') + parser.add_argument('-target', '-t', default='./dataset/VCTK-Corpus/wav48/p252/p253_032.wav') parser.add_argument('-output', '-o', default='./result/evaluate/eval.wav') parser.add_argument('-sample_rate', '-sr', help='sample rate', default=24000, type=int) args = parser.parse_args() diff --git a/run_talkingface.py b/run_talkingface.py index ea698f32..1397ff1b 100644 --- a/run_talkingface.py +++ b/run_talkingface.py @@ -2,7 +2,7 @@ Description: Author: Fu Yuxuan Date: 2024-01-15 20:16:39 -LastEditTime: 2024-01-19 22:21:57 +LastEditTime: 2024-01-21 22:51:39 ''' from argparse import ArgumentParser, Namespace import torch @@ -15,7 +15,7 @@ parser.add_argument("--model", "-m", type=str, default="Adaptive_VC", help="name of models") parser.add_argument("--dataset", type=str, default="VCTK", help="name of datasets") parser.add_argument('-config', '-c', default='./talkingface/properties/model/adaptive-VC.yaml') - parser.add_argument('-data_dir', '-d', default='./autodl-fs/preprocessed_data') + parser.add_argument('-data_dir', '-d', default='./dataset/preprocessed_data') parser.add_argument('-train_set', default='train_128') parser.add_argument('-train_index_file', default='train_samples_128.json') parser.add_argument('-logdir', default='./tf-logs/') From 7038762faa6127aa585c53e5c74766169300737c Mon Sep 17 00:00:00 2001 From: bengbeng-bomb <10098452+bit_fuyuxuan@user.noreply.gitee.com> Date: Mon, 22 Jan 2024 10:44:47 +0800 Subject: [PATCH 5/5] =?UTF-8?q?1/22=20=E6=9B=B4=E6=96=B0=E4=BA=86readme?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 62 ++--- .../inference_output.log} | 3 +- .../eval.wav => inference/output.wav} | Bin result/{evaluate => inference}/source.wav | Bin result/{evaluate => inference}/target.wav | Bin run_evaluator.py => run_inference.py | 8 +- talkingface/evaluator/base_metric.py | 98 +++++++ talkingface/evaluator/metric_models.py | 105 ++++++++ talkingface/evaluator/metrics.py | 243 ++++++++++++++++++ talkingface/evaluator/register.py | 46 ++++ talkingface/utils/data_process.py | 95 +++++++ talkingface/utils/enum_type.py | 13 + talkingface/utils/logger.py | 95 +++++++ talkingface/utils/wandblogger.py | 57 ++++ 14 files changed, 792 insertions(+), 33 deletions(-) rename result/{evaluate/evaluate_output.log => inference/inference_output.log} (98%) rename result/{evaluate/eval.wav => inference/output.wav} (100%) rename result/{evaluate => inference}/source.wav (100%) rename result/{evaluate => inference}/target.wav (100%) rename run_evaluator.py => run_inference.py (84%) create mode 100644 talkingface/evaluator/base_metric.py create mode 100644 talkingface/evaluator/metric_models.py create mode 100644 talkingface/evaluator/metrics.py create mode 100644 talkingface/evaluator/register.py create mode 100644 talkingface/utils/data_process.py create mode 100644 talkingface/utils/enum_type.py create mode 100644 talkingface/utils/logger.py create mode 100644 talkingface/utils/wandblogger.py diff --git a/README.md b/README.md index c975f277..c1c59b50 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ ## Abstract -- 实验项目为voice_conversion类别下的adaptive-VC； +- 实验项目为voice_conversion类别下的adaptive-VC - 项目由付宇轩1120213142个人完成 - 重构代码地址：https://github.com/FFFXX0319/talkingface-toolkit.git @@ -10,7 +10,7 @@ - 文档主要分为两个部分： - 代码结构介绍：对重构的代码结构进行介绍，并说明完成的工作 - - 实验步骤：说明实验流程，对模型训练和验证的结果进行展示 + - 实验步骤：说明实验流程，对模型训练、验证和推理的结果进行展示 ## 代码结构介绍 @@ -28,7 +28,7 @@ 实验使用的数据集是CSTR VCTK Corpus数据集，可以在下面的地址中下载获得。 -\- [CSTR VCTK Corpus](https://homepages.inf.ed.ac.uk/jyamagis/page3/page58/page58.html) +\-[CSTR VCTK Corpus](https://datashare.ed.ac.uk/handle/10283/3443) 在dataset文件夹中，VCTK-Corpus存放的是在网站下载的原始数据，preprocessed_data存放的是经过预处理之后的数据。 @@ -57,15 +57,15 @@ dataset ### result -result文件夹存放了模型训练和验证的log文件，以及模型生成的语音wav文件。 +result文件夹存放了模型训练和验证的log文件，以及模型推理生成的语音wav文件。 ```python result -├── evaluate +├── inference │ ├── source.wav │ ├── target.wav -│ ├── eval.wav -│ └── evaluate_output.log +│ ├── output.wav +│ └── inference_output.log └── train └── train_output.log ``` @@ -77,9 +77,9 @@ result ```python saved └── adaptive_vc - ├── vctk_model.args.yaml # 训练时的命令参数配置文件 + ├── vctk_model.args.yaml # 训练生成的命令参数配置文件 ├── vctk_model.ckpt # 训练后模型的checkpoint - ├── vctk_model.config.yaml # 模型的参数配置文件 + ├── vctk_model.config.yaml # 训练生成的模型参数配置文件 └── vctk_model.opt ``` @@ -118,29 +118,36 @@ data ``` - dataprocess：模型特有的数据处理代码，可以通过运行preprocess_vctk.sh脚本直接进行数据的处理。 - -- dataset： - - vctk_dataset.py: 创建SequenceDataset，PickleDataset， get_data_loader等子类，继承自基类Dataset，实现基类中的方法。 +- vctk_dataset.py: 创建SequenceDataset，PickleDataset子类，继承自基类Dataset，实现基类中的方法。 + - SequenceDataset子类用于处理数据存储在字典中，每个键对应一个完整的数据序列的情况。 + - PickleDataset子类用于处理数据存储在pickle文件中，需要根据样本索引截取数据段的情况。 #### model - +- model_adaptive_VC.py：创建MLP，Prenet，SpeakerEncoder，ContentEncoder，Decoder，AE等子类，继承自基类AbstractTalkingFace。 + - MLP子类实现了一个多层感知机模型，具有输入层、多个块（每个块包含两个全连接层和激活函数），以及最后的输出层。在前向传播过程中，输入依次经过每个块，其中输出层的结果与隐藏层相加，形成更新后的隐藏层输出，最终返回更新后的隐藏层张量作为模型的输出。 + - Prenet类用于语音合成任务中的预处理阶段，将输入张量通过多个卷积块和跳跃连接的层，经过一系列的卷积、激活函数、实例归一化和dropout操作，最终输出一个经过1D卷积和激活函数处理的张量。 + - SpeakerEncoder和ContentEncoder都是语音编码模型，通过一系列的卷积和全连接操作，对语音信号进行特征提取和编码。其中SpeakerEncoder 子类是对说话人特征进行编码，ContentEncoder是对语音内容进行编码。 + - Decoder 类实现了一个解码器模型，用于将编码后的语音内容向量解码为语音信号。它接收一个表示语音内容的向量以及条件信息作为输入，并通过一系列的卷积、上采样和转换操作生成输出的语音信号。 + - AE类封装了一个能够对语音信号进行编码和解码的自编码器模型，通过 SpeakerEncoder 对说话人特征进行编码，通过 ContentEncoder 对语音内容进行编码，然后通过 Decoder 进行解码，生成重建的语音信号。 #### properties - +- dataset：vctk.config VCTK数据集配置文件 +- model：adaptive-VC.yaml adaptive-VC模型配置文件 #### utils - +- data_utils.py：数据处理相关的工具类，与源代码一致。 +- utils.py：模型公用的工具类，包含训练设备获取、tensorboard日志文件等。 #### trainer - +- trainer.py：在Trainer类中增添了一些用于模型训练的方法，如save_config、ae_step等。 #### evalutor - +- evaluator.py：在Evaluator类中增添了一些用于模型推理的方法，如load_model、denormalize、normalize、write_wav_to_file等。 ## 实验步骤 @@ -170,7 +177,7 @@ data - **安装实验所用的第三方库** - ​ 实验根据文档提出的环境要求进行了尝试，使用`python=3.8`，`torch==1.13.1+cu116`，`numpy==1.20.3`，`librosa==0.10.1`进行环境配置，但是在项目的复现中遇到了非常多的版本冲突。项目源码实现的环境使用了低于0.8.0版本的librosa，低于0.50.0版本的numba，使用文档要求的环境会引发一系列与numba\llvmlite\python包版本不兼容的问题，所以实验使用了较早版本的第三方库环境，具体的第三方库配置已导出到requirement.txt中。 + 实验根据文档提出的环境要求进行了尝试，使用`python=3.8`，`torch==1.13.1+cu116`，`numpy==1.20.3`，`librosa==0.10.1`进行环境配置，但是在项目的复现中遇到了非常多的版本冲突。项目源码实现的环境使用了低于0.8.0版本的librosa，低于0.50.0版本的numba，使用文档要求的环境会引发一系列与numba\llvmlite\python包版本不兼容的问题，所以实验使用了较早版本的第三方库环境，具体的第三方库配置已导出到requirement.txt中。 - torch=1.0.1 torchvision=0.2.2 - numba=0.48.0 @@ -194,9 +201,9 @@ bash preprocess_vctk.sh ![image-20240119123956865](README.assets/image-20240119123956865.png) -### 3.模型训练 +### 3.模型训练和验证 -使用run_talkingface.py进行模型的训练 +使用run_talkingface.py进行模型的训练和验证 ``` python run_talkingface.py --model=Adaptive_VC --dataset=VCTK @@ -239,15 +246,15 @@ python run_talkingface.py --model=Adaptive_VC --dataset=VCTK ![image-20240121231348020](README.assets/image-20240121231348020.png) -### 4.模型验证 +### 4.模型推理 -使用run_evaluator.py进行模型的验证 +使用run_inference.py进行模型的推理 ``` -python run_evaluator.py +python run_inference.py ``` -模型验证的参设置如下： +模型推理的参设置如下： - **-c**, the path of config file. - **-m**:,the path of model checkpoint. @@ -256,12 +263,7 @@ python run_evaluator.py - **-t**, the path of target file (.wav). - **-o**, the path of output converted file (.wav). -验证使用的模型为`saved/adaptive_vc/vctk_model.ckpt`，模型验证结果的输出在`result\evaluate\evaluate_output.log`，生成的语音在`result\evaluate\eval.wav`。 - -## 作业要求 - -- 确保可以仅在命令行输入模型和数据集名称就可以训练、验证。（部分仓库没有提供训练代码的，可以不训练） -- 每个组都要提交一个README文件，写明==完成的功能==、最终**实现的训练、验证截图**、**所使用的依赖**、**成员分工**等。 +验证使用的模型为`saved/adaptive_vc/vctk_model.ckpt`，模型推理结果的输出在`result\inference\inference_output.log`，生成的语音在`result\inference\output.wav`。 diff --git a/result/evaluate/evaluate_output.log b/result/inference/inference_output.log similarity index 98% rename from result/evaluate/evaluate_output.log rename to result/inference/inference_output.log index f1f6b7a2..938912ad 100644 --- a/result/evaluate/evaluate_output.log +++ b/result/inference/inference_output.log @@ -122,5 +122,4 @@ AE( (dropout_layer): Dropout(p=0) ) ) -Load model from ./saved/adaptive_vc/vctk_model.ckpt -wav result in result/evaluate/eval.wav \ No newline at end of file +Load model from ./saved/adaptive_vc/vctk_model.ckpt \ No newline at end of file diff --git a/result/evaluate/eval.wav b/result/inference/output.wav similarity index 100% rename from result/evaluate/eval.wav rename to result/inference/output.wav diff --git a/result/evaluate/source.wav b/result/inference/source.wav similarity index 100% rename from result/evaluate/source.wav rename to result/inference/source.wav diff --git a/result/evaluate/target.wav b/result/inference/target.wav similarity index 100% rename from result/evaluate/target.wav rename to result/inference/target.wav diff --git a/run_evaluator.py b/run_inference.py similarity index 84% rename from run_evaluator.py rename to run_inference.py index ec0042c8..7f8a4c90 100644 --- a/run_evaluator.py +++ b/run_inference.py @@ -1,3 +1,9 @@ +''' +Description: +Author: Fu Yuxuan +Date: 2024-01-19 22:22:13 +LastEditTime: 2024-01-22 10:35:00 +''' from argparse import ArgumentParser, Namespace import torch from talkingface.evaluator.evaluator import Evaluator @@ -12,7 +18,7 @@ parser.add_argument('-model', '-m', default ='./saved/adaptive_vc/vctk_model.ckpt', help='model path') parser.add_argument('-source', '-s', default='./dataset/VCTK-Corpus/wav48/p231/p231_006.wav') parser.add_argument('-target', '-t', default='./dataset/VCTK-Corpus/wav48/p252/p253_032.wav') - parser.add_argument('-output', '-o', default='./result/evaluate/eval.wav') + parser.add_argument('-output', '-o', default='./result/inference/output.wav') parser.add_argument('-sample_rate', '-sr', help='sample rate', default=24000, type=int) args = parser.parse_args() # load config file diff --git a/talkingface/evaluator/base_metric.py b/talkingface/evaluator/base_metric.py new file mode 100644 index 00000000..81e15721 --- /dev/null +++ b/talkingface/evaluator/base_metric.py @@ -0,0 +1,98 @@ +import torch +from talkingface.utils import EvaluatorType + +class AbstractMetric(object): + """:class:`AbstractMetric` is the base object of all metrics. If you want to + implement a metric, you should inherit this class. + + Args: + config (Config): the config of evaluator. + """ + + smaller = False + + def __init__(self, config): + self.decimal_place = config["metric_decimal_place"] + + def calculate_metric(self, dataobject): + """Get the dictionary of a metric. + + Args: + dataobject: (dict): it contains all the information needed to calculate metrics. + + Returns: + dict: such as ``{LSE-C': 0.0000}`` + """ + raise NotImplementedError("Method [calculate_metric] should be implemented.") + + +class SyncMetric(AbstractMetric): + """Base class for all Sync metrics. If you want to implement a sync metric, you can inherit this class. + """ + + metric_type = EvaluatorType.SYNC + metric_need = ["generated_video"] + def __init__(self, config): + super(SyncMetric, self).__init__(config) + + def get_videolist(self, dataobject): + """Get the list of videos. + + Args: + dataobject(DataStruct): (dict): it contains all the information needed to calculate metrics. + + Returns: + list: a list of videos. + """ + return dataobject["generated_video"] + + def metric_info(self, dataobject): + """Calculate the value of the metric. + + Args: + dataobject(DataStruct): it contains all the information needed to calculate metrics. + + Returns: + dict: {"LSE-C": LSE_C, "LSE-D": LSE_D} + """ + raise NotImplementedError("Method [metric_info] should be implemented.") + +class VideoQMetric(AbstractMetric): + """Base class for all Video Quality metrics. If you want to implement a Video Quality metric, you can inherit this class. + """ + + metric_type = EvaluatorType.VIDEOQ + def __init__(self, config): + super(VideoQMetric, self).__init__(config) + + def get_videopair(self, dataobject): + return list(zip(dataobject["generated_video"], dataobject["real_video"])) + + def metric_info(self, dataobject): + """Calculate the value of the metric. + + Args: + dataobject(DataStruct): (dict): it contains all the information needed to calculate metrics. + + Returns: + float: the value of the metric. + """ + raise NotImplementedError("Method [metric_info] should be implemented.") + +# class AudioQMetric(AbstractMetric): +# """Base class for all Audio Quality metrics. If you want to implement a Audio Quality metric, you can inherit this class. +# """ +# def __init__(self, config): +# super(SyncMetric, self).__init__(config) + +# def metric_info(self, dataobject): +# """Calculate the value of the metric. + +# Args: +# dataobject(DataStruct): it contains all the information needed to calculate metrics. + +# Returns: +# float: the value of the metric. +# """ +# raise NotImplementedError("Method [metric_info] should be implemented.") + diff --git a/talkingface/evaluator/metric_models.py b/talkingface/evaluator/metric_models.py new file mode 100644 index 00000000..3a38ccba --- /dev/null +++ b/talkingface/evaluator/metric_models.py @@ -0,0 +1,105 @@ +import torch +import torch.nn as nn + +class S(nn.Module): + def __init__(self, num_layers_in_fc_layers = 1024): + super(S, self).__init__() + + self.__nFeatures__ = 24 + self.__nChs__ = 32 + self.__midChs__ = 32 + + self.netcnnaud = nn.Sequential( + nn.Conv2d(1, 64, kernel_size=(3,3), stride=(1,1), padding=(1,1)), + nn.BatchNorm2d(64), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=(1,1), stride=(1,1)), + + nn.Conv2d(64, 192, kernel_size=(3,3), stride=(1,1), padding=(1,1)), + nn.BatchNorm2d(192), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=(3,3), stride=(1,2)), + + nn.Conv2d(192, 384, kernel_size=(3,3), padding=(1,1)), + nn.BatchNorm2d(384), + nn.ReLU(inplace=True), + + nn.Conv2d(384, 256, kernel_size=(3,3), padding=(1,1)), + nn.BatchNorm2d(256), + nn.ReLU(inplace=True), + + nn.Conv2d(256, 256, kernel_size=(3,3), padding=(1,1)), + nn.BatchNorm2d(256), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=(3,3), stride=(2,2)), + + nn.Conv2d(256, 512, kernel_size=(5,4), padding=(0,0)), + nn.BatchNorm2d(512), + nn.ReLU(), + ) + + self.netfcaud = nn.Sequential( + nn.Linear(512, 512), + nn.BatchNorm1d(512), + nn.ReLU(), + nn.Linear(512, num_layers_in_fc_layers), + ) + + self.netfclip = nn.Sequential( + nn.Linear(512, 512), + nn.BatchNorm1d(512), + nn.ReLU(), + nn.Linear(512, num_layers_in_fc_layers), + ) + + self.netcnnlip = nn.Sequential( + nn.Conv3d(3, 96, kernel_size=(5,7,7), stride=(1,2,2), padding=0), + nn.BatchNorm3d(96), + nn.ReLU(inplace=True), + nn.MaxPool3d(kernel_size=(1,3,3), stride=(1,2,2)), + + nn.Conv3d(96, 256, kernel_size=(1,5,5), stride=(1,2,2), padding=(0,1,1)), + nn.BatchNorm3d(256), + nn.ReLU(inplace=True), + nn.MaxPool3d(kernel_size=(1,3,3), stride=(1,2,2), padding=(0,1,1)), + + nn.Conv3d(256, 256, kernel_size=(1,3,3), padding=(0,1,1)), + nn.BatchNorm3d(256), + nn.ReLU(inplace=True), + + nn.Conv3d(256, 256, kernel_size=(1,3,3), padding=(0,1,1)), + nn.BatchNorm3d(256), + nn.ReLU(inplace=True), + + nn.Conv3d(256, 256, kernel_size=(1,3,3), padding=(0,1,1)), + nn.BatchNorm3d(256), + nn.ReLU(inplace=True), + nn.MaxPool3d(kernel_size=(1,3,3), stride=(1,2,2)), + + nn.Conv3d(256, 512, kernel_size=(1,6,6), padding=0), + nn.BatchNorm3d(512), + nn.ReLU(inplace=True), + ) + + def forward_aud(self, x): + mid = self.netcnnaud(x); # N x ch x 24 x M + mid = mid.view((mid.size()[0], -1)); # N x (ch x 24) + out = self.netfcaud(mid) + + return out + + def forward_lip(self, x): + + mid = self.netcnnlip(x); + mid = mid.view((mid.size()[0], -1)); # N x (ch x 24) + out = self.netfclip(mid) + + return out + + def forward_lipfeat(self, x): + + mid = self.netcnnlip(x) + out = mid.view((mid.size()[0], -1)); # N x (ch x 24) + + return out + \ No newline at end of file diff --git a/talkingface/evaluator/metrics.py b/talkingface/evaluator/metrics.py new file mode 100644 index 00000000..eb2692c7 --- /dev/null +++ b/talkingface/evaluator/metrics.py @@ -0,0 +1,243 @@ +import torch +import numpy +import time, pdb, argparse, subprocess, os, math, glob +import cv2 +import python_speech_features +from tqdm import tqdm +from scipy import signal +from scipy.io import wavfile +from talkingface.evaluator.metric_models import * +from shutil import rmtree +from skimage.metrics import structural_similarity as ssim +from talkingface.evaluator.base_metric import AbstractMetric, SyncMetric, VideoQMetric +from talkingface.utils.logger import set_color + +class LSE(SyncMetric): + + ''' + ''' + def __init__(self, config, num_layers_in_fc_layers = 1024): + super(LSE, self).__init__(config) + self.config = config + self.syncnet = S(num_layers_in_fc_layers = num_layers_in_fc_layers) + + def metric_info(self, videofile): + self.loadParameters(self.config['lse_checkpoint_path']) + self.syncnet.to(self.config["device"]) + self.syncnet.eval() + + if os.path.exists(os.path.join(self.config['temp_dir'], self.config['lse_reference_dir'])): + rmtree(os.path.join(self.config['temp_dir'], self.config['lse_reference_dir'])) + + os.makedirs(os.path.join(self.config['temp_dir'], self.config['lse_reference_dir'])) + + command = ("ffmpeg -loglevel error -y -i %s -threads 1 -f image2 %s" % (videofile,os.path.join(self.config['temp_dir'], self.config['lse_reference_dir'],'%06d.jpg'))) + output = subprocess.call(command, shell=True, stdout=None) + + command = ("ffmpeg -loglevel error -y -i %s -async 1 -ac 1 -vn -acodec pcm_s16le -ar 16000 %s" % (videofile,os.path.join(self.config['temp_dir'], self.config['lse_reference_dir'],'audio.wav'))) + output = subprocess.call(command, shell=True, stdout=None) + + images = [] + + flist = glob.glob(os.path.join(self.config['temp_dir'], self.config['lse_reference_dir'],'*.jpg')) + flist.sort() + + for fname in flist: + img_input = cv2.imread(fname) + img_input = cv2.resize(img_input, (224, 224)) + images.append(img_input) + + im = numpy.stack(images, axis=3) + im = numpy.expand_dims(im, axis=0) + im = numpy.transpose(im, (0, 3, 4, 1, 2)) + + imtv = torch.autograd.Variable(torch.from_numpy(im.astype(float)).float()) + + sample_rate, audio = wavfile.read(os.path.join(self.config['temp_dir'], self.config['lse_reference_dir'],'audio.wav')) + mfcc = zip(*python_speech_features.mfcc(audio,sample_rate)) + mfcc = numpy.stack([numpy.array(i) for i in mfcc]) + + cc = numpy.expand_dims(numpy.expand_dims(mfcc,axis=0),axis=0) + cct = torch.autograd.Variable(torch.from_numpy(cc.astype(float)).float()) + + # ========== ========== + # Check audio and video input length + # ========== ========== + + #if (float(len(audio))/16000) != (float(len(images))/25) : + # print("WARNING: Audio (%.4fs) and video (%.4fs) lengths are different."%(float(len(audio))/16000,float(len(images))/25)) + + min_length = min(len(images),math.floor(len(audio)/640)) + + # ========== ========== + # Generate video and audio feats + # ========== ========== + + lastframe = min_length-5 + im_feat = [] + cc_feat = [] + + tS = time.time() + for i in range(0,lastframe,self.config['evaluate_batch_size']): + + im_batch = [ imtv[:,:,vframe:vframe+5,:,:] for vframe in range(i,min(lastframe, i+self.config['evaluate_batch_size'])) ] + im_in = torch.cat(im_batch,0) + im_out = self.syncnet.forward_lip(im_in.cuda()) + im_feat.append(im_out.data.cpu()) + + cc_batch = [ cct[:,:,:,vframe*4:vframe*4+20] for vframe in range(i,min(lastframe, i+self.config['evaluate_batch_size'])) ] + cc_in = torch.cat(cc_batch,0) + cc_out = self.syncnet.forward_aud(cc_in.cuda()) + cc_feat.append(cc_out.data.cpu()) + + im_feat = torch.cat(im_feat,0) + cc_feat = torch.cat(cc_feat,0) + + # ========== ========== + # Compute offset + # ========== ========== + + #print('Compute time %.3f sec.' % (time.time()-tS)) + + dists = self.calc_pdist(im_feat,cc_feat,vshift=self.config['vshift']) + mdist = torch.mean(torch.stack(dists,1),1) + + minval, minidx = torch.min(mdist,0) + + offset = self.config['vshift']-minidx + conf = torch.median(mdist) - minval + + fdist = numpy.stack([dist[minidx].numpy() for dist in dists]) + # fdist = numpy.pad(fdist, (3,3), 'constant', constant_values=15) + fconf = torch.median(mdist).numpy() - fdist + fconfm = signal.medfilt(fconf,kernel_size=9) + + numpy.set_printoptions(formatter={'float': '{: 0.3f}'.format}) + + return conf.numpy(), minval.numpy() + + + + def calc_pdist(self, feat1, feat2, vshift=10): + + win_size = vshift*2+1 + + feat2p = torch.nn.functional.pad(feat2,(0,0,vshift,vshift)) + + dists = [] + + for i in range(0,len(feat1)): + + dists.append(torch.nn.functional.pairwise_distance(feat1[[i],:].repeat(win_size, 1), feat2p[i:i+win_size,:])) + + return dists + + def calculate_metric(self, dataobject): + video_list = self.get_videolist(dataobject) + + LSE_dict = {} + + iter_data = ( + tqdm( + video_list, + total=len(video_list), + desc=set_color("calculate for lip-audio sync", "yellow") + ) + if self.config['show_progress'] + else video_list + ) + + for video in iter_data: + LSE_C, LSE_D = self.metric_info(video) + if "LSE_C" not in LSE_dict: + LSE_dict["LSE_C"] = [LSE_C] + else: + LSE_dict["LSE_C"].append(LSE_C) + if "LSE_D" not in LSE_dict: + LSE_dict["LSE_D"] = [LSE_D] + else: + LSE_dict["LSE_D"].append(LSE_D) + + return {"LSE-C: {}".format(sum(LSE_dict["LSE_C"])/len(LSE_dict["LSE_C"])), "LSE-D: {}".format(sum(LSE_dict["LSE_D"])/len(LSE_dict["LSE_D"]))} + + def loadParameters(self, path): + loaded_state = torch.load(path, map_location=lambda storage, loc: storage) + + self_state = self.syncnet.state_dict() + + for name, param in loaded_state.items(): + + self_state[name].copy_(param) + + +class SSIM(VideoQMetric): + + metric_need = ["generated_video", "real_video"] + + def __init__(self, config): + super(SSIM, self).__init__(config) + self.config = config + + def metric_info(self, g_videofile, r_videofile): + g_frames = [] + g_video = cv2.VideoCapture(g_videofile) + while g_video.isOpened(): + ret, frame = g_video.read() + if not ret: + break + frame = cv2.resize(frame, (224, 224)) + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + g_frames.append(gray) + g_video.release() + + r_frames = [] + r_video = cv2.VideoCapture(r_videofile) + while r_video.isOpened(): + ret, frame = r_video.read() + if not ret: + break + frame = cv2.resize(frame, (224, 224)) + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + r_frames.append(gray) + r_video.release() + + min_frames = min(len(g_frames), len(r_frames)) + + g_frames = g_frames[:min_frames] + r_frames = r_frames[:min_frames] + + ssim_scores = [] + for frame1, frame2 in zip(g_frames, r_frames): + # 计算两帧之间的SSIM + score, _ = ssim(frame1, frame2, full=True) + ssim_scores.append(score) + + return numpy.mean(ssim_scores) + + + + + + + def calculate_metric(self, dataobject): + + pair_list = self.get_videopair(dataobject) + + ssim_score_total = [] + + iter_data = ( + tqdm( + pair_list, + total=len(pair_list), + desc=set_color("calculate for video quality ssim", "yellow") + ) + if self.config['show_progress'] + else pair_list + ) + for pair in iter_data: + g_video = pair[0] + r_video = pair[1] + ssim_score = self.metric_info(g_video, r_video) + ssim_score_total.append(ssim_score) + + return sum(ssim_score_total)/len(ssim_score_total) diff --git a/talkingface/evaluator/register.py b/talkingface/evaluator/register.py new file mode 100644 index 00000000..2ff33c3b --- /dev/null +++ b/talkingface/evaluator/register.py @@ -0,0 +1,46 @@ +import inspect +import sys + +def cluster_info(module_name): + """Collect information of all metrics, including: + + - ``metric_need``: Information needed to calculate this metric, the combination of ``rec.items, rec.topk, + rec.meanrank, rec.score, data.num_items, data.num_users, data.count_items, data.count_users, data.label``. + - ``metric_type``: Whether the scores required by metric are grouped by user, range in ``EvaluatorType.RANKING`` + and ``EvaluatorType.VALUE``. + - ``smaller``: Whether the smaller metric value represents better performance, + range in ``True`` and ``False``, default to ``False``. + + Args: + module_name (str): the name of module ``recbole.evaluator.metrics``. + + Returns: + dict: Three dictionaries containing the above information + and a dictionary matching metric names to metric classes. + """ + smaller_m = [] + m_dict, m_info, m_types = {}, {}, {} + metric_class = inspect.getmembers( + sys.modules[module_name], + lambda x: inspect.isclass(x) and x.__module__ == module_name, + ) + for name, metric_cls in metric_class: + name = name.lower() + m_dict[name] = metric_cls + if hasattr(metric_cls, "metric_need"): + m_info[name] = metric_cls.metric_need + else: + raise AttributeError(f"Metric '{name}' has no attribute [metric_need].") + if hasattr(metric_cls, "metric_type"): + m_types[name] = metric_cls.metric_type + else: + raise AttributeError(f"Metric '{name}' has no attribute [metric_type].") + if metric_cls.smaller is True: + smaller_m.append(name) + return smaller_m, m_info, m_types, m_dict + + +metric_module_name = "talkingface.evaluator.metrics" +smaller_metrics, metric_information, metric_types, metrics_dict = cluster_info( + metric_module_name +) \ No newline at end of file diff --git a/talkingface/utils/data_process.py b/talkingface/utils/data_process.py new file mode 100644 index 00000000..cbc430ac --- /dev/null +++ b/talkingface/utils/data_process.py @@ -0,0 +1,95 @@ +import os +import cv2 +import numpy as np +import subprocess +from tqdm import tqdm +from glob import glob +from concurrent.futures import ThreadPoolExecutor, as_completed +from talkingface.utils import face_detection +import traceback +import librosa +import librosa.filters +from scipy import signal +from scipy.io import wavfile + + +class lrs2Preprocess: + def __init__(self, config): + self.config = config + self.fa = [face_detection.FaceAlignment(face_detection.LandmarksType._2D, flip_input=False, + device=f'cuda:{id}') for id in range(config['ngpu'])] + self.template = 'ffmpeg -loglevel panic -y -i {} -strict -2 {}' + + def process_video_file(self, vfile, gpu_id): + video_stream = cv2.VideoCapture(vfile) + + frames = [] + while 1: + still_reading, frame = video_stream.read() + if not still_reading: + video_stream.release() + break + frames.append(frame) + + vidname = os.path.basename(vfile).split('.')[0] + dirname = vfile.split('/')[-2] + + fulldir = os.path.join(self.config['preprocessed_root'], dirname, vidname) + os.makedirs(fulldir, exist_ok=True) + + batches = [frames[i:i + self.config['preprocess_batch_size']] for i in range(0, len(frames), self.config['preprocess_batch_size'])] + + i = -1 + for fb in batches: + preds = self.fa[gpu_id].get_detections_for_batch(np.asarray(fb)) + + for j, f in enumerate(preds): + i += 1 + if f is None: + continue + + x1, y1, x2, y2 = f + cv2.imwrite(os.path.join(fulldir, '{}.jpg'.format(i)), fb[j][y1:y2, x1:x2]) + + + def process_audio_file(self, vfile): + vidname = os.path.basename(vfile).split('.')[0] + dirname = vfile.split('/')[-2] + + fulldir = os.path.join(self.config['preprocessed_root'], dirname, vidname) + os.makedirs(fulldir, exist_ok=True) + + wavpath = os.path.join(fulldir, 'audio.wav') + + command =self.template.format(vfile, wavpath) + subprocess.call(command, shell=True) + + def mp_handler(self, job): + vfile, gpu_id = job + try: + self.process_video_file(vfile, gpu_id) + except KeyboardInterrupt: + exit(0) + except: + traceback.print_exc() + + def run(self): + print(f'Started processing for {self.config["data_root"]} with {self.config["ngpu"]} GPUs') + + filelist = glob(os.path.join(self.config["data_root"], '*/*.mp4')) + + # jobs = [(vfile, i % self.config["ngpu"]) for i, vfile in enumerate(filelist)] + # with ThreadPoolExecutor(self.config["ngpu"]) as p: + # futures = [p.submit(self.mp_handler, j) for j in jobs] + # _ = [r.result() for r in tqdm(as_completed(futures), total=len(futures))] + + print('Dumping audios...') + for vfile in tqdm(filelist): + try: + self.process_audio_file(vfile) + except KeyboardInterrupt: + exit(0) + except: + traceback.print_exc() + continue + diff --git a/talkingface/utils/enum_type.py b/talkingface/utils/enum_type.py new file mode 100644 index 00000000..b701b38b --- /dev/null +++ b/talkingface/utils/enum_type.py @@ -0,0 +1,13 @@ +from enum import Enum + +class EvaluatorType(Enum): + """Type for evaluation metrics. + + - ``SYNC``: SYNC metrics like LSE-C, LSE-D, etc. + - ``VIDEOQ``: Video quality metrics like FID, etc. + - ``AUDIOQ``: Audio quality metrics like PESQ, etc. + """ + + SYNC = 1 + VIDEOQ = 2 + AUDIOQ = 3 \ No newline at end of file diff --git a/talkingface/utils/logger.py b/talkingface/utils/logger.py new file mode 100644 index 00000000..855dbb94 --- /dev/null +++ b/talkingface/utils/logger.py @@ -0,0 +1,95 @@ +import logging +import os +import colorlog +import re +import hashlib +from talkingface.utils.utils import get_local_time, ensure_dir +from colorama import init + +log_colors_config = { + "DEBUG": "cyan", + "WARNING": "yellow", + "ERROR": "red", + "CRITICAL": "red", +} + +class RemoveColorFilter(logging.Filter): + def filter(self, record): + if record: + ansi_escape = re.compile(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])") + record.msg = ansi_escape.sub("", str(record.msg)) + return True + +def set_color(log, color, highlight=True): + color_set = ["black", "red", "green", "yellow", "blue", "pink", "cyan", "white"] + try: + index = color_set.index(color) + except: + index = len(color_set) - 1 + prev_log = "\033[" + if highlight: + prev_log += "1;3" + else: + prev_log += "0;3" + prev_log += str(index) + "m" + return prev_log + log + "\033[0m" + +def init_logger(config): + """ + A logger that can show a message on standard output and write it into the + file named `filename` simultaneously. + All the message that you want to log MUST be str. + + Args: + config (Config): An instance object of Config, used to record parameter information. + + Example: + >>> logger = logging.getLogger(config) + >>> logger.debug(train_state) + >>> logger.info(train_result) + """ + init(autoreset=True) + LOGROOT = "./log/" + dir_name = os.path.dirname(LOGROOT) + ensure_dir(dir_name) + model_name = os.path.join(dir_name, config["model"]) + ensure_dir(model_name) + config_str = "".join([str(key) for key in config.final_config_dict.values()]) + md5 = hashlib.md5(config_str.encode(encoding="utf-8")).hexdigest()[:6] + logfilename = "{}/{}-{}-{}-{}.log".format( + config["model"], config["model"], config["dataset"], get_local_time(), md5 + ) + + logfilepath = os.path.join(LOGROOT, logfilename) + + filefmt = "%(asctime)-15s %(levelname)s %(message)s" + filedatefmt = "%a %d %b %Y %H:%M:%S" + fileformatter = logging.Formatter(filefmt, filedatefmt) + + sfmt = "%(log_color)s%(asctime)-15s %(levelname)s %(message)s" + sdatefmt = "%d %b %H:%M" + sformatter = colorlog.ColoredFormatter(sfmt, sdatefmt, log_colors=log_colors_config) + if config["state"] is None or config["state"].lower() == "info": + level = logging.INFO + elif config["state"].lower() == "debug": + level = logging.DEBUG + elif config["state"].lower() == "error": + level = logging.ERROR + elif config["state"].lower() == "warning": + level = logging.WARNING + elif config["state"].lower() == "critical": + level = logging.CRITICAL + else: + level = logging.INFO + + fh = logging.FileHandler(logfilepath) + fh.setLevel(level) + fh.setFormatter(fileformatter) + remove_color_filter = RemoveColorFilter() + fh.addFilter(remove_color_filter) + + sh = logging.StreamHandler() + sh.setLevel(level) + sh.setFormatter(sformatter) + + logging.basicConfig(level=level, handlers=[sh, fh]) \ No newline at end of file diff --git a/talkingface/utils/wandblogger.py b/talkingface/utils/wandblogger.py new file mode 100644 index 00000000..8d958474 --- /dev/null +++ b/talkingface/utils/wandblogger.py @@ -0,0 +1,57 @@ +class WandbLogger(object): + """WandbLogger to log metrics to Weights and Biases.""" + + def __init__(self, config): + """ + Args: + config (dict): A dictionary of parameters used by RecBole. + """ + self.config = config + self.log_wandb = config.log_wandb + self.setup() + + def setup(self): + if self.log_wandb: + try: + import wandb + + self._wandb = wandb + except ImportError: + raise ImportError( + "To use the Weights and Biases Logger please install wandb." + "Run `pip install wandb` to install it." + ) + + # Initialize a W&B run + if self._wandb.run is None: + self._wandb.init(project=self.config.wandb_project, config=self.config) + + self._set_steps() + + def log_metrics(self, metrics, head="train", commit=True): + if self.log_wandb: + if head: + metrics = self._add_head_to_metrics(metrics, head) + self._wandb.log(metrics, commit=commit) + else: + self._wandb.log(metrics, commit=commit) + + def log_eval_metrics(self, metrics, head="eval"): + if self.log_wandb: + metrics = self._add_head_to_metrics(metrics, head) + for k, v in metrics.items(): + self._wandb.run.summary[k] = v + + def _set_steps(self): + self._wandb.define_metric("train/*", step_metric="train_step") + self._wandb.define_metric("valid/*", step_metric="valid_step") + + def _add_head_to_metrics(self, metrics, head): + head_metrics = dict() + for k, v in metrics.items(): + if "_step" in k: + head_metrics[k] = v + else: + head_metrics[f"{head}/{k}"] = v + + return head_metrics

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