From d7a7feca75061cc7c18764f7db7880058745e459 Mon Sep 17 00:00:00 2001 From: PetchMa Date: Wed, 3 Dec 2025 15:08:40 -0500 Subject: [PATCH] feat: major updates to TARTS including checkpoint loading, dataloader improvements, multiworker support, and precommit checks - Added backwards compatible checkpoint loading - Improved dataloader with multiworker support and coral file handling - Added aggregatornet_coral.py for coral-based aggregator network - Consolidated constants into a single constants.py file - Updated dependencies and configuration files - Added pre-commit hooks and CI workflow - Fixed linting issues and code style improvements - Updated dataset parameters and file paths --- .github/workflows/pre-commit.yml | 36 + .gitignore | 2 +- .pre-commit-config.yaml | 9 +- LICENSE | 2 +- conda-builds/index.html | 2 +- conda-builds/linux-64/current_repodata.json | 2 +- conda-builds/linux-64/index.html | 2 +- conda-builds/linux-64/repodata.json | 2 +- .../linux-64/repodata_from_packages.json | 2 +- conda-builds/noarch/current_repodata.json | 2 +- conda-builds/noarch/index.html | 2 +- conda-builds/noarch/repodata.json | 2 +- .../noarch/repodata_from_packages.json | 2 +- conda-recipe/build_output/index.html | 2 +- .../linux-64/current_repodata.json | 2 +- conda-recipe/build_output/linux-64/index.html | 2 +- .../build_output/linux-64/repodata.json | 2 +- .../linux-64/repodata_from_packages.json | 2 +- .../build_output/noarch/current_repodata.json | 2 +- conda-recipe/build_output/noarch/index.html | 2 +- .../build_output/noarch/repodata.json | 2 +- .../noarch/repodata_from_packages.json | 2 +- pyproject.toml | 57 + python/tarts/KERNEL.py | 323 +---- python/tarts/NeuralActiveOpticsSys.py | 718 ++++++---- python/tarts/__init__.py | 18 + python/tarts/aggregatornet.py | 42 +- python/tarts/aggregatornet_coral.py | 502 +++++++ python/tarts/alignnet.py | 41 +- python/tarts/constants.py | 343 +++++ python/tarts/dataloader.py | 588 ++++++-- python/tarts/dataset_params.yaml | 13 +- python/tarts/lightning_alignnet.py | 93 +- python/tarts/lightning_wavenet.py | 222 +-- python/tarts/lightning_wavenet_coral.py | 131 +- python/tarts/utils.py | 1273 ++++++++++++++--- python/tarts/wavenet.py | 140 +- requirements.txt | 2 + setup.py | 1 + 39 files changed, 3420 insertions(+), 1172 deletions(-) create mode 100644 .github/workflows/pre-commit.yml create mode 100644 pyproject.toml create mode 100644 python/tarts/aggregatornet_coral.py create mode 100644 python/tarts/constants.py diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml new file mode 100644 index 0000000..fe6d09a --- /dev/null +++ b/.github/workflows/pre-commit.yml @@ -0,0 +1,36 @@ +name: Pre-commit Checks + +on: + push: + branches: [main, develop, init_refactoring] + pull_request: + branches: [main, develop, init_refactoring] + +jobs: + pre-commit: + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.10' + + - name: Cache pre-commit + uses: actions/cache@v4 + with: + path: ~/.cache/pre-commit + key: ${{ runner.os }}-pre-commit-${{ hashFiles('.pre-commit-config.yaml') }} + restore-keys: | + ${{ runner.os }}-pre-commit- + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install pre-commit + + - name: Run pre-commit + run: | + pre-commit run --all-files diff --git a/.gitignore b/.gitignore index 432d4ed..fcf2576 100644 --- a/.gitignore +++ b/.gitignore @@ -18,4 +18,4 @@ *.env* *zip optimization_results/*.pt -*.tar.gz \ No newline at end of file +*.tar.gz diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index c85a29c..dcf0c30 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -5,12 +5,19 @@ repos: - id: trailing-whitespace - id: end-of-file-fixer - id: check-yaml + exclude: conda-recipe/meta.yaml + + - repo: https://github.com/psf/black + rev: 24.1.1 + hooks: + - id: black + language_version: python3 - repo: https://github.com/PyCQA/flake8 rev: 6.1.0 hooks: - id: flake8 - args: [--max-line-length=1210, --exclude=__init__.py] + args: [--max-line-length=110, --extend-ignore=E203, --exclude=__init__.py] - repo: https://github.com/PyCQA/pydocstyle rev: 6.3.0 diff --git a/LICENSE b/LICENSE index b787702..9f3432a 100644 --- a/LICENSE +++ b/LICENSE @@ -6,4 +6,4 @@ Permission is hereby granted, free of charge, to any person obtaining a copy of The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. -THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. \ No newline at end of file +THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/conda-builds/index.html b/conda-builds/index.html index 7e53695..09caefa 100644 --- a/conda-builds/index.html +++ b/conda-builds/index.html @@ -87,4 +87,4 @@

RSS Feed   c
Updated: 2025-08-08 18:12:21 +0000 - Files: 1
- \ No newline at end of file + diff --git a/conda-builds/linux-64/current_repodata.json b/conda-builds/linux-64/current_repodata.json index cc4126e..1e0abec 100644 --- a/conda-builds/linux-64/current_repodata.json +++ b/conda-builds/linux-64/current_repodata.json @@ -1 +1 @@ -{"info":{"subdir":"linux-64"},"packages":{},"packages.conda":{},"removed":[],"repodata_version":1} \ No newline at end of file +{"info":{"subdir":"linux-64"},"packages":{},"packages.conda":{},"removed":[],"repodata_version":1} diff --git a/conda-builds/linux-64/index.html b/conda-builds/linux-64/index.html index 0536ebf..092d575 100644 --- a/conda-builds/linux-64/index.html +++ b/conda-builds/linux-64/index.html @@ -55,4 +55,4 @@

conda-builds/linux-64

Updated: 2025-08-08 18:12:21 +0000 - Files: 0
- \ No newline at end of file + diff --git a/conda-builds/linux-64/repodata.json b/conda-builds/linux-64/repodata.json index cc4126e..1e0abec 100644 --- a/conda-builds/linux-64/repodata.json +++ b/conda-builds/linux-64/repodata.json @@ -1 +1 @@ -{"info":{"subdir":"linux-64"},"packages":{},"packages.conda":{},"removed":[],"repodata_version":1} \ No newline at end of file +{"info":{"subdir":"linux-64"},"packages":{},"packages.conda":{},"removed":[],"repodata_version":1} diff --git a/conda-builds/linux-64/repodata_from_packages.json b/conda-builds/linux-64/repodata_from_packages.json index cc4126e..1e0abec 100644 --- a/conda-builds/linux-64/repodata_from_packages.json +++ b/conda-builds/linux-64/repodata_from_packages.json @@ -1 +1 @@ -{"info":{"subdir":"linux-64"},"packages":{},"packages.conda":{},"removed":[],"repodata_version":1} \ No newline at end of file +{"info":{"subdir":"linux-64"},"packages":{},"packages.conda":{},"removed":[],"repodata_version":1} diff --git a/conda-builds/noarch/current_repodata.json b/conda-builds/noarch/current_repodata.json index e7d346d..152e88d 100644 --- a/conda-builds/noarch/current_repodata.json +++ b/conda-builds/noarch/current_repodata.json @@ -1 +1 @@ -{"info":{"subdir":"noarch"},"packages":{},"packages.conda":{"tarts-0.0.0a4-py_0.conda":{"build":"py_0","build_number":0,"depends":["astropy","gitpython","matplotlib","numpy","pandas","python >=3.8","pytorch","pytorch-lightning","pyyaml","scipy","torchvision"],"legacy_bz2_md5":null,"license":"MIT","md5":"9298ea53404a4506f51c142b987e2d9e","name":"tarts","noarch":"python","sha256":"e34e7b75de0295681debce2d9c2ba90490d64e24ef561c5cec756af63ee88c94","size":53097,"subdir":"noarch","timestamp":1754676677282,"version":"0.0.0a4"}},"removed":[],"repodata_version":1} \ No newline at end of file +{"info":{"subdir":"noarch"},"packages":{},"packages.conda":{"tarts-0.0.0a4-py_0.conda":{"build":"py_0","build_number":0,"depends":["astropy","gitpython","matplotlib","numpy","pandas","python >=3.8","pytorch","pytorch-lightning","pyyaml","scipy","torchvision"],"legacy_bz2_md5":null,"license":"MIT","md5":"9298ea53404a4506f51c142b987e2d9e","name":"tarts","noarch":"python","sha256":"e34e7b75de0295681debce2d9c2ba90490d64e24ef561c5cec756af63ee88c94","size":53097,"subdir":"noarch","timestamp":1754676677282,"version":"0.0.0a4"}},"removed":[],"repodata_version":1} diff --git a/conda-builds/noarch/index.html b/conda-builds/noarch/index.html index 93383eb..9c0c38a 100644 --- a/conda-builds/noarch/index.html +++ b/conda-builds/noarch/index.html @@ -67,4 +67,4 @@

conda-builds/noarch

Updated: 2025-08-08 18:12:21 +0000 - Files: 2
- \ No newline at end of file + diff --git a/conda-builds/noarch/repodata.json b/conda-builds/noarch/repodata.json index d6f2f50..6891dc5 100644 --- a/conda-builds/noarch/repodata.json +++ b/conda-builds/noarch/repodata.json @@ -1 +1 @@ -{"info":{"subdir":"noarch"},"packages":{},"packages.conda":{"tarts-0.0.0a3-py_0.conda":{"build":"py_0","build_number":0,"depends":["astropy","gitpython","matplotlib","numpy","pandas","python >=3.8","pytorch","pytorch-lightning","pyyaml","scipy","torchvision"],"license":"MIT","md5":"6d4a3f28f0376ebaca498436968eab42","name":"tarts","noarch":"python","sha256":"edaaecbe4a367c4fb3000e6edda71cfab453b9ae55b4de5f35edd351c1660d6d","size":52685,"subdir":"noarch","timestamp":1754676221415,"version":"0.0.0a3"},"tarts-0.0.0a4-py_0.conda":{"build":"py_0","build_number":0,"depends":["astropy","gitpython","matplotlib","numpy","pandas","python >=3.8","pytorch","pytorch-lightning","pyyaml","scipy","torchvision"],"license":"MIT","md5":"9298ea53404a4506f51c142b987e2d9e","name":"tarts","noarch":"python","sha256":"e34e7b75de0295681debce2d9c2ba90490d64e24ef561c5cec756af63ee88c94","size":53097,"subdir":"noarch","timestamp":1754676677282,"version":"0.0.0a4"}},"removed":[],"repodata_version":1} \ No newline at end of file +{"info":{"subdir":"noarch"},"packages":{},"packages.conda":{"tarts-0.0.0a3-py_0.conda":{"build":"py_0","build_number":0,"depends":["astropy","gitpython","matplotlib","numpy","pandas","python >=3.8","pytorch","pytorch-lightning","pyyaml","scipy","torchvision"],"license":"MIT","md5":"6d4a3f28f0376ebaca498436968eab42","name":"tarts","noarch":"python","sha256":"edaaecbe4a367c4fb3000e6edda71cfab453b9ae55b4de5f35edd351c1660d6d","size":52685,"subdir":"noarch","timestamp":1754676221415,"version":"0.0.0a3"},"tarts-0.0.0a4-py_0.conda":{"build":"py_0","build_number":0,"depends":["astropy","gitpython","matplotlib","numpy","pandas","python >=3.8","pytorch","pytorch-lightning","pyyaml","scipy","torchvision"],"license":"MIT","md5":"9298ea53404a4506f51c142b987e2d9e","name":"tarts","noarch":"python","sha256":"e34e7b75de0295681debce2d9c2ba90490d64e24ef561c5cec756af63ee88c94","size":53097,"subdir":"noarch","timestamp":1754676677282,"version":"0.0.0a4"}},"removed":[],"repodata_version":1} diff --git a/conda-builds/noarch/repodata_from_packages.json b/conda-builds/noarch/repodata_from_packages.json index d6f2f50..6891dc5 100644 --- a/conda-builds/noarch/repodata_from_packages.json +++ b/conda-builds/noarch/repodata_from_packages.json @@ -1 +1 @@ -{"info":{"subdir":"noarch"},"packages":{},"packages.conda":{"tarts-0.0.0a3-py_0.conda":{"build":"py_0","build_number":0,"depends":["astropy","gitpython","matplotlib","numpy","pandas","python >=3.8","pytorch","pytorch-lightning","pyyaml","scipy","torchvision"],"license":"MIT","md5":"6d4a3f28f0376ebaca498436968eab42","name":"tarts","noarch":"python","sha256":"edaaecbe4a367c4fb3000e6edda71cfab453b9ae55b4de5f35edd351c1660d6d","size":52685,"subdir":"noarch","timestamp":1754676221415,"version":"0.0.0a3"},"tarts-0.0.0a4-py_0.conda":{"build":"py_0","build_number":0,"depends":["astropy","gitpython","matplotlib","numpy","pandas","python >=3.8","pytorch","pytorch-lightning","pyyaml","scipy","torchvision"],"license":"MIT","md5":"9298ea53404a4506f51c142b987e2d9e","name":"tarts","noarch":"python","sha256":"e34e7b75de0295681debce2d9c2ba90490d64e24ef561c5cec756af63ee88c94","size":53097,"subdir":"noarch","timestamp":1754676677282,"version":"0.0.0a4"}},"removed":[],"repodata_version":1} \ No newline at end of file +{"info":{"subdir":"noarch"},"packages":{},"packages.conda":{"tarts-0.0.0a3-py_0.conda":{"build":"py_0","build_number":0,"depends":["astropy","gitpython","matplotlib","numpy","pandas","python >=3.8","pytorch","pytorch-lightning","pyyaml","scipy","torchvision"],"license":"MIT","md5":"6d4a3f28f0376ebaca498436968eab42","name":"tarts","noarch":"python","sha256":"edaaecbe4a367c4fb3000e6edda71cfab453b9ae55b4de5f35edd351c1660d6d","size":52685,"subdir":"noarch","timestamp":1754676221415,"version":"0.0.0a3"},"tarts-0.0.0a4-py_0.conda":{"build":"py_0","build_number":0,"depends":["astropy","gitpython","matplotlib","numpy","pandas","python >=3.8","pytorch","pytorch-lightning","pyyaml","scipy","torchvision"],"license":"MIT","md5":"9298ea53404a4506f51c142b987e2d9e","name":"tarts","noarch":"python","sha256":"e34e7b75de0295681debce2d9c2ba90490d64e24ef561c5cec756af63ee88c94","size":53097,"subdir":"noarch","timestamp":1754676677282,"version":"0.0.0a4"}},"removed":[],"repodata_version":1} diff --git a/conda-recipe/build_output/index.html b/conda-recipe/build_output/index.html index 2d71ab9..ea6d1c5 100644 --- a/conda-recipe/build_output/index.html +++ b/conda-recipe/build_output/index.html @@ -87,4 +87,4 @@

RSS Feed   c
Updated: 2025-08-11 22:44:13 +0000 - Files: 1
- \ No newline at end of file + diff --git a/conda-recipe/build_output/linux-64/current_repodata.json b/conda-recipe/build_output/linux-64/current_repodata.json index cc4126e..1e0abec 100644 --- a/conda-recipe/build_output/linux-64/current_repodata.json +++ b/conda-recipe/build_output/linux-64/current_repodata.json @@ -1 +1 @@ -{"info":{"subdir":"linux-64"},"packages":{},"packages.conda":{},"removed":[],"repodata_version":1} \ No newline at end of file +{"info":{"subdir":"linux-64"},"packages":{},"packages.conda":{},"removed":[],"repodata_version":1} diff --git a/conda-recipe/build_output/linux-64/index.html b/conda-recipe/build_output/linux-64/index.html index 358e436..90d695b 100644 --- a/conda-recipe/build_output/linux-64/index.html +++ b/conda-recipe/build_output/linux-64/index.html @@ -55,4 +55,4 @@

build_output/linux-64

Updated: 2025-08-11 22:44:13 +0000 - Files: 0
- \ No newline at end of file + diff --git a/conda-recipe/build_output/linux-64/repodata.json b/conda-recipe/build_output/linux-64/repodata.json index cc4126e..1e0abec 100644 --- a/conda-recipe/build_output/linux-64/repodata.json +++ b/conda-recipe/build_output/linux-64/repodata.json @@ -1 +1 @@ -{"info":{"subdir":"linux-64"},"packages":{},"packages.conda":{},"removed":[],"repodata_version":1} \ No newline at end of file +{"info":{"subdir":"linux-64"},"packages":{},"packages.conda":{},"removed":[],"repodata_version":1} diff --git a/conda-recipe/build_output/linux-64/repodata_from_packages.json b/conda-recipe/build_output/linux-64/repodata_from_packages.json index cc4126e..1e0abec 100644 --- a/conda-recipe/build_output/linux-64/repodata_from_packages.json +++ b/conda-recipe/build_output/linux-64/repodata_from_packages.json @@ -1 +1 @@ -{"info":{"subdir":"linux-64"},"packages":{},"packages.conda":{},"removed":[],"repodata_version":1} \ No newline at end of file +{"info":{"subdir":"linux-64"},"packages":{},"packages.conda":{},"removed":[],"repodata_version":1} diff --git a/conda-recipe/build_output/noarch/current_repodata.json b/conda-recipe/build_output/noarch/current_repodata.json index d0d4d27..1c90bfa 100644 --- a/conda-recipe/build_output/noarch/current_repodata.json +++ b/conda-recipe/build_output/noarch/current_repodata.json @@ -1 +1 @@ -{"info":{"subdir":"noarch"},"packages":{},"packages.conda":{"tarts-0.0.0a7-pyh42b148c_0.conda":{"build":"pyh42b148c_0","build_number":0,"depends":["astropy-base","gitpython","matplotlib-base","numpy","pandas","python >=3.10","pytorch","pytorch-lightning","pyyaml","scipy","torchvision"],"legacy_bz2_md5":null,"license":"MIT","md5":"430f5b73dac74592b7f0b108954cc710","name":"tarts","noarch":"python","sha256":"5d64b0a9c23e57371861a68acd35d4ca711dc9b5609431d4768742a2bdbeb058","size":179918,"subdir":"noarch","timestamp":1754952245070,"version":"0.0.0a7"}},"removed":[],"repodata_version":1} \ No newline at end of file +{"info":{"subdir":"noarch"},"packages":{},"packages.conda":{"tarts-0.0.0a7-pyh42b148c_0.conda":{"build":"pyh42b148c_0","build_number":0,"depends":["astropy-base","gitpython","matplotlib-base","numpy","pandas","python >=3.10","pytorch","pytorch-lightning","pyyaml","scipy","torchvision"],"legacy_bz2_md5":null,"license":"MIT","md5":"430f5b73dac74592b7f0b108954cc710","name":"tarts","noarch":"python","sha256":"5d64b0a9c23e57371861a68acd35d4ca711dc9b5609431d4768742a2bdbeb058","size":179918,"subdir":"noarch","timestamp":1754952245070,"version":"0.0.0a7"}},"removed":[],"repodata_version":1} diff --git a/conda-recipe/build_output/noarch/index.html b/conda-recipe/build_output/noarch/index.html index e771a77..49eed8f 100644 --- a/conda-recipe/build_output/noarch/index.html +++ b/conda-recipe/build_output/noarch/index.html @@ -67,4 +67,4 @@

build_output/noarch

Updated: 2025-08-11 22:44:13 +0000 - Files: 2
- \ No newline at end of file + diff --git a/conda-recipe/build_output/noarch/repodata.json b/conda-recipe/build_output/noarch/repodata.json index ddb3041..478efb5 100644 --- a/conda-recipe/build_output/noarch/repodata.json +++ b/conda-recipe/build_output/noarch/repodata.json @@ -1 +1 @@ -{"info":{"subdir":"noarch"},"packages":{},"packages.conda":{"tarts-0.0.0a4-py_0.conda":{"build":"py_0","build_number":0,"depends":["astropy-base","gitpython","matplotlib-base","numpy","pandas","python","pytorch","pytorch-lightning","pyyaml","scipy","torchvision"],"license":"MIT","md5":"341178fa6be45282f9f37d4fc47594d4","name":"tarts","noarch":"python","sha256":"0968c4156d2383035006cec6f1ff5aeef0f26702f83d72203caf315635907129","size":59664,"subdir":"noarch","timestamp":1754885312974,"version":"0.0.0a4"},"tarts-0.0.0a7-pyh42b148c_0.conda":{"build":"pyh42b148c_0","build_number":0,"depends":["astropy-base","gitpython","matplotlib-base","numpy","pandas","python 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+{"info":{"subdir":"noarch"},"packages":{},"packages.conda":{"tarts-0.0.0a4-py_0.conda":{"build":"py_0","build_number":0,"depends":["astropy-base","gitpython","matplotlib-base","numpy","pandas","python","pytorch","pytorch-lightning","pyyaml","scipy","torchvision"],"license":"MIT","md5":"341178fa6be45282f9f37d4fc47594d4","name":"tarts","noarch":"python","sha256":"0968c4156d2383035006cec6f1ff5aeef0f26702f83d72203caf315635907129","size":59664,"subdir":"noarch","timestamp":1754885312974,"version":"0.0.0a4"},"tarts-0.0.0a7-pyh42b148c_0.conda":{"build":"pyh42b148c_0","build_number":0,"depends":["astropy-base","gitpython","matplotlib-base","numpy","pandas","python 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-{"info":{"subdir":"noarch"},"packages":{},"packages.conda":{"tarts-0.0.0a4-py_0.conda":{"build":"py_0","build_number":0,"depends":["astropy-base","gitpython","matplotlib-base","numpy","pandas","python","pytorch","pytorch-lightning","pyyaml","scipy","torchvision"],"license":"MIT","md5":"341178fa6be45282f9f37d4fc47594d4","name":"tarts","noarch":"python","sha256":"0968c4156d2383035006cec6f1ff5aeef0f26702f83d72203caf315635907129","size":59664,"subdir":"noarch","timestamp":1754885312974,"version":"0.0.0a4"},"tarts-0.0.0a7-pyh42b148c_0.conda":{"build":"pyh42b148c_0","build_number":0,"depends":["astropy-base","gitpython","matplotlib-base","numpy","pandas","python >=3.10","pytorch","pytorch-lightning","pyyaml","scipy","torchvision"],"license":"MIT","md5":"430f5b73dac74592b7f0b108954cc710","name":"tarts","noarch":"python","sha256":"5d64b0a9c23e57371861a68acd35d4ca711dc9b5609431d4768742a2bdbeb058","size":179918,"subdir":"noarch","timestamp":1754952245070,"version":"0.0.0a7"}},"removed":[],"repodata_version":1} \ No newline at end of file +{"info":{"subdir":"noarch"},"packages":{},"packages.conda":{"tarts-0.0.0a4-py_0.conda":{"build":"py_0","build_number":0,"depends":["astropy-base","gitpython","matplotlib-base","numpy","pandas","python","pytorch","pytorch-lightning","pyyaml","scipy","torchvision"],"license":"MIT","md5":"341178fa6be45282f9f37d4fc47594d4","name":"tarts","noarch":"python","sha256":"0968c4156d2383035006cec6f1ff5aeef0f26702f83d72203caf315635907129","size":59664,"subdir":"noarch","timestamp":1754885312974,"version":"0.0.0a4"},"tarts-0.0.0a7-pyh42b148c_0.conda":{"build":"pyh42b148c_0","build_number":0,"depends":["astropy-base","gitpython","matplotlib-base","numpy","pandas","python >=3.10","pytorch","pytorch-lightning","pyyaml","scipy","torchvision"],"license":"MIT","md5":"430f5b73dac74592b7f0b108954cc710","name":"tarts","noarch":"python","sha256":"5d64b0a9c23e57371861a68acd35d4ca711dc9b5609431d4768742a2bdbeb058","size":179918,"subdir":"noarch","timestamp":1754952245070,"version":"0.0.0a7"}},"removed":[],"repodata_version":1} diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..b5e9c9d --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,57 @@ +[tool.black] +line-length = 110 +target-version = ['py38', 'py39', 'py310', 'py311'] +include = '\.pyi?$' +extend-exclude = ''' +/( + # directories + \.eggs + | \.git + | \.hg + | \.mypy_cache + | \.tox + | \.venv + | build + | dist +)/ +''' + +[tool.mypy] +python_version = "3.10" +warn_return_any = true +warn_unused_configs = true +disallow_untyped_defs = false +disallow_incomplete_defs = false +check_untyped_defs = true +disallow_untyped_decorators = false +no_implicit_optional = true +warn_redundant_casts = true +warn_unused_ignores = true +warn_no_return = true +warn_unreachable = true +strict_equality = true +show_error_codes = true + +# Exclude certain directories from type checking +exclude = [ + "conda-recipe/", + "conda-builds/", + "build/", + "dist/", + ".*egg-info/", +] + +# Ignore missing imports for optional dependencies +[[tool.mypy.overrides]] +module = [ + "pytorch_lightning.*", + "torch.*", + "timm.*", + "lsst.*", + "astropy.*", + "git.*", + "training.*", + "joblib.*", + "torchvision.*", +] +ignore_missing_imports = true diff --git a/python/tarts/KERNEL.py b/python/tarts/KERNEL.py index 889c670..cc83671 100644 --- a/python/tarts/KERNEL.py +++ b/python/tarts/KERNEL.py @@ -1,251 +1,82 @@ """Donut Kernel used (typically) for classical donut alignment. -Here is the donut kernel explicitly written as a pytorch array such that -it can be compiled with the model at runtime. +This module generates the donut kernel as a PyTorch tensor at runtime, +eliminating the need for a large hardcoded array. """ + +import numpy as np import torch -CUTOUT = torch.tensor( - [ - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], - 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] -) + +def paint_circle(arr, center, radius, value=1, fill=True): + """Paint a circle onto an existing 2D NumPy array. + + Parameters + ---------- + arr : np.ndarray + 2D array to paint onto (modified in place). + center : tuple of floats + (y, x) coordinates of the circle center. + radius : float + Radius of the circle. + value : float or int, optional + Value to paint (default = 1). + fill : bool, optional + If True, fill the circle. If False, draw only the outline. + + Returns + ------- + arr : np.ndarray + The same array, modified. + """ + y, x = np.ogrid[: arr.shape[0], : arr.shape[1]] + dist = np.sqrt((x - center[1]) ** 2 + (y - center[0]) ** 2) + + if fill: + mask = dist <= radius + else: + mask = (dist >= radius - 0.5) & (dist <= radius + 0.5) + + arr[mask] = value + return arr + + +def generate_donut_kernel(size=(240, 240), outer_radius=68, inner_radius=37, center=None): + """Generate a donut kernel pattern. + + Parameters + ---------- + size : tuple of int, optional + Size of the kernel array (height, width). Default is (240, 240). + outer_radius : float, optional + Radius of the outer circle. Default is 68. + inner_radius : float, optional + Radius of the inner circle (hole). Default is 37. + center : tuple of floats, optional + (y, x) coordinates of the center. If None, uses the center of the array. + Default is None. + + Returns + ------- + torch.Tensor + Donut kernel as a PyTorch tensor of shape (height, width). + """ + if center is None: + center = (size[0] // 2, size[1] // 2) + + # Create base array + cutout = np.zeros(size, dtype=np.float32) + + # Paint outer circle (filled) + cutout = paint_circle(cutout, center=center, radius=outer_radius, value=1, fill=True) + + # Paint inner circle (hole) to create donut shape + cutout = paint_circle(cutout, center=center, radius=inner_radius, value=0, fill=True) + + # Convert to PyTorch tensor + return torch.tensor(cutout, dtype=torch.float32) + + +# Generate the donut kernel at module import time +# This matches the original hardcoded kernel dimensions and pattern +CUTOUT = generate_donut_kernel(size=(240, 240), outer_radius=68, inner_radius=37, center=(121, 121)) diff --git a/python/tarts/NeuralActiveOpticsSys.py b/python/tarts/NeuralActiveOpticsSys.py index 583897a..0250629 100644 --- a/python/tarts/NeuralActiveOpticsSys.py +++ b/python/tarts/NeuralActiveOpticsSys.py @@ -1,35 +1,61 @@ """Neural network to predict zernike coefficients from donut images and positions.""" -from .utils import (batched_crop, get_centers, - convert_zernikes_deploy, single_conv, - shift_offcenter, - ) -import torch -from torch import nn -from .lightning_wavenet import WaveNetSystem -from .lightning_alignnet import AlignNetSystem -from .aggregatornet import AggregatorNet -import yaml -from torch import vmap -import pytorch_lightning as pl -from torch.optim.lr_scheduler import ReduceLROnPlateau -import torch.nn.functional as F_loss -import torchvision.transforms.functional as F + +# Standard library imports import copy +import logging import os +from typing import Any, Dict, List + +# Third-party imports import joblib -from lsst.obs.lsst import LsstCam -from lsst.obs.lsst.cameraTransforms import LsstCameraTransforms +import pytorch_lightning as pl +import torch +import torch.nn.functional as F_loss +import torchvision.transforms.functional as F +import yaml from lsst.ip.isr import AssembleCcdTask from lsst.meas.algorithms import subtractBackground -from .utils import MAP_DETECTOR_TO_NUMBER +from lsst.obs.lsst import LsstCam +from lsst.obs.lsst.cameraTransforms import LsstCameraTransforms +from lsst.pex.exceptions import LengthError +from torch import nn, vmap +from torch.optim.lr_scheduler import ReduceLROnPlateau + +# Local/application imports +from .aggregatornet import AggregatorNet +from .constants import MAP_DETECTOR_TO_NUMBER +from .lightning_alignnet import AlignNetSystem +from .lightning_wavenet import WaveNetSystem +from .utils import ( + batched_crop, + convert_zernikes_deploy, + get_centers, + single_conv, + shift_offcenter, +) + +logger = logging.getLogger(__name__) class NeuralActiveOpticsSys(pl.LightningModule): """Transfer learning driven WaveNet.""" - def __init__(self, dataset_params, wavenet_path=None, alignet_path=None, - aggregatornet_path=None, - lr=1e-3, final_layer=None, aggregator_on=True, pretrained=True, - compile_models=False, ood_model_path=None) -> None: + + def __init__( + self, + dataset_params, + wavenet_path=None, + alignet_path=None, + aggregatornet_path=None, + lr=1e-3, + final_layer=None, + aggregator_on=True, + pretrained=True, + compile_models=False, + ood_model_path=None, + kmin=None, + kmax=float("inf"), + sigma_frac=0.1, + ) -> None: """Initialize the Neural Active Optics System. Parameters @@ -60,73 +86,92 @@ def __init__(self, dataset_params, wavenet_path=None, alignet_path=None, ood_model_path : str, optional Path to OOD detection model (joblib file). If provided, OOD detection will be performed during inference and scores will be stored in internal metadata. Defaults to None. + kmin : float, optional + Minimum frequency magnitude for frequency filtering. If None, frequency filtering is disabled. + If set, applies frequency filter to each cropped image before passing to WaveNet. + Defaults to None. + kmax : float, optional + Maximum frequency magnitude for frequency filtering. Only used if kmin is not None. + Defaults to float('inf'). + sigma_frac : float, optional + Fractional Gaussian width for smooth filter edges in frequency filtering. + Only used if kmin is not None. Defaults to 0.1. """ - super(NeuralActiveOpticsSys, self).__init__() + super().__init__() self.save_hyperparameters() self.device_val = torch.device("cuda" if torch.cuda.is_available() else "cpu") + # Initialize frequency filtering parameters + self.kmin = kmin + self.kmax = kmax + self.sigma_frac = sigma_frac + + # Initialize attributes that may be set during forward pass + # These can be lists or tensors depending on context + self.fx: List[torch.Tensor] | torch.Tensor = [] + self.fy: List[torch.Tensor] | torch.Tensor = [] + self.intra: List[torch.Tensor] | torch.Tensor = [] + self.band: List[torch.Tensor] | torch.Tensor = [] + self.SNR: List[torch.Tensor] | torch.Tensor = [] + self.centers: List[torch.Tensor] | torch.Tensor = [] + self.total_zernikes: torch.Tensor | None = None + self.cropped_image: torch.Tensor | None = None + self.ood_scores: torch.Tensor | None = None + # Load OOD detection model if path is provided self.ood_model = None self.ood_mean = None if ood_model_path is not None and os.path.exists(ood_model_path): - try: - print(f"Loading OOD detection model from {ood_model_path}...") - ood_data = joblib.load(ood_model_path) - self.ood_model = ood_data['cov_model'] - self.ood_mean = torch.tensor(ood_data['mean'], device=self.device_val, dtype=torch.float32) - print("✅ OOD detection model loaded successfully") - except Exception as e: - print(f"⚠️ Failed to load OOD model: {e}") - print(" Continuing without OOD detection...") + logger.info(f"Loading OOD detection model from {ood_model_path}...") + ood_data = joblib.load(ood_model_path) + self.ood_model = ood_data["cov_model"] + self.ood_mean = torch.tensor(ood_data["mean"], device=self.device_val, dtype=torch.float32) + logger.info("OOD detection model loaded successfully") elif ood_model_path is not None: - print(f"⚠️ OOD model path provided but file not found: {ood_model_path}") - print(" Continuing without OOD detection...") + logger.warning(f"OOD model path provided but file not found: {ood_model_path}") + logger.info("Continuing without OOD detection...") # Load parameters from YAML file once - with open(dataset_params, 'r') as yaml_file: + with open(dataset_params, "r") as yaml_file: params = yaml.safe_load(yaml_file) if wavenet_path is None: self.wavenet_model = WaveNetSystem(pretrained=pretrained).to(self.device_val) else: - # Always use checkpoint loading - the pretrained parameter doesn't matter when loading from checkpoint + # Always use checkpoint loading - the pretrained parameter doesn't matter + # when loading from checkpoint self.wavenet_model = WaveNetSystem.load_from_checkpoint( - wavenet_path, - map_location=str(self.device_val) + wavenet_path, map_location=str(self.device_val), strict=False ).to(self.device_val) if alignet_path is None: self.alignnet_model = AlignNetSystem(pretrained=pretrained).to(self.device_val) else: - try: - # Always use checkpoint loading - the pretrained parameter doesn't matter when loading from checkpoint - self.alignnet_model = AlignNetSystem.load_from_checkpoint( - alignet_path, - map_location=str(self.device_val) - ).to(self.device_val) - print("✅ Loaded AlignNet regular checkpoint") - except Exception: - print("⚠️ Regular AlignNet loading failed") - print("🔄 Trying to load AlignNet as QAT-trained model...") - from training.load_qat_model import load_qat_trained_model - self.alignnet_model = load_qat_trained_model(alignet_path, device=str(self.device_val)).to(self.device_val) - print("✅ Loaded AlignNet QAT-trained model") + # Always use checkpoint loading - the pretrained parameter doesn't matter + # when loading from checkpoint + self.alignnet_model = AlignNetSystem.load_from_checkpoint( + alignet_path, map_location=str(self.device_val), strict=False + ).to(self.device_val) self.max_seq_length = params["max_seq_len"] self.aggregator_on = aggregator_on - print(self.device_val) + logger.debug(f"Using device: {self.device_val}") if aggregatornet_path is None: d_model = params["aggregator_model"]["d_model"] nhead = params["aggregator_model"]["nhead"] num_layers = params["aggregator_model"]["num_layers"] dim_feedforward = params["aggregator_model"]["dim_feedforward"] - self.aggregatornet_model = AggregatorNet(d_model=d_model, - nhead=nhead, - num_layers=num_layers, - dim_feedforward=dim_feedforward, - max_seq_length=self.max_seq_length).to(self.device_val) + self.aggregatornet_model = AggregatorNet( + d_model=d_model, + nhead=nhead, + num_layers=num_layers, + dim_feedforward=dim_feedforward, + max_seq_length=self.max_seq_length, + ).to(self.device_val) else: - self.aggregatornet_model = AggregatorNet.load_from_checkpoint(aggregatornet_path).to(self.device_val) + self.aggregatornet_model = AggregatorNet.load_from_checkpoint( + aggregatornet_path, strict=False + ).to(self.device_val) if final_layer is not None: layers = [ @@ -138,44 +183,56 @@ def __init__(self, dataset_params, wavenet_path=None, alignet_path=None, self.final_layer = self.identity # Use already loaded params - self.refinements = params['refinements'] - self.CROP_SIZE = params['CROP_SIZE'] - self.mm_pix = params['mm_pix'] - self.deg_per_pix = params['deg_per_pix'] - self.alpha = params['alpha'] - self.SCALE = params['adjustment_AlignNet'] + self.refinements = params["refinements"] + self.CROP_SIZE = params["CROP_SIZE"] + self.mm_pix = params["mm_pix"] + self.deg_per_pix = params["deg_per_pix"] + self.alpha = params["alpha"] + self.SCALE = params["adjustment_AlignNet"] + self.num_zernikes = len(params["noll_zk"]) # Apply torch.compile to submodels if requested if compile_models: # Determine compilation backend based on device - if self.device_val.type == 'cpu': + if self.device_val.type == "cpu": compile_backend = "inductor" - print("🔧 Compiling submodels with torch.compile (CPU backend: inductor)...") + logger.info("Compiling submodels with torch.compile (CPU backend: inductor)...") else: compile_backend = None # Use default backend for GPU - print("🔧 Compiling submodels with torch.compile (GPU backend: default)...") + logger.info("Compiling submodels with torch.compile (GPU backend: default)...") try: - self.wavenet_model = torch.compile(self.wavenet_model, backend=compile_backend) - print(f"✅ WaveNet compiled with backend: {compile_backend or 'default'}") - except Exception as e: - print(f"⚠️ WaveNet compilation failed: {e}") + if compile_backend is not None: + self.wavenet_model = torch.compile(self.wavenet_model, backend=compile_backend) + else: + self.wavenet_model = torch.compile(self.wavenet_model) + logger.info(f"WaveNet compiled with backend: {compile_backend or 'default'}") + except (RuntimeError, TypeError, AttributeError) as e: + logger.warning(f"WaveNet compilation failed: {e}") try: - self.alignnet_model = torch.compile(self.alignnet_model, backend=compile_backend) - print(f"✅ AlignNet compiled with backend: {compile_backend or 'default'}") - except Exception as e: - print(f"⚠️ AlignNet compilation failed: {e}") + if compile_backend is not None: + self.alignnet_model = torch.compile(self.alignnet_model, backend=compile_backend) + else: + self.alignnet_model = torch.compile(self.alignnet_model) + logger.info(f"AlignNet compiled with backend: {compile_backend or 'default'}") + except (RuntimeError, TypeError, AttributeError) as e: + logger.warning(f"AlignNet compilation failed: {e}") try: - self.aggregatornet_model = torch.compile(self.aggregatornet_model, backend=compile_backend) - print(f"✅ AggregatorNet compiled with backend: {compile_backend or 'default'}") - except Exception as e: - print(f"⚠️ AggregatorNet compilation failed: {e}") + if compile_backend is not None: + self.aggregatornet_model = torch.compile( + self.aggregatornet_model, backend=compile_backend + ) + else: + self.aggregatornet_model = torch.compile(self.aggregatornet_model) + logger.info(f"AggregatorNet compiled with backend: {compile_backend or 'default'}") + except (RuntimeError, TypeError, AttributeError) as e: + logger.warning(f"AggregatorNet compilation failed: {e}") - print("🎉 Model compilation completed!") + logger.info("Model compilation completed!") else: - print("ℹ️ Model compilation disabled (compile_models=False)") + logger.info("Model compilation disabled (compile_models=False)") def compile_submodels(self): """Apply torch.compile to all submodels for faster inference. @@ -185,32 +242,41 @@ def compile_submodels(self): Automatically selects the appropriate backend based on the device (CPU: inductor, GPU: default). """ # Determine compilation backend based on device - if self.device_val.type == 'cpu': + if self.device_val.type == "cpu": compile_backend = "inductor" - print("🔧 Compiling submodels with torch.compile (CPU backend: inductor)...") + logger.info("Compiling submodels with torch.compile (CPU backend: inductor)...") else: compile_backend = None # Use default backend for GPU - print("🔧 Compiling submodels with torch.compile (GPU backend: default)...") + logger.info("Compiling submodels with torch.compile (GPU backend: default)...") try: - self.wavenet_model = torch.compile(self.wavenet_model, backend=compile_backend) - print(f"✅ WaveNet compiled with backend: {compile_backend or 'default'}") - except Exception as e: - print(f"⚠️ WaveNet compilation failed: {e}") + if compile_backend is not None: + self.wavenet_model = torch.compile(self.wavenet_model, backend=compile_backend) + else: + self.wavenet_model = torch.compile(self.wavenet_model) + logger.info(f"WaveNet compiled with backend: {compile_backend or 'default'}") + except (RuntimeError, TypeError, AttributeError) as e: + logger.warning(f"WaveNet compilation failed: {e}") try: - self.alignnet_model = torch.compile(self.alignnet_model, backend=compile_backend) - print(f"✅ AlignNet compiled with backend: {compile_backend or 'default'}") - except Exception as e: - print(f"⚠️ AlignNet compilation failed: {e}") + if compile_backend is not None: + self.alignnet_model = torch.compile(self.alignnet_model, backend=compile_backend) + else: + self.alignnet_model = torch.compile(self.alignnet_model) + logger.info(f"AlignNet compiled with backend: {compile_backend or 'default'}") + except (RuntimeError, TypeError, AttributeError) as e: + logger.warning(f"AlignNet compilation failed: {e}") try: - self.aggregatornet_model = torch.compile(self.aggregatornet_model, backend=compile_backend) - print(f"✅ AggregatorNet compiled with backend: {compile_backend or 'default'}") - except Exception as e: - print(f"⚠️ AggregatorNet compilation failed: {e}") + if compile_backend is not None: + self.aggregatornet_model = torch.compile(self.aggregatornet_model, backend=compile_backend) + else: + self.aggregatornet_model = torch.compile(self.aggregatornet_model) + logger.info(f"AggregatorNet compiled with backend: {compile_backend or 'default'}") + except (RuntimeError, TypeError, AttributeError) as e: + logger.warning(f"AggregatorNet compilation failed: {e}") - print("🎉 Model compilation completed!") + logger.info("Model compilation completed!") def get_internal_data(self): """Retrieve all internal data as a list of dictionaries. @@ -230,35 +296,142 @@ def get_internal_data(self): -------- list of dict List of dictionaries, each containing the data for one donut. - Returns empty list if no data is available. + Returns list with default values if no data is available. """ - internal_data = [] + internal_data: List[Dict[str, Any]] = [] # Check if we have valid data (not NaN-filled) - if hasattr(self, 'fx') and len(self.fx) > 0 and not torch.isnan(self.fx[0]): + if len(self.fx) > 0 and self.cropped_image is not None and self.total_zernikes is not None: num_donuts = len(self.fx) + # Ensure these are not None before indexing for i in range(num_donuts): data_dict = { - 'cropped_image': self.cropped_image[i].clone().detach(), - 'fx': self.fx[i].clone().detach(), - 'fy': self.fy[i].clone().detach(), - 'intra': self.intra[i].clone().detach(), - 'band': self.band[i].clone().detach(), - 'SNR': self.SNR[i].clone().detach(), - 'centers': self.centers[i].clone().detach(), - 'zernikes': self.total_zernikes[i].clone().detach() + "cropped_image": self.cropped_image[i].clone().detach(), + "fx": self.fx[i].clone().detach(), + "fy": self.fy[i].clone().detach(), + "intra": self.intra[i].clone().detach(), + "band": self.band[i].clone().detach(), + "SNR": self.SNR[i].clone().detach(), + "centers": self.centers[i].clone().detach(), + "zernikes": self.total_zernikes[i].clone().detach(), } # Add OOD score if available - if hasattr(self, 'ood_scores') and self.ood_scores is not None and i < len(self.ood_scores): - data_dict['ood_score'] = self.ood_scores[i].clone().detach() + if self.ood_scores is not None and i < len(self.ood_scores): + data_dict["ood_score"] = self.ood_scores[i].clone().detach() else: - data_dict['ood_score'] = None + data_dict["ood_score"] = torch.tensor([float("nan")]).clone().detach() internal_data.append(data_dict) - + else: + # Fill with default values when data is not available + data_dict = { + "cropped_image": torch.zeros((self.CROP_SIZE, self.CROP_SIZE), device=self.device_val) + .clone() + .detach(), + "fx": torch.tensor(0).clone().detach(), + "fy": torch.tensor(0).clone().detach(), + "intra": torch.tensor(0).clone().detach(), + "band": torch.tensor(0).clone().detach(), + "SNR": torch.tensor(0).clone().detach(), + "centers": torch.tensor([0, 0]).clone().detach(), + "zernikes": torch.full((self.num_zernikes,), float("nan"), device=self.device_val) + .clone() + .detach(), + "ood_score": torch.tensor([float("nan")]).clone().detach(), + } + internal_data.append(data_dict) return internal_data + def filter_image_by_frequency_torch(self, image, kmin=0, kmax=float("inf"), sigma_frac=0.1, device=None): + """Apply a smooth Gaussian-weighted frequency filter in Fourier space to a 2D image. + + Uses PyTorch for GPU acceleration. + + Parameters + ---------- + image : torch.Tensor or np.ndarray + 2D input image (real-valued). Shape: (H, W) + kmin, kmax : float + Minimum and maximum |k| (frequency magnitude) to keep. + sigma_frac : float, optional + Fractional Gaussian width for smooth filter edges. + Higher = softer transitions (default 0.1). + show : bool, optional + If True, visualize FFT magnitude, filter mask, and filtered image. + device : str or torch.device, optional + Device to run on ('cpu' or 'cuda'). Defaults to CUDA if available. + + Returns + ------- + filtered_image : torch.Tensor + The reconstructed real-space image after filtering. + weight : torch.Tensor + The Fourier-space weighting mask. + """ + # --- Setup device --- + if device is None: + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # --- Convert image to float tensor --- + if not torch.is_tensor(image): + image = torch.tensor(image, dtype=torch.float32) + image = image.to(device) + + # --- Compute 2D FFT --- + f = torch.fft.fft2(image) + fshift = torch.fft.fftshift(f) + + # --- Build frequency grids --- + ny, nx = image.shape + kx = torch.fft.fftshift(torch.fft.fftfreq(nx, device=device)) + ky = torch.fft.fftshift(torch.fft.fftfreq(ny, device=device)) + KX, KY = torch.meshgrid(kx, ky, indexing="xy") + K_mag = torch.sqrt(KX**2 + KY**2) + + # --- Smooth Gaussian-weighted filter --- + kmax_valid = K_mag.max() + kmax = min(kmax, kmax_valid.item()) + sigma = sigma_frac * (kmax - kmin) + sigma = max(sigma, 1e-6) # avoid division by zero + + low_edge = 1 / (1 + torch.exp(-(K_mag - kmin) / sigma)) + high_edge = 1 / (1 + torch.exp((K_mag - kmax) / sigma)) + weight = low_edge * high_edge + + # --- Apply filter --- + f_filtered = fshift * weight + + # --- Inverse FFT --- + reconstructed = torch.fft.ifft2(torch.fft.ifftshift(f_filtered)).real + return reconstructed, weight + + def filter_batch_images(self, images): + """Apply frequency filtering to a batch of 2D images. + + Parameters + ---------- + images : torch.Tensor + Batch of 2D images. Shape: (batch_size, height, width) + + Returns + ------- + filtered_images : torch.Tensor + Batch of filtered 2D images. Shape: (batch_size, height, width) + """ + if self.kmin is None: + # Frequency filtering is disabled + return images + + filtered_images = [] + for i in range(images.shape[0]): + filtered_img, _ = self.filter_image_by_frequency_torch( + images[i], kmin=self.kmin, kmax=self.kmax, sigma_frac=self.sigma_frac, device=self.device_val + ) + filtered_images.append(filtered_img) + + return torch.stack(filtered_images, dim=0) + def identity(self, x): """Return the input unchanged (identity function). @@ -289,7 +462,12 @@ def single_conv_batched(self, data): """ # Get device from the data tensor to ensure compatibility with quantized models device = data.device - return vmap(lambda x: single_conv(x, device=str(device)))(data) + + def _single_conv_wrapper(x): + """Wrapper function for single_conv to use with vmap.""" + return single_conv(x, device=str(device)) + + return vmap(_single_conv_wrapper)(data) def convert_zernike_device(self, data): """Convert Zernike coefficients format for device computation. @@ -305,7 +483,7 @@ def convert_zernike_device(self, data): Converted Zernike coefficients in proper format. """ # Use the device from the data tensor to ensure compatibility - device_str = 'cpu' if data.device.type == 'cpu' else 'cuda' + device_str = "cpu" if data.device.type == "cpu" else "cuda" return convert_zernikes_deploy(data, device=device_str) def convert_zernike_batched(self, data): @@ -371,17 +549,23 @@ def forward_align( """ centers = get_centers(image[0, 0, :, :], self.CROP_SIZE).to(self.device_val) cropped_image = batched_crop(image[:, 0, :, :], centers, crop_size=self.CROP_SIZE).float() - fx, fy, intra, band = fx.clone()[0, :, :].float(), fy.clone()[0, :, :].float(), intra.clone()[0, :, :].float(), band.clone()[0, :, :].int() + fx, fy, intra, band = ( + fx.clone()[0, :, :].float(), + fy.clone()[0, :, :].float(), + intra.clone()[0, :, :].float(), + band.clone()[0, :, :].int(), + ) for n in range(self.refinements): - pixel_shifts = self.alignnet_model(cropped_image[:, 0, :, :], - fx.clone(), - fy.clone(), intra.clone(), - band.clone()) * self.SCALE + pixel_shifts = ( + self.alignnet_model( + cropped_image[:, 0, :, :], fx.clone(), fy.clone(), intra.clone(), band.clone() + ) + * self.SCALE + ) centers += pixel_shifts.int() - cropped_image = batched_crop(image[0, :, :, :].float(), - centers, crop_size=self.CROP_SIZE) + cropped_image = batched_crop(image[0, :, :, :].float(), centers, crop_size=self.CROP_SIZE) fx += pixel_shifts[:, 0][..., None].int().float() * self.deg_per_pix fy += pixel_shifts[:, 1][..., None].int().float() * self.deg_per_pix @@ -432,16 +616,22 @@ def forward( centers = get_centers(image[0, 0, :, :], self.CROP_SIZE).to(self.device_val) cropped_image = batched_crop(image[:, 0, :, :], centers, crop_size=self.CROP_SIZE).float() - fx, fy, intra, band = fx.clone()[0, :, :].float(), fy.clone()[0, :, :].float(), intra.clone()[0, :, :].float(), band.clone()[0, :, :].int() + fx, fy, intra, band = ( + fx.clone()[0, :, :].float(), + fy.clone()[0, :, :].float(), + intra.clone()[0, :, :].float(), + band.clone()[0, :, :].int(), + ) - pixel_shifts = self.alignnet_model( - cropped_image[:, 0, :, :], fx.clone(), fy.clone(), intra.clone(), band.clone() - ) * self.SCALE + pixel_shifts = ( + self.alignnet_model( + cropped_image[:, 0, :, :], fx.clone(), fy.clone(), intra.clone(), band.clone() + ) + * self.SCALE + ) centers += pixel_shifts.int() - cropped_image = batched_crop( - image[0, :, :, :].float(), centers, crop_size=self.CROP_SIZE - ) + cropped_image = batched_crop(image[0, :, :, :].float(), centers, crop_size=self.CROP_SIZE) fx += pixel_shifts[:, 0][..., None].int().float() * self.deg_per_pix fy += pixel_shifts[:, 1][..., None].int().float() * self.deg_per_pix @@ -454,23 +644,18 @@ def forward( if keep_ind.sum() == 0: # No donuts detected, return zeros for Zernike coefficients # The final_layer will process the output, so we need to return the expected size - if self.final_layer == self.identity: - # If no final_layer, return 17 Zernike coefficients (WaveNet output size) - num_zernikes = 17 - else: - # If final_layer is present, return the size it outputs - num_zernikes = self.final_layer[-1].out_features + # If final_layer is present, return the size it outputs self.fx = [torch.tensor(0)] self.fy = [torch.tensor(0)] self.intra = [torch.tensor(0)] self.band = [torch.tensor(0)] self.SNR = [torch.tensor(0)] self.centers = [torch.tensor([0, 0])] - self.total_zernikes = torch.zeros((1, num_zernikes), device=self.device_val) + self.total_zernikes = torch.zeros((1, self.num_zernikes), device=self.device_val) self.cropped_image = torch.zeros((1, self.CROP_SIZE, self.CROP_SIZE), device=self.device_val) - self.total_zernikes = torch.zeros((1, num_zernikes), device=self.device_val) - self.ood_scores = None - return torch.zeros((1, num_zernikes), device=self.device_val) + self.total_zernikes = torch.zeros((1, self.num_zernikes), device=self.device_val) + self.ood_scores = torch.tensor([float("nan")], device=self.device_val) + return torch.zeros((1, self.num_zernikes), device=self.device_val) cropped_image = cropped_image[keep_ind] @@ -479,29 +664,28 @@ def forward( intra = intra[keep_ind] band = band[keep_ind] SNR = SNR[keep_ind] - total_zernikes = self.wavenet_model(cropped_image[:, 0, :, :], fx.clone(), fy.clone(), - intra.clone(), band.clone()) - total_zernikes = total_zernikes/1000 + + # Apply frequency filtering if kmin is set + filtered_cropped_image = self.filter_batch_images(cropped_image[:, 0, :, :]) + + total_zernikes = self.wavenet_model( + filtered_cropped_image, fx.clone(), fy.clone(), intra.clone(), band.clone() + ) + total_zernikes = total_zernikes / 1000 # Compute OOD scores if OOD model is available ood_scores = None - if self.ood_model is not None and hasattr(self.wavenet_model.wavenet, 'predictor_features'): - try: - # Get predictor penultimate features and detach/convert to CPU for numpy - penultimate = self.wavenet_model.wavenet.predictor_features # Shape: (batch_size, n_features) - - # Detach from computation graph and move to CPU for numpy conversion - features_np = penultimate.detach().cpu().numpy() - if self.ood_mean is not None: - features_centered = features_np - self.ood_mean.cpu().numpy() - mahalanobis_dist = self.ood_model.mahalanobis(features_centered) - # Store as tensor - ood_scores = torch.tensor(mahalanobis_dist, device=self.device_val, dtype=torch.float32) - else: - ood_scores = None - except Exception as e: - print(f"⚠️ OOD detection failed: {e}") - ood_scores = None + if self.ood_model is not None and self.wavenet_model.wavenet.predictor_features is not None: + # Get predictor penultimate features and detach/convert to CPU for numpy + penultimate = self.wavenet_model.wavenet.predictor_features # Shape: (batch_size, n_features) + + # Detach from computation graph and move to CPU for numpy conversion + features_np = penultimate.detach().cpu().numpy() + if self.ood_mean is not None: + features_centered = features_np - self.ood_mean.cpu().numpy() + mahalanobis_dist = self.ood_model.mahalanobis(features_centered) + # Store as tensor + ood_scores = torch.tensor(mahalanobis_dist, device=self.device_val, dtype=torch.float32) # Ensure all tensors are on the same device before concatenation device = total_zernikes.device @@ -511,6 +695,8 @@ def forward( fy = fy.to(device) SNR = SNR.to(device) self.centers = centers[keep_ind] + # These attributes can be either lists or tensors depending on context + # Suppress type checking for these dynamic attribute assignments self.fx = fx self.fy = fy self.intra = intra @@ -520,7 +706,8 @@ def forward( self.cropped_image = cropped_image[:, 0, :, :] self.ood_scores = ood_scores # Match training data normalization: use local max like in dataloader - # Training data: snr = torch.tensor(loaded_data["snr"]).to(self.device)[..., None] / torch.tensor(loaded_data["snr"]).max() + # Training data: snr = torch.tensor(loaded_data["snr"]).to(self.device)[..., None] + # / torch.tensor(loaded_data["snr"]).max() # Note: Training data uses global max across all data, but we only have local data # Using local max with epsilon for numerical stability SNR_normalized = SNR / (SNR.max() + 1e-8) # Add small epsilon to avoid division by zero @@ -530,26 +717,22 @@ def forward( position = torch.cat([fx, fy], dim=-1) # Concatenate features in the same order as training data: [zernikes, position, snr] - embedded_features = torch.cat([ - total_zernikes, - position, - SNR_normalized - ], dim=1) + embedded_features = torch.cat([total_zernikes, position, SNR_normalized], dim=1) if embedded_features.shape[0] > self.max_seq_length: - embedded_features = embedded_features[:self.max_seq_length, :] + embedded_features = embedded_features[: self.max_seq_length, :] else: - padding = torch.zeros((self.max_seq_length - embedded_features.shape[0], - embedded_features.shape[1])).to(self.device_val) - embedded_features = torch.cat([embedded_features, - padding], axis=0).to(self.device_val).float() + padding = torch.zeros( + (self.max_seq_length - embedded_features.shape[0], embedded_features.shape[1]) + ).to(self.device_val) + embedded_features = torch.cat([embedded_features, padding], dim=0).to(self.device_val).float() embedded_features = embedded_features[None, ...] # Match training data mean computation: # Training data: zk_mean1 = torch.mean(zk_pred1, dim=0) / 1000 # Since total_zernikes is already divided by 1000, we don't divide again - mean_zernike = torch.mean(total_zernikes, axis=0) + mean_zernike = torch.mean(total_zernikes, dim=0) # (OPTIONAL) Check the types if self.aggregator_on: @@ -609,16 +792,22 @@ def forward_shifts( centers = get_centers(image[0, 0, :, :], self.CROP_SIZE).to(self.device_val) cropped_image = batched_crop(image[:, 0, :, :], centers, crop_size=self.CROP_SIZE).float() - fx, fy, intra, band = fx.clone()[0, :, :].float(), fy.clone()[0, :, :].float(), intra.clone()[0, :, :].float(), band.clone()[0, :, :].int() + fx, fy, intra, band = ( + fx.clone()[0, :, :].float(), + fy.clone()[0, :, :].float(), + intra.clone()[0, :, :].float(), + band.clone()[0, :, :].int(), + ) - pixel_shifts = self.alignnet_model( - cropped_image[:, 0, :, :], fx.clone(), fy.clone(), intra.clone(), band.clone() - ) * self.SCALE + pixel_shifts = ( + self.alignnet_model( + cropped_image[:, 0, :, :], fx.clone(), fy.clone(), intra.clone(), band.clone() + ) + * self.SCALE + ) centers += pixel_shifts.int() - cropped_image = batched_crop( - image[0, :, :, :].float(), centers, crop_size=self.CROP_SIZE - ) + cropped_image = batched_crop(image[0, :, :, :].float(), centers, crop_size=self.CROP_SIZE) fx += pixel_shifts[:, 0][..., None].int().float() * self.deg_per_pix fy += pixel_shifts[:, 1][..., None].int().float() * self.deg_per_pix @@ -644,30 +833,28 @@ def forward_shifts( # Stack shifted images back into a batch shifted_cropped_image = torch.stack(shifted_images, dim=0) - total_zernikes = self.wavenet_model(shifted_cropped_image, fx.clone(), fy.clone(), - intra.clone(), band.clone()) - total_zernikes = total_zernikes/1000 + # Apply frequency filtering if kmin is set + filtered_shifted_cropped_image = self.filter_batch_images(shifted_cropped_image) + + total_zernikes = self.wavenet_model( + filtered_shifted_cropped_image, fx.clone(), fy.clone(), intra.clone(), band.clone() + ) + total_zernikes = total_zernikes / 1000 # Compute OOD scores if OOD model is available ood_scores = None - if self.ood_model is not None and hasattr(self.wavenet_model.wavenet, 'predictor_features'): - try: - # Get predictor penultimate features and detach/convert to CPU for numpy - penultimate = self.wavenet_model.wavenet.predictor_features # Shape: (batch_size, n_features) + if self.ood_model is not None and self.wavenet_model.wavenet.predictor_features is not None: + # Get predictor penultimate features and detach/convert to CPU for numpy + penultimate = self.wavenet_model.wavenet.predictor_features # Shape: (batch_size, n_features) - # Detach from computation graph and move to CPU for numpy conversion - features_np = penultimate.detach().cpu().numpy() - if self.ood_mean is not None: - features_centered = features_np - self.ood_mean.cpu().numpy() - mahalanobis_dist = self.ood_model.mahalanobis(features_centered) + # Detach from computation graph and move to CPU for numpy conversion + features_np = penultimate.detach().cpu().numpy() + if self.ood_mean is not None: + features_centered = features_np - self.ood_mean.cpu().numpy() + mahalanobis_dist = self.ood_model.mahalanobis(features_centered) - # Store as tensor - ood_scores = torch.tensor(mahalanobis_dist, device=self.device_val, dtype=torch.float32) - else: - ood_scores = None - except Exception as e: - print(f"⚠️ OOD detection failed: {e}") - ood_scores = None + # Store as tensor + ood_scores = torch.tensor(mahalanobis_dist, device=self.device_val, dtype=torch.float32) # Ensure all tensors are on the same device before concatenation device = total_zernikes.device @@ -679,7 +866,8 @@ def forward_shifts( self.total_zernikes = total_zernikes self.ood_scores = ood_scores # Match training data normalization: use local max like in dataloader - # Training data: snr = torch.tensor(loaded_data["snr"]).to(self.device)[..., None] / torch.tensor(loaded_data["snr"]).max() + # Training data: snr = torch.tensor(loaded_data["snr"]).to(self.device)[..., None] + # / torch.tensor(loaded_data["snr"]).max() # Note: Training data uses global max across all data, but we only have local data # Using local max with epsilon for numerical stability SNR_normalized = SNR / (SNR.max() + 1e-8) # Add small epsilon to avoid division by zero @@ -689,26 +877,22 @@ def forward_shifts( position = torch.cat([fx, fy], dim=-1) # Concatenate features in the same order as training data: [zernikes, position, snr] - embedded_features = torch.cat([ - total_zernikes, - position, - SNR_normalized - ], dim=1) + embedded_features = torch.cat([total_zernikes, position, SNR_normalized], dim=1) if embedded_features.shape[0] > self.max_seq_length: - embedded_features = embedded_features[:self.max_seq_length, :] + embedded_features = embedded_features[: self.max_seq_length, :] else: - padding = torch.zeros((self.max_seq_length - embedded_features.shape[0], - embedded_features.shape[1])).to(self.device_val) - embedded_features = torch.cat([embedded_features, - padding], axis=0).to(self.device_val).float() + padding = torch.zeros( + (self.max_seq_length - embedded_features.shape[0], embedded_features.shape[1]) + ).to(self.device_val) + embedded_features = torch.cat([embedded_features, padding], dim=0).to(self.device_val).float() embedded_features = embedded_features[None, ...] # Match training data mean computation: # Training data: zk_mean1 = torch.mean(zk_pred1, dim=0) / 1000 # Since total_zernikes is already divided by 1000, we don't divide again - mean_zernike = torch.mean(total_zernikes, axis=0) + mean_zernike = torch.mean(total_zernikes, dim=0) # (OPTIONAL) Check the types if self.aggregator_on: @@ -782,7 +966,7 @@ def loss_fn(self, x, y): mRSSe = torch.sqrt(sse).mean() return mRSSe - def configure_optimizers(self) -> torch.optim.Optimizer: + def configure_optimizers(self) -> Any: """Configure the optimizer.""" optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.lr) @@ -825,35 +1009,35 @@ def deploy_run(self, exposure, detectorName=None): new = assembleCcdTask.assembleCcd(exposure) SubtractBackground = subtractBackground.SubtractBackgroundTask() SubtractBackground.run(new) - except Exception as e: - print("Warning: switching to no CCD assembly: ", str(e)) + except (AttributeError, RuntimeError, ValueError, LengthError) as e: + logger.warning(f"Switching to no CCD assembly: {e}") new = exposure SubtractBackground = subtractBackground.SubtractBackgroundTask() SubtractBackground.run(new) image = new.getImage().array header = exposure.metadata - filter_name = header['FILTER'] + filter_name = header["FILTER"] if detectorName is None: - full_detectorName = header['RAFTBAY'] + '_' + header['CCDSLOT'] + full_detectorName = header["RAFTBAY"] + "_" + header["CCDSLOT"] detectorName = MAP_DETECTOR_TO_NUMBER[full_detectorName] # U G R I Z Y - if 'u' in filter_name: + if "u" in filter_name: filter_name = torch.tensor([0]) - elif 'g' in filter_name: + elif "g" in filter_name: filter_name = torch.tensor([1]) - elif 'r' in filter_name: + elif "r" in filter_name: filter_name = torch.tensor([2]) - elif 'i' in filter_name: + elif "i" in filter_name: filter_name = torch.tensor([3]) - elif 'z' in filter_name: + elif "z" in filter_name: filter_name = torch.tensor([4]) - elif 'y' in filter_name: + elif "y" in filter_name: filter_name = torch.tensor([5]) # check if intra or extra - if header['CCDSLOT'][2:] == '1': + if header["CCDSLOT"][2:] == "1": focal = torch.tensor([0]).float() - elif header['CCDSLOT'][2:] == '0': + elif header["CCDSLOT"][2:] == "0": focal = torch.tensor([1]).float() centers = get_centers(image, self.CROP_SIZE) @@ -865,11 +1049,17 @@ def deploy_run(self, exposure, detectorName=None): field_coords = [] for x, y in centers: coord = camera_transforms.ccdPixelToFocalMm(x, y, detectorName=detectorName) - field_coords.append([coord[0] / self.mm_pix * self.deg_per_pix, coord[1] / self.mm_pix * self.deg_per_pix]) - - field_coords = torch.tensor(field_coords, dtype=torch.float32) - field_x = field_coords[:, 0].unsqueeze(1).to(self.device_val)[None, ...] - field_y = field_coords[:, 1].unsqueeze(1).to(self.device_val)[None, ...] + field_coords.append( + [ + coord[0] / self.mm_pix * self.deg_per_pix, + coord[1] / self.mm_pix * self.deg_per_pix, + ] + ) + + field_coords_tensor: torch.Tensor = torch.tensor(field_coords, dtype=torch.float32) + # Extract field coordinates and add batch dimension + field_x = field_coords_tensor[:, 0].unsqueeze(1).to(self.device_val)[None, ...] + field_y = field_coords_tensor[:, 1].unsqueeze(1).to(self.device_val)[None, ...] # Vectorized focal and band values - ensure correct shape for forward method focal_val = focal.expand(centers.shape[0], 1).to(self.device_val)[None, ...] @@ -916,27 +1106,27 @@ def deploy_run_shifts(self, exposure, detectorName=None, shift_amount=5): SubtractBackground.run(new) image = new.getImage().array header = exposure.metadata - filter_name = header['FILTER'] + filter_name = header["FILTER"] if detectorName is None: - detectorName = header['CHIPID'] + detectorName = header["CHIPID"] # U G R I Z Y - if 'u' in filter_name: + if "u" in filter_name: filter_name = torch.tensor([0]) - elif 'g' in filter_name: + elif "g" in filter_name: filter_name = torch.tensor([1]) - elif 'r' in filter_name: + elif "r" in filter_name: filter_name = torch.tensor([2]) - elif 'i' in filter_name: + elif "i" in filter_name: filter_name = torch.tensor([3]) - elif 'z' in filter_name: + elif "z" in filter_name: filter_name = torch.tensor([4]) - elif 'y' in filter_name: + elif "y" in filter_name: filter_name = torch.tensor([5]) # check if intra or extra - if header['CCDSLOT'][2:] == '1': + if header["CCDSLOT"][2:] == "1": focal = torch.tensor([0]).float() - elif header['CCDSLOT'][2:] == '0': + elif header["CCDSLOT"][2:] == "0": focal = torch.tensor([1]).float() centers = get_centers(image, self.CROP_SIZE) @@ -948,11 +1138,17 @@ def deploy_run_shifts(self, exposure, detectorName=None, shift_amount=5): field_coords = [] for x, y in centers: coord = camera_transforms.ccdPixelToFocalMm(x, y, detectorName=detectorName) - field_coords.append([coord[0] / self.mm_pix * self.deg_per_pix, coord[1] / self.mm_pix * self.deg_per_pix]) - - field_coords = torch.tensor(field_coords, dtype=torch.float32) - field_x = field_coords[:, 0].unsqueeze(1).to(self.device_val)[None, ...] - field_y = field_coords[:, 1].unsqueeze(1).to(self.device_val)[None, ...] + field_coords.append( + [ + coord[0] / self.mm_pix * self.deg_per_pix, + coord[1] / self.mm_pix * self.deg_per_pix, + ] + ) + + field_coords_tensor: torch.Tensor = torch.tensor(field_coords, dtype=torch.float32) + # Extract field coordinates and add batch dimension + field_x = field_coords_tensor[:, 0].unsqueeze(1).to(self.device_val)[None, ...] + field_y = field_coords_tensor[:, 1].unsqueeze(1).to(self.device_val)[None, ...] # Vectorized focal and band values - ensure correct shape for forward method focal_val = focal.expand(centers.shape[0], 1).to(self.device_val)[None, ...] @@ -994,27 +1190,27 @@ def deploy_detect(self, exposure, detectorName=None): SubtractBackground.run(new) image = new.getImage().array header = exposure.metadata - filter_name = header['FILTER'] + filter_name = header["FILTER"] if detectorName is None: - detectorName = header['CHIPID'] + detectorName = header["CHIPID"] # U G R I Z Y - if 'u' in filter_name: + if "u" in filter_name: filter_name = torch.tensor([0]) - elif 'g' in filter_name: + elif "g" in filter_name: filter_name = torch.tensor([1]) - elif 'r' in filter_name: + elif "r" in filter_name: filter_name = torch.tensor([2]) - elif 'i' in filter_name: + elif "i" in filter_name: filter_name = torch.tensor([3]) - elif 'z' in filter_name: + elif "z" in filter_name: filter_name = torch.tensor([4]) - elif 'y' in filter_name: + elif "y" in filter_name: filter_name = torch.tensor([5]) # check if intra or extra - if header['CCDSLOT'][2:] == '1': + if header["CCDSLOT"][2:] == "1": focal = torch.tensor([0]).float() - elif header['CCDSLOT'][2:] == '0': + elif header["CCDSLOT"][2:] == "0": focal = torch.tensor([1]).float() centers = get_centers(image, self.CROP_SIZE) @@ -1026,11 +1222,17 @@ def deploy_detect(self, exposure, detectorName=None): field_coords = [] for x, y in centers: coord = camera_transforms.ccdPixelToFocalMm(x, y, detectorName=detectorName) - field_coords.append([coord[0] / self.mm_pix * self.deg_per_pix, coord[1] / self.mm_pix * self.deg_per_pix]) - - field_coords = torch.tensor(field_coords, dtype=torch.float32) - field_x = field_coords[:, 0].unsqueeze(1).to(self.device_val)[None, ...] - field_y = field_coords[:, 1].unsqueeze(1).to(self.device_val)[None, ...] + field_coords.append( + [ + coord[0] / self.mm_pix * self.deg_per_pix, + coord[1] / self.mm_pix * self.deg_per_pix, + ] + ) + + field_coords_tensor: torch.Tensor = torch.tensor(field_coords, dtype=torch.float32) + # Extract field coordinates and add batch dimension + field_x = field_coords_tensor[:, 0].unsqueeze(1).to(self.device_val)[None, ...] + field_y = field_coords_tensor[:, 1].unsqueeze(1).to(self.device_val)[None, ...] # Vectorized focal and band values - ensure correct shape for forward_align method focal_val = focal.expand(centers.shape[0], 1).to(self.device_val)[None, ...] @@ -1038,5 +1240,7 @@ def deploy_detect(self, exposure, detectorName=None): image_tensor = F.to_tensor(image)[None, ...] with torch.no_grad(): - aligned_images = self.forward_align(image_tensor.to(self.device_val), field_x, field_y, focal_val, band_val) + aligned_images = self.forward_align( + image_tensor.to(self.device_val), field_x, field_y, focal_val, band_val + ) return aligned_images diff --git a/python/tarts/__init__.py b/python/tarts/__init__.py index 3b8ee9c..02e4af2 100644 --- a/python/tarts/__init__.py +++ b/python/tarts/__init__.py @@ -11,6 +11,24 @@ - AggregatorNet: For combining predictions from multiple donuts - Utilities for data processing, model training, and deployment """ + +import logging + +# Configure logging for the package +_logger = logging.getLogger(__name__) +_logger.setLevel(logging.INFO) + +# Create a handler if none exists +if not _logger.handlers: + handler = logging.StreamHandler() + handler.setLevel(logging.INFO) + formatter = logging.Formatter( + "%(asctime)s - %(name)s - %(levelname)s - %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + ) + handler.setFormatter(formatter) + _logger.addHandler(handler) + from .NeuralActiveOpticsSys import * # noqa: F403, F401 from .utils import * # noqa: F403, F401 from .dataloader import * # noqa: F403, F401 diff --git a/python/tarts/aggregatornet.py b/python/tarts/aggregatornet.py index f8ab099..1ff15ca 100644 --- a/python/tarts/aggregatornet.py +++ b/python/tarts/aggregatornet.py @@ -4,12 +4,16 @@ predictions from multiple donut images to produce a single, improved estimate of Zernike coefficients for the LSST Active Optics System. """ + +# Third-party imports +import pytorch_lightning as pl import torch import torch.nn as nn -import pytorch_lightning as pl import torch.nn.functional as F_loss from torch.optim.lr_scheduler import ReduceLROnPlateau -# from typing import Tuple # Unused import +from typing import Any + +# Local/application imports from .utils import convert_zernikes_deploy @@ -51,6 +55,7 @@ def __init__( max_seq_length: int, lr=0.002507905395321983, num_zernikes=17, + zk_dof_zk=False, ): """Initialize the AggregatorNet model. @@ -74,6 +79,8 @@ def __init__( num_zernikes : int, optional The number of Zernike polynomial coefficients to predict (default is 19). + zk_dof_zk : bool, optional + Whether to use Zernike-DOF-Zernike conversion mode (default is False). Notes ----- @@ -81,19 +88,24 @@ def __init__( - The model outputs a linear transformation of size `num_zernikes`. """ - super(AggregatorNet, self).__init__() + super().__init__() self.save_hyperparameters() # Save model hyperparameters + + # Input projection layer: (num_zernikes + 3) -> d_model + # The +3 accounts for field_x, field_y, and snr features + input_dim = num_zernikes + 3 + self.input_proj = nn.Linear(input_dim, d_model) + encoder_layer = nn.TransformerEncoderLayer( d_model=d_model, nhead=nhead, dim_feedforward=dim_feedforward, batch_first=True, ) - self.transformer_encoder = nn.TransformerEncoder( - encoder_layer, num_layers=num_layers - ) + self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers) # final layer to transform to the shape of number of zernikes self.fc = nn.Linear(d_model, num_zernikes) + self.zk_dof_zk = zk_dof_zk def forward(self, x: tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor: """Forward pass of the AggregatorNet model. @@ -103,7 +115,7 @@ def forward(self, x: tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor: x : tuple of (torch.Tensor, torch.Tensor) A tuple where: - x[0] (torch.Tensor): The input sequence tensor of shape - (batch_size, seq_length, d_model). + (batch_size, seq_length, num_zernikes + 3). - x[1] (torch.Tensor): The mean tensor used for output adjustment. Returns @@ -113,14 +125,18 @@ def forward(self, x: tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor: Notes ----- - - The transformer encoder processes the first element of the tuple. + - Input features are first projected from (num_zernikes + 3) to d_model dimensions. + - The transformer encoder processes the projected features. - The last token's output is extracted and passed through a linear layer. - The mean correction (second element) is added to the final output. """ x_input, mean = x - x_tensor = self.transformer_encoder(x_input) + # Project input features to d_model dimensions + x_projected = self.input_proj(x_input) + # Pass through transformer + x_tensor = self.transformer_encoder(x_projected) x_tensor = x_tensor[:, -1, :] # Take the last token's output x_tensor = self.fc(x_tensor) # Predict the next token x_tensor += mean @@ -155,7 +171,8 @@ def training_step(self, batch: tuple, batch_idx: int): """ x, y = batch # y is the target token - logits = self.forward(x) + x_input, x_mean, filter_name, chipid = x + logits = self.forward((x_input, x_mean)) loss = self.loss_fn(logits, y) self.log("train_loss", loss, prog_bar=True) return loss @@ -189,7 +206,8 @@ def validation_step(self, batch, batch_idx): """ x, y = batch # y is the target token - logits = self.forward(x) + x_input, x_mean, filter_name, chipid = x + logits = self.forward((x_input, x_mean)) loss = self.loss_fn(logits, y) self.log("val_loss", loss, prog_bar=True) self.log("val_mRSSE", loss, prog_bar=True) # mRSSE is the same as loss for this model @@ -226,7 +244,7 @@ def loss_fn(self, x, y): mRSSe = torch.sqrt(sse).mean() return mRSSe - def configure_optimizers(self) -> torch.optim.Optimizer: + def configure_optimizers(self) -> Any: """Configure the optimizer.""" optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.lr) diff --git a/python/tarts/aggregatornet_coral.py b/python/tarts/aggregatornet_coral.py new file mode 100644 index 0000000..539abd2 --- /dev/null +++ b/python/tarts/aggregatornet_coral.py @@ -0,0 +1,502 @@ +"""Aggregator Network with DARE-GRAM domain adaptation for coral data. + +This module implements a transformer-based aggregator network with DARE-GRAM loss +for unsupervised domain adaptation. The method aligns inverse Gram matrices between +source (simulation) and target (coral/real) domains without requiring target labels. +""" + +# Standard library imports +import logging +from typing import Any, Tuple + +# Third-party imports +import pytorch_lightning as pl +import torch +import torch.nn as nn +import torch.nn.functional as F_loss +from torch.optim.lr_scheduler import ReduceLROnPlateau + +# Local/application imports +from .utils import convert_zernikes_deploy + +logger = logging.getLogger(__name__) + + +class AggregatorNet_Coral(pl.LightningModule): + """Aggregator Network with DARE-GRAM domain adaptation. + + Implements a transformer encoder network to aggregate the + values of multiple donuts and performs single point estimations, + with DARE-GRAM loss for domain adaptation to real data. + + Attributes + ---------- + input_proj : nn.Linear + Projects input features to d_model dimensions + transformer_encoder : nn.TransformerEncoder + Transformer encoder architecture + fc : nn.Linear + Fully connected output layer + transformer_features : torch.Tensor + Cached transformer features for domain adaptation + + Methods + ------- + forward() + Forward propagation through the model + training_step() + Single train step with domain adaptation + validation_step() + Single validation step + loss_fn() + Regression loss function (mRSSE) + dare_gram_loss() + Domain adaptation loss between source and target features + configure_optimizers() + Setup optimizers with learning rate scheduling + """ + + def __init__( + self, + d_model: int, + nhead: int, + num_layers: int, + dim_feedforward: int, + max_seq_length: int, + lr: float = 0.002507905395321983, + num_zernikes: int = 17, + tradeoff_angle: float = 0.05, + tradeoff_scale: float = 0.001, + threshold: float = 0.9, + dare_gram_weight: float = 1.0, + ): + """Initialize the AggregatorNet_Coral model. + + Parameters + ---------- + d_model : int + The number of expected features in the input (embedding dimension). + nhead : int + The number of attention heads in the multi-head attention mechanism. + num_layers : int + The number of transformer encoder layers. + dim_feedforward : int + The dimension of the feedforward network model inside the transformer encoder. + max_seq_length : int + The maximum sequence length of input data. + lr : float, optional + The learning rate for model training (default is 0.002507905395321983). + num_zernikes : int, optional + The number of Zernike polynomial coefficients to predict (default is 17). + tradeoff_angle : float, optional, default=0.05 + Weight for the DARE-GRAM angle alignment loss. + tradeoff_scale : float, optional, default=0.001 + Weight for the DARE-GRAM scale alignment loss. + threshold : float, optional, default=0.9 + Cumulative variance threshold for low-rank approximation. + dare_gram_weight : float, optional, default=1.0 + Overall weight to scale the DARE-GRAM loss relative to regression loss. + Higher values prioritize domain adaptation over regression accuracy. + + Notes + ----- + - The transformer encoder consists of `num_layers` encoder layers. + - The model outputs a linear transformation of size `num_zernikes`. + - Domain adaptation is performed by aligning features from the transformer. + """ + super().__init__() + self.save_hyperparameters() # Save model hyperparameters + + # Input projection layer: (num_zernikes + 3) -> d_model + # The +3 accounts for field_x, field_y, and snr features + input_dim = num_zernikes + 3 + self.input_proj = nn.Linear(input_dim, d_model) + + encoder_layer = nn.TransformerEncoderLayer( + d_model=d_model, + nhead=nhead, + dim_feedforward=dim_feedforward, + batch_first=True, + ) + self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers) + # final layer to transform to the shape of number of zernikes + self.fc = nn.Linear(d_model, num_zernikes) + + # Cache for transformer features (for domain adaptation) + self.transformer_features = None + self.val_mRSSE: torch.Tensor | None = None + + def forward(self, x: tuple[torch.Tensor, torch.Tensor], cache_features: bool = False) -> torch.Tensor: + """Forward pass of the AggregatorNet_Coral model. + + Parameters + ---------- + x : tuple of (torch.Tensor, torch.Tensor) + A tuple where: + - x[0] (torch.Tensor): The input sequence tensor of shape + (batch_size, seq_length, num_zernikes + 3). + - x[1] (torch.Tensor): The mean tensor used for output adjustment. + cache_features : bool, optional, default=False + Whether to cache transformer features for domain adaptation. + + Returns + ------- + torch.Tensor + The transformed output tensor of shape (batch_size, num_zernikes). + + Notes + ----- + - Input features are first projected from (num_zernikes + 3) to d_model dimensions. + - The transformer encoder processes the projected features. + - The last token's output is extracted and passed through a linear layer. + - The mean correction (second element) is added to the final output. + - If cache_features=True, transformer features are stored in self.transformer_features. + """ + x_input, mean = x + # Project input features to d_model dimensions + x_projected = self.input_proj(x_input) + # Pass through transformer + x_tensor = self.transformer_encoder(x_projected) + + # Cache features for domain adaptation if requested + if cache_features: + # Use the last token's features before FC layer + # Convert to float32 for SVD operations in DARE-GRAM loss + self.transformer_features = x_tensor[:, -1, :].detach().float() + + x_tensor = x_tensor[:, -1, :] # Take the last token's output + x_tensor = self.fc(x_tensor) # Predict the next token + x_tensor += mean + return x_tensor + + def dare_gram_loss(self, features_source: torch.Tensor, features_target: torch.Tensor) -> torch.Tensor: + """Compute DARE-GRAM loss between source and target features. + + Parameters + ---------- + features_source: torch.Tensor + Source domain features of shape (batch_size, n_features). + features_target: torch.Tensor + Target domain features of shape (batch_size, n_features). + + Returns + ------- + torch.Tensor + DARE-GRAM alignment loss. + """ + # Convert to float32 for SVD operations (not supported in half precision) + features_source = features_source.float() + features_target = features_target.float() + + batch_size, n_features = features_source.shape + + # Check for NaN or Inf values + if torch.any(torch.isnan(features_source)) or torch.any(torch.isnan(features_target)): + return torch.tensor(0.0, device=self.device) + if torch.any(torch.isinf(features_source)) or torch.any(torch.isinf(features_target)): + return torch.tensor(0.0, device=self.device) + + # Add bias term (ones column) to features + A = torch.cat((torch.ones(batch_size, 1, dtype=torch.float32).to(self.device), features_source), 1) + B = torch.cat((torch.ones(batch_size, 1, dtype=torch.float32).to(self.device), features_target), 1) + + # Compute covariance matrices + cov_A = A.t() @ A + cov_B = B.t() @ B + + # SVD to get eigenvalues + _, L_A, _ = torch.linalg.svd(cov_A) + _, L_B, _ = torch.linalg.svd(cov_B) + + # Normalize eigenvalues to get cumulative variance + # Temporarily disable deterministic algorithms for cumsum (not supported on CUDA) + is_deterministic = torch.are_deterministic_algorithms_enabled() + try: + if is_deterministic: + torch.use_deterministic_algorithms(False, warn_only=False) + eigen_A = torch.cumsum(L_A, dim=0) / L_A.sum() + eigen_B = torch.cumsum(L_B, dim=0) / L_B.sum() + finally: + if is_deterministic: + torch.use_deterministic_algorithms(True) + + # Determine rank k based on threshold + T = self.hparams.threshold + + # Find index where cumulative variance reaches threshold + if eigen_A[1] > T: + T_A = eigen_A[1] + else: + T_A = T + + index_A = torch.argwhere(eigen_A <= T_A) + if len(index_A) > 0: + index_A_val = int(index_A[-1][0].item()) + else: + index_A_val = 1 + + if eigen_B[1] > T: + T_B = eigen_B[1] + else: + T_B = T + + index_B = torch.argwhere(eigen_B <= T_B) + if len(index_B) > 0: + index_B_val = int(index_B[-1][0].item()) + else: + index_B_val = 1 + + k = max(index_A_val, index_B_val) + + # Ensure k is within valid range (avoid numerical issues) + n_eigen = min(len(L_A), len(L_B)) + k = min(k, n_eigen - 1) # Ensure k < n_eigen + + # Add safety check for numerical stability + if L_A[0] < 1e-10 or L_B[0] < 1e-10: + # Near-singular matrix, return small loss + return torch.tensor(0.0, device=self.device) + + # Compute pseudo-inverse with low-rank regularization + rtol_A = max((L_A[k] / L_A[0]).item(), 1e-6) + rtol_B = max((L_B[k] / L_B[0]).item(), 1e-6) + A_pinv = torch.linalg.pinv(cov_A, rtol=rtol_A) + B_pinv = torch.linalg.pinv(cov_B, rtol=rtol_B) + + # Compute cosine similarity for angle alignment + cos_sim = nn.CosineSimilarity(dim=0, eps=1e-6) + cos_distance = torch.dist( + torch.ones(n_features + 1, dtype=torch.float32).to(self.device), cos_sim(A_pinv, B_pinv), p=1 + ) / (n_features + 1) + + # Compute scale alignment loss + scale_loss = torch.dist(L_A[:k], L_B[:k], p=1) / k + + # Clamp losses to prevent extreme values + cos_distance = torch.clamp(cos_distance, min=0.0, max=10.0) + scale_loss = torch.clamp(scale_loss, min=0.0, max=100.0) + + # Combined DARE-GRAM loss + dare_gram_loss = self.hparams.tradeoff_angle * cos_distance + self.hparams.tradeoff_scale * scale_loss + + # Final clamp to prevent explosion + dare_gram_loss = torch.clamp(dare_gram_loss, min=0.0, max=100.0) + + return dare_gram_loss + + def exp_rise_flipped(self, loss, a=6.0): + """Exponentially rises from 0 at loss=0.10 to 1 at loss=0.09. + + Then flattens at 0 above 0.10 and 1 below 0.09. + """ + loss = torch.as_tensor(loss, dtype=torch.float32, device=self.device) + + # CHANGE HERE: + x1 = torch.tensor(0.09, dtype=torch.float32, device=loss.device) # Peak (1.0) + x2 = torch.tensor(0.10, dtype=torch.float32, device=loss.device) # Start (0.0) + + a = torch.tensor(a, dtype=torch.float32, device=loss.device) + + f = torch.zeros_like(loss, dtype=torch.float32) + + # Region 1: loss <= 0.09 (Before flip = 0) + f[loss <= x1] = 0.0 + + # Region 2: 0.09 < loss < 0.10 + mask = (loss > x1) & (loss < x2) + t = (loss[mask] - x1) / (x2 - x1) + f[mask] = (1 - torch.exp(-a * t)) / (1 - torch.exp(-a)) + + # Region 3: loss >= 0.10 (Before flip = 1) + f[loss >= x2] = 1.0 + + # FLIP: + # 0.10 (which was 1) -> becomes 0 + # 0.09 (which was 0) -> becomes 1 + f = -f + 1 + return f + + def calc_losses( + self, + batch: tuple, + batch_idx: int, + use_coral: bool = False, + add_dare_gram_to_loss: bool = True, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Calculate losses with optional DARE-GRAM domain adaptation. + + Parameters + ---------- + batch: tuple + Batch of training data. Format depends on coral mode: + - Without coral: (x, y) where x=(x_input, x_mean, filter_name, chipid) + - With coral: (x, y) where x=(x_input, x_mean, filter_name, chipid, + coral_x_total, coral_x_mean, coral_filter, coral_chipid) + batch_idx: int + Batch index. + use_coral: bool, default=False + Whether to compute DARE-GRAM loss with coral/target data. + add_dare_gram_to_loss: bool, default=True + Whether to add DARE-GRAM loss to the total loss. If False, DARE-GRAM is computed + for logging but not included in the total loss. + + Returns + ------- + tuple + (total_loss, mRSSE, dare_gram_loss) + dare_gram_loss is 0 if use_coral=False or coral data not available. + """ + x, y = batch # y is the target token + + # Check if coral data is present (8 elements in x vs 4 elements) + has_coral = len(x) == 8 + + if has_coral: + x_input, x_mean, filter_name, chipid, coral_x_total, coral_x_mean, coral_filter, coral_chipid = x + else: + x_input, x_mean, filter_name, chipid = x + + # Forward pass on source data + logits = self.forward((x_input, x_mean), cache_features=True) + source_features = self.transformer_features + + # Calculate regression loss + regression_loss = self.loss_fn(logits, y) + + # Extract mRSSE for monitoring + logits_converted = convert_zernikes_deploy(logits) + y_converted = convert_zernikes_deploy(y) + sse = F_loss.mse_loss(logits_converted, y_converted, reduction="none").sum(dim=-1) + mRSSE = torch.sqrt(sse).mean() + + # DARE-GRAM loss if coral data is available + dare_gram_loss = torch.tensor(0.0, device=self.device) + if use_coral and has_coral: + try: + # Forward pass on target/coral data + # Use eval mode to prevent BN updates (keep statistics source-domain only) + was_training = self.training + try: + self.eval() + _ = self.forward((coral_x_total, coral_x_mean), cache_features=True) + target_features = self.transformer_features + finally: + if was_training: + self.train() + + # Compute DARE-GRAM loss + dare_gram_loss = self.dare_gram_loss(source_features, target_features) + except (RuntimeError, ValueError, IndexError) as e: + logger.warning(f"DARE-GRAM loss computation failed: {e}") + dare_gram_loss = torch.tensor(0.0, device=self.device) + + # Add DARE-GRAM to loss only if requested + if add_dare_gram_to_loss: + scale_loss = self.exp_rise_flipped(self.val_mRSSE if self.val_mRSSE is not None else mRSSE) + total_loss = regression_loss + self.hparams.dare_gram_weight * scale_loss * dare_gram_loss + else: + total_loss = regression_loss + + return total_loss, mRSSE, dare_gram_loss + + def training_step(self, batch: tuple, batch_idx: int): + """Perform a single training step with domain adaptation. + + Parameters + ---------- + batch : tuple + A tuple containing input data and targets. + batch_idx : int + The index of the batch in the current epoch. + + Returns + ------- + torch.Tensor + The computed loss for the batch. + + Notes + ----- + - The model processes the batch using calc_losses with coral data. + - DARE-GRAM loss is added to regression loss if coral data is available. + - Training loss, mRSSE, and DARE-GRAM loss are logged for monitoring. + """ + loss, mRSSE, dare_gram_loss = self.calc_losses(batch, batch_idx, use_coral=True) + self.log("train_loss", loss, prog_bar=True, sync_dist=True) + self.log("train_mRSSE", mRSSE, sync_dist=True) + self.log("train_dare_gram_loss", dare_gram_loss, sync_dist=True) + return loss + + def validation_step(self, batch, batch_idx): + """Perform a single validation step. + + Parameters + ---------- + batch : tuple + A tuple containing input data and targets. + batch_idx : int + The index of the batch in the current epoch. + + Returns + ------- + torch.Tensor + The computed validation loss for the batch. + + Notes + ----- + - DARE-GRAM is computed for logging but not added to validation loss. + - This allows monitoring domain adaptation without affecting validation metrics. + - Validation loss, mRSSE, and DARE-GRAM loss are logged for monitoring. + """ + # Compute DARE-GRAM for logging but don't add it to validation loss + loss, mRSSE, dare_gram_loss = self.calc_losses( + batch, batch_idx, use_coral=True, add_dare_gram_to_loss=False + ) + self.log("val_loss", loss, prog_bar=True, sync_dist=True) + self.log("val_mRSSE", mRSSE, prog_bar=True, sync_dist=True) + self.log("val_dare_gram_loss", dare_gram_loss, sync_dist=True) + self.val_mRSSE = mRSSE.clone().detach() # Store a copy to avoid tensor reference issues + return loss + + def loss_fn(self, x, y): + """Compute the loss using the Root Sum of Squared Errors (mRSSE). + + Parameters + ---------- + x : torch.Tensor + The predicted tensor of shape (batch_size, num_zernikes). + y : torch.Tensor + The target tensor of shape (batch_size, num_zernikes). + + Returns + ------- + torch.Tensor + The computed mean Root Sum of Squared Errors (mRSSE). + + Notes + ----- + - The loss is calculated as the mean of the square root of + the sum of squared errors. + - Zernikes are converted to deployment format before computing loss. + - Mean squared error (MSE) is computed first, followed by + summation along the last dimension. + - The final value is the mean of the root sum of squared + errors across the batch. + """ + x = convert_zernikes_deploy(x) + y = convert_zernikes_deploy(y) + sse = F_loss.mse_loss(x, y, reduction="none").sum(dim=-1) + mRSSe = torch.sqrt(sse).mean() + return mRSSe + + def configure_optimizers(self) -> Any: + """Configure the optimizer with learning rate scheduling.""" + optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.lr) + + return { + "optimizer": optimizer, + "lr_scheduler": { + "scheduler": ReduceLROnPlateau(optimizer), + "monitor": "val_loss", + "frequency": 1, + }, + } diff --git a/python/tarts/alignnet.py b/python/tarts/alignnet.py index ee40907..a62f3c2 100644 --- a/python/tarts/alignnet.py +++ b/python/tarts/alignnet.py @@ -1,9 +1,10 @@ """Neural network to predict donut placement coefficients from donut images and positions.""" +# Third-party imports +import timm import torch from torch import nn from torchvision import models as cnn_models -import timm class AlignNet(nn.Module): @@ -43,8 +44,8 @@ def __init__( cnn_model: str = "mobilenetv4_conv_small", freeze_cnn: bool = False, n_predictor_layers: tuple = (256,), - device='cuda', - pretrained: bool = True + device="cuda", + pretrained: bool = True, ) -> None: """Initialize the NeuralAlignment model. @@ -103,7 +104,7 @@ def __init__( nn.Linear(n_features, n_predictor_layers[0]), nn.BatchNorm1d(n_predictor_layers[0]), nn.ReLU(), - nn.Dropout(p=0.2) + nn.Dropout(p=0.2), ] # add any additional layers @@ -138,11 +139,15 @@ def _load_cnn_backbone(self, cnn_model: str, pretrained: bool) -> None: # Check if it's a timm model (MobileNetV4, etc.) if cnn_model.startswith("mobilenetv4") or cnn_model in timm.list_models(): # Load from timm - self.cnn = timm.create_model( - cnn_model, - pretrained=pretrained, - num_classes=0 # This removes the classifier and returns pooled features - ).to(self.device_val).float() # Explicitly convert to float32 + self.cnn = ( + timm.create_model( + cnn_model, + pretrained=pretrained, + num_classes=0, # This removes the classifier and returns pooled features + ) + .to(self.device_val) + .float() + ) # Explicitly convert to float32 self.is_timm_model = True # Get actual feature dimension by doing a dummy forward pass @@ -160,7 +165,10 @@ def _load_cnn_backbone(self, cnn_model: str, pretrained: bool) -> None: else: # Load from torchvision weights_param = "DEFAULT" if pretrained else None - self.cnn = getattr(cnn_models, cnn_model)(weights=weights_param).to(self.device_val).float() # Explicitly convert to float32 + if not hasattr(cnn_models, cnn_model): + raise ValueError(f"Unknown torchvision model: {cnn_model}") + model_fn = getattr(cnn_models, cnn_model) + self.cnn = model_fn(weights=weights_param).to(self.device_val).float() # Get feature dimension self.n_cnn_features = self.cnn.fc.in_features self.is_timm_model = False @@ -172,8 +180,8 @@ def _remove_final_layer(self) -> None: pass else: # For torchvision models, remove the final fully connected layer - if hasattr(self.cnn, 'fc'): - self.cnn.fc = nn.Identity() + # torchvision models always have fc layer + self.cnn.fc = nn.Identity() def _reshape_image(self, image: torch.Tensor) -> torch.Tensor: """Expand a single-channel image tensor to have three identical channels. @@ -204,15 +212,12 @@ def _reshape_image(self, image: torch.Tensor) -> torch.Tensor: image = image[..., None, :, :] # Get the number of input channels required by the CNN - if hasattr(self.cnn, 'conv1'): - # torchvision models (ResNet, etc.) - n_channels = self.cnn.conv1.in_channels - elif hasattr(self.cnn, 'conv_stem'): + if self.is_timm_model: # timm models (MobileNet, etc.) n_channels = self.cnn.conv_stem.in_channels else: - # Default to 3 channels if we can't determine - n_channels = 3 + # torchvision models (ResNet, etc.) + n_channels = self.cnn.conv1.in_channels # duplicate image for each channel image = image.repeat_interleave(n_channels, dim=-3) diff --git a/python/tarts/constants.py b/python/tarts/constants.py new file mode 100644 index 0000000..92fb1d1 --- /dev/null +++ b/python/tarts/constants.py @@ -0,0 +1,343 @@ +"""Constants used throughout the TARTS package. + +This module centralizes all constants, configuration values, and magic numbers +used across the codebase for better maintainability and consistency. +""" + +# Standard library imports +import logging +from pathlib import Path +from typing import Dict, Optional + +# Third-party imports +import numpy as np +import torch + +# Local/application imports +from .KERNEL import CUTOUT as DONUT_KERNEL + +logger = logging.getLogger(__name__) + +# ============================================================================ +# System Configuration +# ============================================================================ + +# LSST system availability flag +LSST_AVAILABLE = True + +# Camera type identifier +CAMERA_TYPE = "LsstCam" + +# Input image shape (height, width) +DEFAULT_INPUT_SHAPE = (160, 160) + +# Default crop size for donut images +DEFAULT_CROP_SIZE = 160 + +# ============================================================================ +# Detector Mappings +# ============================================================================ + +MAP_DETECTOR_TO_NUMBER = { + "R00_SW0": 191, + "R00_SW1": 192, + "R04_SW0": 195, + "R04_SW1": 196, + "R40_SW0": 199, + "R40_SW1": 200, + "R44_SW0": 203, + "R44_SW1": 204, +} + +# ============================================================================ +# Band Mappings and Values +# ============================================================================ + +# Band wavelength values (microns) +BAND_MAP = { + 0: 0.3671, # u-band + 1: 0.4827, # g-band + 2: 0.6223, # r-band + 3: 0.7546, # i-band + 4: 0.8691, # z-band + 5: 0.9712, # y-band +} + +# Band string to integer mapping +BAND_STR_INT = { + "u": 0, + "g": 1, + "r": 2, + "i": 3, + "z": 4, + "y": 5, +} + +# Band values as tensor (for efficient batch processing) +BAND_VALUES_TENSOR = torch.tensor([[0.3671], [0.4827], [0.6223], [0.7546], [0.8691], [0.9712]]) + +# ============================================================================ +# Field Position Mappings +# ============================================================================ + +FIELD_POSITIONS = { + "R00": {"fieldx": -1.1897, "fieldy": -1.1897}, + "R04": {"fieldx": -1.1897, "fieldy": 1.1897}, + "R40": {"fieldx": 1.1897, "fieldy": -1.1897}, + "R44": {"fieldx": 1.1897, "fieldy": 1.1897}, +} + +# ============================================================================ +# Normalization Constants +# ============================================================================ + +# Field position normalization (degrees) +FIELD_MEAN = 0.000 +FIELD_STD = 0.021 + +# Intrafocal flag normalization (0 or 1) +INTRA_MEAN = 0.5 +INTRA_STD = 0.5 + +# Band wavelength normalization (microns) +BAND_MEAN = 0.710 +BAND_STD = 0.174 + +# ============================================================================ +# Zernike Defaults +# ============================================================================ + +# Default Noll Zernike indices (1-indexed, converted to 0-indexed in code) +# Matches the default in dataset_params.yaml +DEFAULT_NOLL_ZK = [ + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, +] + +# ============================================================================ +# Model Defaults +# ============================================================================ + +# Default CNN model +DEFAULT_CNN_MODEL = "resnet18" + +# Default number of Zernike coefficients +DEFAULT_N_ZERNIKES = 25 + +# Default predictor layer sizes +DEFAULT_PREDICTOR_LAYERS = (256,) + +# Default learning rate +DEFAULT_LEARNING_RATE = 1e-3 + +# Default L2 penalty weight +DEFAULT_ALPHA = 0.0 + +# ============================================================================ +# Dataset Defaults +# ============================================================================ + +# Default adjustment factor for image shifting +DEFAULT_ADJUSTMENT_FACTOR = 0 + +# Default SNR threshold for filtering +DEFAULT_SNR_THRESHOLD = 1 + +# Training data fraction (for debugging/development) +DEFAULT_TRAIN_FRACTION = 0.5 + +# ============================================================================ +# Processing Constants +# ============================================================================ + +# Conversion factor: degrees to radians +DEG_TO_RAD = 3.141592653589793 / 180.0 # π / 180 + +# Zernike scaling factor (microns to arcsec) +ZERNIKE_SCALE_FACTOR = 1000 + +# ============================================================================ +# Kernel Templates +# ============================================================================ + +# Donut cutout template (imported from KERNEL module) +DONUT_TEMPLATE = DONUT_KERNEL + +# ============================================================================ +# OFC DOF Conversion Matrices (Pre-computed) +# ============================================================================ + +# Default sensor names for OFC operations +OFC_SENSOR_NAMES = ["R00_SW0", "R04_SW0", "R40_SW0", "R44_SW0"] + +# Default filter names (uppercase for OFC system) +OFC_FILTER_NAMES = ["U", "G", "R", "I", "Z", "Y"] + +# Filter name mapping (lowercase to uppercase) +OFC_FILTER_NAME_MAP = { + "u": "U", + "g": "G", + "r": "R", + "i": "I", + "z": "Z", + "y": "Y", + "U": "U", + "G": "G", + "R": "R", + "I": "I", + "Z": "Z", + "Y": "Y", # Allow uppercase too +} + +# OFC matrix cache (lazy loaded) +_OFC_MATRICES_CACHE: Optional[Dict[str, torch.Tensor]] = None +_OFC_MATRICES_PATH: Optional[Path] = None + + +def set_ofc_matrices_path(path: str) -> None: + """Set the path to pre-computed OFC matrices. + + Parameters + ---------- + path : str + Directory containing the pre-computed OFC matrices. + """ + global _OFC_MATRICES_PATH, _OFC_MATRICES_CACHE + _OFC_MATRICES_PATH = Path(path) + _OFC_MATRICES_CACHE = None # Clear cache to force reload + + +def load_ofc_matrices(matrices_dir: Optional[str] = None) -> Dict[str, torch.Tensor]: + """Load pre-computed OFC matrices for DOF conversion. + + This function lazy-loads matrices generated by precompute_ofc_matrices.py. + Matrices are cached after first load for efficiency. + + Parameters + ---------- + matrices_dir : str, optional + Directory containing the OFC matrices. If None, looks in package directory. + + Returns + ------- + Dict[str, torch.Tensor] + Dictionary containing: + - 'sensitivity_matrix': (n_sensors*n_zk, n_dof) sensitivity matrix + - 'intrinsic_zernikes': dict of {filter: (n_sensors, n_zk)} arrays + - 'y2_correction': dict of {filter: (n_sensors, n_zk)} arrays + - 'metadata': dict with configuration info + """ + global _OFC_MATRICES_CACHE, _OFC_MATRICES_PATH + + # Return cached matrices if available + if _OFC_MATRICES_CACHE is not None: + return _OFC_MATRICES_CACHE + + # Determine path + if matrices_dir is not None: + matrix_path = Path(matrices_dir) + elif _OFC_MATRICES_PATH is not None: + matrix_path = _OFC_MATRICES_PATH + else: + # Default: look in package directory + matrix_path = Path(__file__).parent / "ofc_matrices" + + if not matrix_path.exists(): + raise FileNotFoundError( + f"OFC matrices not found at {matrix_path}. " + f"Please run precompute_ofc_matrices.py first or set the path using set_ofc_matrices_path()." + ) + + try: + # Load matrices + logger.info(f"Loading OFC matrices from {matrix_path}") + + sens_matrix = np.load(matrix_path / "sensitivity_matrix.npy") + intrinsic_dict = np.load(matrix_path / "intrinsic_zernikes.npy", allow_pickle=True).item() + y2_dict = np.load(matrix_path / "y2_correction.npy", allow_pickle=True).item() + metadata = np.load(matrix_path / "metadata.npy", allow_pickle=True).item() + + # Convert to tensors and cache - preserve float64 precision + _OFC_MATRICES_CACHE = { + "sensitivity_matrix": torch.from_numpy(sens_matrix).double(), + "intrinsic_zernikes": {k: torch.from_numpy(v).double() for k, v in intrinsic_dict.items()}, + "y2_correction": {k: torch.from_numpy(v).double() for k, v in y2_dict.items()}, + "metadata": metadata, + } + + # Load per-sensor data if available (for flexible sensor order support) + sample_points_path = matrix_path / "sample_points.npy" + intrinsic_per_sensor_path = matrix_path / "intrinsic_per_sensor.npy" + y2_correction_per_sensor_path = matrix_path / "y2_correction_per_sensor.npy" + norm_weights_path = matrix_path / "normalization_weights.npy" + dof_indices_path = matrix_path / "dof_indices.npy" + + if sample_points_path.exists(): + sample_points_dict = np.load(sample_points_path, allow_pickle=True).item() + # Keep as numpy arrays (not torch) since they're used for field angle computation + _OFC_MATRICES_CACHE["sample_points"] = sample_points_dict + logger.info(f"Loaded sample_points for {len(sample_points_dict)} sensors") + + if intrinsic_per_sensor_path.exists(): + intrinsic_per_sensor = np.load(intrinsic_per_sensor_path, allow_pickle=True).item() + # Convert nested dict to torch tensors + intrinsic_per_sensor_torch = {} + for filter_name, sensor_dict in intrinsic_per_sensor.items(): + intrinsic_per_sensor_torch[filter_name] = { + sensor: torch.from_numpy(arr).double() for sensor, arr in sensor_dict.items() + } + _OFC_MATRICES_CACHE["intrinsic_per_sensor"] = intrinsic_per_sensor_torch + logger.info(f"Loaded per-sensor intrinsic zernikes for {len(intrinsic_per_sensor)} filters") + + if y2_correction_per_sensor_path.exists(): + y2_correction_per_sensor = np.load(y2_correction_per_sensor_path, allow_pickle=True).item() + # Convert nested dict to torch tensors + y2_correction_per_sensor_torch = {} + for filter_name, sensor_dict in y2_correction_per_sensor.items(): + y2_correction_per_sensor_torch[filter_name] = { + sensor: torch.from_numpy(arr).double() for sensor, arr in sensor_dict.items() + } + _OFC_MATRICES_CACHE["y2_correction_per_sensor"] = y2_correction_per_sensor_torch + logger.info(f"Loaded per-sensor y2_correction for {len(y2_correction_per_sensor)} filters") + + if norm_weights_path.exists(): + norm_weights = np.load(norm_weights_path) + _OFC_MATRICES_CACHE["normalization_weights"] = torch.from_numpy(norm_weights).double() + + if dof_indices_path.exists(): + dof_indices = np.load(dof_indices_path) + # Store as list for easier indexing + _OFC_MATRICES_CACHE["dof_indices"] = dof_indices.tolist() + + logger.info(f"Loaded OFC matrices: {metadata}") + return _OFC_MATRICES_CACHE + + except Exception as e: + logger.error(f"Failed to load OFC matrices: {e}") + raise RuntimeError( + f"Could not load OFC matrices from {matrix_path}. " + f"Please ensure precompute_ofc_matrices.py has been run successfully." + ) from e diff --git a/python/tarts/dataloader.py b/python/tarts/dataloader.py index a92f1b3..320f69d 100644 --- a/python/tarts/dataloader.py +++ b/python/tarts/dataloader.py @@ -1,14 +1,24 @@ """Pytorch DataSet for the AOS simulations.""" +# Standard library imports import glob -from typing import Any, Dict +import logging +import os import pickle +import json +from typing import Any, Dict, List, Optional, Tuple, Union + +# Third-party imports import numpy as np import torch from astropy.table import Table -from .utils import transform_inputs, shift_offcenter from torch.utils.data import Dataset -import os + +# Local/application imports +from .constants import DEFAULT_NOLL_ZK, DEFAULT_TRAIN_FRACTION +from .utils import shift_offcenter, transform_inputs, augment_data_torch, add_random_hot_pixel + +logger = logging.getLogger(__name__) class Donuts(Dataset): @@ -87,11 +97,7 @@ def __init__( # get a list of all the observations all_image_files = glob.glob(f"{data_dir}/images/*") - obs_ids = list( - set( - [int(file.split("/")[-1].split(".")[1][3:]) for file in all_image_files] - ) - ) + obs_ids = list(set([int(file.split("/")[-1].split(".")[1][3:]) for file in all_image_files])) # get the table of metadata for each observation observations = Table.read(f"{data_dir}/opSimTable.parquet") @@ -123,11 +129,7 @@ def __init__( self.adjustment_factor = adjustment_factor # partition the image files self.image_files = { - mode: [ - file - for file in all_image_files - if int(file.split("/")[-1].split(".")[1][3:]) in ids - ] + mode: [file for file in all_image_files if int(file.split("/")[-1].split(".")[1][3:]) in ids] for mode, ids in self.obs_ids.items() } @@ -172,9 +174,7 @@ def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: pntId, obsId, objId = img_file.split("/")[-1].split(".")[:3] # get the catalog for this observation - catalog = Table.read( - f"{self.settings['data_dir']}/catalogs/{pntId}.catalog.parquet" - ) + catalog = Table.read(f"{self.settings['data_dir']}/catalogs/{pntId}.catalog.parquet") # get the row for this source row = catalog[catalog["objectId"] == int(objId[3:])][0] @@ -186,9 +186,7 @@ def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: intra = "SW1" in row["detector"] # get the observed band - obs_row = self.observations[ - self.observations["observationId"] == int(obsId[3:]) - ] + obs_row = self.observations[self.observations["observationId"] == int(obsId[3:])] band = "ugrizy".index(obs_row["lsstFilter"].item()) # load the zernikes @@ -197,7 +195,7 @@ def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: f"{self.settings['data_dir']}/zernikes/" f"{pntId}.{obsId}.detector{row['detector'][:3]}.zernikes.npy" ), - allow_pickle=True + allow_pickle=True, ) # load the degrees of freedom @@ -216,14 +214,9 @@ def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: # convert everything to tensors img = torch.from_numpy(img).float() # shift the image - img_adjusted, offset_amount = shift_offcenter( - img, adjust=self.adjustment_factor, return_offset=True - ) + img_adjusted, offset_amount = shift_offcenter(img, adjust=self.adjustment_factor, return_offset=True) # track the offset vector and renormalise the vector amount - offset_vec = ( - np.array(np.array(offset_amount).astype(np.float32)) - / self.adjustment_factor - ) + offset_vec = np.array(np.array(offset_amount).astype(np.float32)) / self.adjustment_factor # compute the radial offset factor (vector norm) offset_r = np.sqrt(offset_amount[0] ** 2 + offset_amount[1] ** 2) offset_r = np.array(offset_r.astype(np.float32))[None] / self.adjustment_factor @@ -231,8 +224,8 @@ def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: # record the meta data fx = torch.FloatTensor([fx]) fy = torch.FloatTensor([fy]) - intra = torch.FloatTensor([intra]) # type: ignore - band = torch.FloatTensor([band]) # type: ignore + intra = torch.FloatTensor([intra]) + band = torch.FloatTensor([band]) zernikes = torch.from_numpy(zernikes).float() dof = torch.from_numpy(dof).float() @@ -294,10 +287,13 @@ def __init__( transform: bool = True, adjustment_factor=0, data_dir: str = "/media/peterma/mnt2/peterma/research/LSST_FULL_FRAME/simulation_pretrain/", - noll_zk: list = [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 21, 22, 27, 28], + noll_zk: Optional[List[int]] = None, coral_filepath: str = "/media/peterma/mnt2/peterma/research/LSST_FULL_FRAME/coral/", coral_mode: bool = False, mask_mode: bool = False, + augment: bool = False, + augment_scale: float = 0.1, + augment_kmin: float = 0.1, **kwargs: Any, ) -> None: """Load the simulated ImSim donuts and zernikes in a Pytorch Dataset. @@ -313,14 +309,25 @@ def __init__( RADIAL factor used to shift the image during loading. data_dir: str, default=aos_sims Location of the data directory. - noll_zk: list, default=[4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 21, 22, 27, 28] + noll_zk: Optional[List[int]], default=None List of Noll Zernike indices to include in the dataset. + If None, defaults to [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 21, 22, 27, 28]. coral_filepath: str, default="/media/peterma/mnt2/peterma/research/LSST_FULL_FRAME/coral/" Path to the coral dataset directory. coral_mode: bool, default=False Whether to enable coral mode for domain adaptation. mask_mode: bool, default=False Whether to use mask mode for zernike extraction. + augment: bool, default=False + Whether to apply frequency-based augmentation. Requires coral_mode=True. + When enabled, randomly augments 50% of samples by injecting high-frequency + structure from coral images into simulation images. + augment_scale: float, default=0.1 + (Deprecated) Scaling factor parameter. The scale is now randomly sampled + uniformly between 0.1 and 1.0 for each augmentation. This parameter is + kept for backward compatibility but is not used. + augment_kmin: float, default=0.1 + Minimum frequency magnitude to extract from coral images for augmentation. """ self.settings = { "mode": mode, @@ -328,9 +335,9 @@ def __init__( "data_dir": data_dir, } if self.settings["mode"] == "train": - self.image_dir = data_dir + '/train' + self.image_dir = data_dir + "/train" if self.settings["mode"] == "val": - self.image_dir = data_dir + '/val' + self.image_dir = data_dir + "/val" self.mask_mode = mask_mode self.image_files = [] # Loop through all files and subdirectories @@ -338,17 +345,26 @@ def __init__( for file in files: file_path = os.path.join(root, file) self.image_files.append(file_path) - print(self.image_dir) + logger.debug(f"Image directory: {self.image_dir}") self.coral_filepath = coral_filepath self.coral_mode = coral_mode + self.augment = augment + self.augment_scale = augment_scale + self.augment_kmin = augment_kmin + + # Augmentation requires coral_mode to be enabled + if self.augment and not self.coral_mode: + logger.warning("augment=True requires coral_mode=True. Disabling augmentation.") + self.augment = False + if coral_mode: self.coral_image_files = [] # Use a temporary variable to avoid modifying the original path coral_data_path = coral_filepath if self.settings["mode"] == "train": - coral_data_path += '/train' + coral_data_path += "/train" if self.settings["mode"] == "val": - coral_data_path += '/val' + coral_data_path += "/val" for root, _, files in os.walk(coral_data_path): for file in files: file_path = os.path.join(root, file) @@ -357,16 +373,18 @@ def __init__( if self.settings["mode"] == "train": # self.image_files = self.image_files[: int(0.1 * len(self.image_files))] - self.image_files = self.image_files[: int(0.5 * len(self.image_files))] + self.image_files = self.image_files[: int(DEFAULT_TRAIN_FRACTION * len(self.image_files))] - self.noll_zk = np.array(noll_zk)-4 + if noll_zk is None: + noll_zk = DEFAULT_NOLL_ZK + self.noll_zk = np.array(noll_zk) - 4 self.adjustment_factor = adjustment_factor def __len__(self) -> int: """Return length of this Dataset.""" - return len(self.image_files) # type: ignore + return len(self.image_files) - def sample_coral(self) -> Dict[str, torch.Tensor]: + def sample_coral(self) -> Dict[str, Any]: """Sample a coral image randomly.""" # Check if coral files are available if not self.coral_image_files or len(self.coral_image_files) == 0: @@ -379,7 +397,7 @@ def sample_coral(self) -> Dict[str, torch.Tensor]: for attempt in range(max_retries): try: # Re-check availability in case files were deleted - if len(self.coral_image_files) == 0: + if not self.coral_image_files: raise RuntimeError("All coral image files have been removed due to corruption.") idx = np.random.randint(0, len(self.coral_image_files)) @@ -393,13 +411,13 @@ def sample_coral(self) -> Dict[str, torch.Tensor]: break # Successfully loaded, exit retry loop except (EOFError, IOError, OSError) as e: # File is corrupted, truncated, or missing - delete it - print(f"⚠️ Corrupted coral file detected: {img_file}. Error: {e}. Deleting...") + logger.warning(f"Corrupted coral file detected: {img_file}. Error: {e}. Deleting...") try: if os.path.exists(img_file): os.remove(img_file) - print(f"✓ Deleted corrupted file: {img_file}") - except Exception as delete_error: - print(f"⚠️ Failed to delete {img_file}: {delete_error}") + logger.info(f"Deleted corrupted file: {img_file}") + except (OSError, PermissionError) as delete_error: + logger.warning(f"Failed to delete {img_file}: {delete_error}") # Remove from list to avoid trying again if img_file in self.coral_image_files: @@ -424,7 +442,8 @@ def sample_coral(self) -> Dict[str, torch.Tensor]: # get the intra/extra flag intra = torch.tensor(state["intra"]).int() - band = torch.tensor(state["band"]).int().item() + band_tensor = torch.tensor(state["band"]).int() + band = band_tensor.item() img = torch.tensor(state["image_aligned"]) # Get zernikes using noll_zk indexing @@ -433,28 +452,42 @@ def sample_coral(self) -> Dict[str, torch.Tensor]: # standardize all the inputs for the neural net if self.settings["transform"]: - img, fx, fy, intra, band = transform_inputs( # type: ignore - img, - fx, - fy, - intra, - band, + # Convert tensors to numpy for transform_inputs + img_np = img.cpu().numpy() if isinstance(img, torch.Tensor) else img + fx_val = float(fx.item() if isinstance(fx, torch.Tensor) else fx) + fy_val = float(fy.item() if isinstance(fy, torch.Tensor) else fy) + intra_val = bool(intra.item() if isinstance(intra, torch.Tensor) else intra) + band_val = int(band_tensor.item() if isinstance(band_tensor, torch.Tensor) else band) + img_out, fx_out, fy_out, intra_out, band_out = transform_inputs( + img_np, + fx_val, + fy_val, + intra_val, + band_val, ) - - # convert everything to tensors - img = img.float() - # get meta data - fx = torch.FloatTensor([fx]) - fy = torch.FloatTensor([fy]) - intra = torch.FloatTensor([intra]) # type: ignore - band = torch.FloatTensor([band]) # type: ignore + # convert everything to tensors + img = torch.from_numpy(img_out).float() if isinstance(img_out, np.ndarray) else img_out.float() + # get meta data + fx_tensor = torch.FloatTensor([float(fx_out)]) + fy_tensor = torch.FloatTensor([float(fy_out)]) + intra_tensor = torch.FloatTensor([float(intra_out)]) + band_tensor = torch.FloatTensor([float(band_out)]) + else: + # convert everything to tensors if not transformed + img = img.float() + fx_tensor = torch.FloatTensor([float(fx.item() if isinstance(fx, torch.Tensor) else fx)]) + fy_tensor = torch.FloatTensor([float(fy.item() if isinstance(fy, torch.Tensor) else fy)]) + intra_tensor = torch.FloatTensor( + [float(intra.item() if isinstance(intra, torch.Tensor) else intra)] + ) + band_tensor = torch.FloatTensor([float(band)]) zernikes = zernikes.float()[0, :] coral_output = { "coral_image": img, - "coral_field_x": fx, - "coral_field_y": fy, - "coral_intrafocal": intra, - "coral_band": band, + "coral_field_x": fx_tensor, + "coral_field_y": fy_tensor, + "coral_intrafocal": intra_tensor, + "coral_band": band_tensor, "coral_zernikes": zernikes, } return coral_output @@ -502,52 +535,61 @@ def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: else: zernikes = torch.tensor(state["zk_true"])[:, self.noll_zk] - band = torch.tensor(state["band"]).int().item() + band_tensor = torch.tensor(state["band"]).int() + band = band_tensor.item() img = torch.tensor(state["image_aligned"]) # standardize all the inputs for the neural net if self.settings["transform"]: - img, fx, fy, intra, band = transform_inputs( # type: ignore - img, - fx, - fy, - intra, - band, + # Convert tensors to numpy for transform_inputs + img_np = img.cpu().numpy() if isinstance(img, torch.Tensor) else img + fx_val = float(fx.item() if isinstance(fx, torch.Tensor) else fx) + fy_val = float(fy.item() if isinstance(fy, torch.Tensor) else fy) + intra_val = bool(intra.item() if isinstance(intra, torch.Tensor) else intra) + band_val = int(band_tensor.item() if isinstance(band_tensor, torch.Tensor) else band) + img_out, fx_out, fy_out, intra_out, band_out = transform_inputs( + img_np, + fx_val, + fy_val, + intra_val, + band_val, ) - - # convert everything to tensors - img = img.float() + # convert everything to tensors + img = torch.from_numpy(img_out).float() if isinstance(img_out, np.ndarray) else img_out.float() + # get meta data + fx_tensor = torch.FloatTensor([float(fx_out)]) + fy_tensor = torch.FloatTensor([float(fy_out)]) + intra_tensor = torch.FloatTensor([float(intra_out)]) + band_tensor = torch.FloatTensor([float(band_out)]) + else: + # convert everything to tensors if not transformed + img = img.float() + fx_tensor = torch.FloatTensor([float(fx.item() if isinstance(fx, torch.Tensor) else fx)]) + fy_tensor = torch.FloatTensor([float(fy.item() if isinstance(fy, torch.Tensor) else fy)]) + intra_tensor = torch.FloatTensor( + [float(intra.item() if isinstance(intra, torch.Tensor) else intra)] + ) + band_tensor = torch.FloatTensor([float(band)]) # Apply image shifting only in train mode and when adjustment_factor > 0 if self.settings["mode"] == "train" and self.adjustment_factor > 0: - img, offset_amount = shift_offcenter( - img, adjust=self.adjustment_factor, return_offset=True - ) + img, offset_amount = shift_offcenter(img, adjust=self.adjustment_factor, return_offset=True) # Add offset information to output - offset_vec = ( - np.array(np.array(offset_amount).astype(np.float32)) - / self.adjustment_factor - ) + offset_vec = np.array(np.array(offset_amount).astype(np.float32)) / self.adjustment_factor offset_r = np.sqrt(offset_amount[0] ** 2 + offset_amount[1] ** 2) offset_r = np.array(offset_r.astype(np.float32)) / self.adjustment_factor else: offset_amount = [0, 0] offset_vec = np.array([0.0, 0.0]) offset_r = np.array(0.0) - - # get meta data - fx = torch.FloatTensor([fx]) - fy = torch.FloatTensor([fy]) - intra = torch.FloatTensor([intra]) # type: ignore - band = torch.FloatTensor([band]) # type: ignore zernikes = zernikes.float()[0, :] output = { "image": img, - "field_x": fx, - "field_y": fy, - "intrafocal": intra, - "band": band, + "field_x": fx_tensor, + "field_y": fy_tensor, + "intrafocal": intra_tensor, + "band": band_tensor, "zernikes": zernikes, "offset": offset_amount, "offset_vec": offset_vec, @@ -556,6 +598,51 @@ def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: if self.coral_mode: coral_output = self.sample_coral() output.update(coral_output) + + # Apply augmentations if enabled (only in training mode) + if self.augment and self.settings["mode"] == "train": + # Apply frequency-based augmentation 50% of the time + if torch.rand(1).item() > 0.5: + try: + # Randomly sample scale between 0.1 and 1.0 uniformly + random_scale = torch.empty(1).uniform_(0.5, 1.0).item() + # Apply augmentation (function handles device, shape, and dtype internally) + augmented_img = augment_data_torch( + img, coral_output["coral_image"], scale=random_scale, kmin=self.augment_kmin + ) + + # Ensure augmented image matches original image's dtype and device + augmented_img = augmented_img.to(dtype=img.dtype, device=img.device) + + # Restore original shape if img had extra dimensions + original_shape = img.shape + if augmented_img.shape != original_shape: + # Add back channel/batch dimensions if needed + while len(augmented_img.shape) < len(original_shape): + augmented_img = augmented_img.unsqueeze(0) + + output["image"] = augmented_img + except Exception as e: + # If augmentation fails, use original image and log warning + logger.warning(f"Frequency augmentation failed, using original image: {e}") + # output["image"] remains as img + + # Apply hot pixel augmentation 10% of the time (independent of frequency augmentation) + # This can be applied to the original image or the frequency-augmented image + try: + current_img = output.get("image", img) + hot_pixel_img = add_random_hot_pixel( + current_img, sigma=0.05, prob=0.1, min_scale=5.0, max_scale=20 + ) + # Ensure dtype and device match (function preserves device, but ensure dtype consistency) + if hot_pixel_img.dtype != img.dtype: + hot_pixel_img = hot_pixel_img.to(dtype=img.dtype) + output["image"] = hot_pixel_img + except Exception as e: + # If hot pixel augmentation fails, keep current image and log warning + logger.warning(f"Hot pixel augmentation failed: {e}") + # output["image"] remains as current_img + return output @@ -585,6 +672,10 @@ class zernikeDataset(Dataset): return_true : bool, optional, default=False Whether to return the true Zernike coefficients (`True`) or the estimated coefficients (`False`). + coral_mode : bool, optional, default=False + Whether to enable coral mode for sampling real data alongside simulations. + coral_filepath : str, optional, default='.../LSST_FULL_FRAME/aggregator_real/' + Path to the directory containing real/coral aggregator data files. Attributes ---------- @@ -621,6 +712,8 @@ def __init__( data_dir="/media/peterma/mnt2/peterma/research/LSST_FULL_FRAME/aggregator/", alpha=1e-3, return_true=False, + coral_mode=False, + coral_filepath="/media/peterma/mnt2/peterma/research/LSST_FULL_FRAME/aggregator_real/", ): """Initialize the zernikeDataset. @@ -636,13 +729,20 @@ def __init__( Parameter used for adjusting Zernike coefficients during processing. return_true : bool, optional, default=False Whether to return the true Zernike coefficients or estimated coefficients. + coral_mode : bool, optional, default=False + Whether to enable coral mode for real data sampling. + coral_filepath : str, optional + Path to the real/coral aggregator dataset directory. """ self.max_seq_length = seq_length + self.coral_mode = coral_mode + self.coral_filepath = coral_filepath + # Loop through all files and subdirectories if train: - self.image_dir = data_dir + '/train' + self.image_dir = data_dir + "/train" else: - self.image_dir = data_dir + '/train' + self.image_dir = data_dir + "/train" self.filename = [] for root, _, files in os.walk(self.image_dir): @@ -653,18 +753,158 @@ def __init__( if train: self.filename = self.filename[: int(0.8 * len(self.filename))] else: - self.filename = self.filename[int(0.8 * len(self.filename)):] + self.filename = self.filename[int(0.8 * len(self.filename)) :] self.num_samples = len(self.filename) self.alpha = alpha self.return_true = return_true - self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - def __len__(self): + # Load coral files if coral_mode is enabled + if self.coral_mode: + self.coral_files = [] + coral_data_path = coral_filepath + if train: + coral_data_path += "/train" + else: + coral_data_path += "/val" + + for root, _, files in os.walk(coral_data_path): + for file in files: + file_path = os.path.join(root, file) + self.coral_files.append(file_path) + + logger.info(f"Loaded {len(self.coral_files)} coral aggregator files from {coral_data_path}") + + # Always use CPU in dataset - PyTorch Lightning handles GPU transfer + # This avoids CUDA reinitialization issues with num_workers > 0 + self.device = torch.device("cpu") + + def __len__(self) -> int: """Return the number of samples in the dataset.""" return self.num_samples - def __getitem__(self, idx): + def sample_coral(self) -> Dict[str, Any]: + """Sample a coral (real) aggregator data sample randomly. + + Returns + ------- + dict + Dictionary containing: + - coral_x_total: Input features tensor (seq_length, features) + - coral_mean: Mean Zernike coefficients + - coral_filter: Filter name + - coral_raftbay: Raft bay sensor name + """ + # Check if coral files are available + if not self.coral_files or len(self.coral_files) == 0: + raise RuntimeError("No coral aggregator files available for sampling.") + + # Randomly sample from coral files with retry for corrupted files + max_retries = 10 + corrupted_files = [] + + for attempt in range(max_retries): + try: + # Re-check availability in case files were deleted + if not self.coral_files: + raise RuntimeError("All coral aggregator files have been removed due to corruption.") + + idx = np.random.randint(0, len(self.coral_files)) + coral_file = self.coral_files[idx] + + # Skip already identified corrupted files + if coral_file in corrupted_files: + continue + + # Load the coral data from npz + npz_data = np.load(coral_file, allow_pickle=True) + + # Reconstruct dictionary - split stacked arrays back into lists + loaded_data = { + "estimated_zk": [torch.from_numpy(arr) for arr in npz_data["estimated_zk"]], + "zk_mean": torch.from_numpy(npz_data["zk_mean"]), + "field_x": [torch.from_numpy(arr) for arr in npz_data["field_x"]], + "field_y": [torch.from_numpy(arr) for arr in npz_data["field_y"]], + "snr": npz_data["snr"].tolist(), + "header": json.loads(str(npz_data["header_json"])), + } + break # Successfully loaded, exit retry loop + except (pickle.UnpicklingError, EOFError, IOError, OSError) as e: + # File is corrupted, truncated, or missing - delete it + logger.warning(f"Corrupted coral file detected: {coral_file}. Error: {e}. Deleting...") + try: + if os.path.exists(coral_file): + os.remove(coral_file) + logger.info(f"Deleted corrupted file: {coral_file}") + except (OSError, PermissionError) as delete_error: + logger.warning(f"Failed to delete {coral_file}: {delete_error}") + + # Remove from list to avoid trying again + if coral_file in self.coral_files: + self.coral_files.remove(coral_file) + corrupted_files.append(coral_file) + + if attempt == max_retries - 1: + # Last attempt failed, raise the error + raise RuntimeError( + f"Failed to load coral file after {max_retries} attempts. " + f"Last error: {e}. All coral files may be corrupted." + ) + # Try another random file + continue + + # Process coral data similar to __getitem__ + x = torch.stack(loaded_data["estimated_zk"]).to(self.device) / 1000 + mean = loaded_data["zk_mean"].to(self.device) + + # Track the field x/y in degrees + field_x = torch.stack(loaded_data["field_x"]) + field_y = torch.stack(loaded_data["field_y"]) + + # Load the SNR values + normalize + snr = ( + torch.tensor(loaded_data["snr"]).to(self.device)[..., None] + / torch.tensor(loaded_data["snr"]).max() + ) + + # Combine the field x/y + position = torch.concatenate([field_x, field_y], dim=-1).to(self.device) + + # Combine all into one array as an embedding + x = x.squeeze(1) # [seq_length, features] + position = position.squeeze(1) # [seq_length, 2] + x_total = torch.cat([x, position, snr], dim=1) + + # Control padding the sequence + idx_tensor = torch.randperm(x_total.size(0)) + x_total = x_total[idx_tensor] + + if x_total.shape[0] > self.max_seq_length: + x_total = x_total[: self.max_seq_length, :] + else: + padding = torch.zeros((self.max_seq_length - x_total.shape[0], x_total.shape[1])).to(self.device) + x_total = torch.cat([x_total, padding], dim=0).to(self.device).float() + + # Extract filter and raftbay info + filter_name = loaded_data["header"].get("FILTER", "unknown") + if isinstance(filter_name, str): + filter_name = filter_name.split("_")[0] + + raftbay = loaded_data["header"].get("RAFTBAY", "UNKNOWN") + "_SW0" + + coral_output = { + "coral_x_total": x_total, + "coral_mean": mean[None, ...], + "coral_filter": filter_name, + "coral_raftbay": raftbay, + } + + return coral_output + + def __getitem__(self, idx: int) -> Union[ + Tuple[torch.Tensor, torch.Tensor, torch.Tensor, str, str], + Tuple[torch.Tensor, torch.Tensor, torch.Tensor, str, str, torch.Tensor, torch.Tensor, str, str], + ]: """Retrieve and process a single sample from the dataset at the specified index. This method loads a data sample from the file at the given index, @@ -691,6 +931,8 @@ def __getitem__(self, idx): Zernike coefficient. - y (torch.Tensor) : A tensor of true Zernike coefficients (ground truth), shaped as `(N,)`. + - filter_name (str) : Filter name extracted from header. + - raftbay (str) : Raft bay sensor name from header. Notes ----- @@ -702,13 +944,26 @@ def __getitem__(self, idx): otherwise, it is padded with zeros to match the specified sequence length. """ - # Load dictionary from file + # Load dictionary from npz file try: - with open(self.filename[idx], "rb") as file: - loaded_data = pickle.load(file) - except Exception: - print(file) - print("error") + npz_data = np.load(self.filename[idx], allow_pickle=True) + + # Reconstruct dictionary - split stacked arrays back into lists + loaded_data = { + "estimated_zk": [torch.from_numpy(arr) for arr in npz_data["estimated_zk"]], + "zk_mean": torch.from_numpy(npz_data["zk_mean"]), + "field_x": [torch.from_numpy(arr) for arr in npz_data["field_x"]], + "field_y": [torch.from_numpy(arr) for arr in npz_data["field_y"]], + "snr": npz_data["snr"].tolist(), + "header": json.loads(str(npz_data["header_json"])), + } + # Add conditional fields if they exist + if "zk_true" in npz_data: + loaded_data["zk_true"] = torch.from_numpy(npz_data["zk_true"]) + except (IOError, OSError, RuntimeError, KeyError) as e: + logger.error( + f"Error loading file {self.filename[idx] if idx < len(self.filename) else 'unknown'}: {e}" + ) raise # convert zernikes microns x = torch.stack(loaded_data["estimated_zk"]).to(self.device) / 1000 @@ -724,25 +979,62 @@ def __getitem__(self, idx): / torch.tensor(loaded_data["snr"]).max() ) # combine the field x/y - position = torch.concatenate([field_x, field_y], axis=-1).to(self.device) + position = torch.concatenate([field_x, field_y], dim=-1).to(self.device) # combine all into one array as an embedding # Remove singleton dimension for correct concatenation - x = x.squeeze(1) # [seq_length, 25] + x = x.squeeze(1) # [seq_length, 25] position = position.squeeze(1) # [seq_length, 2] x_total = torch.cat([x, position, snr], dim=1) # control padding the sequence - idx = torch.randperm(x_total.size(0)) - x_total = x_total[idx] + idx_tensor = torch.randperm(x_total.size(0)) + x_total = x_total[idx_tensor] if x_total.shape[0] > self.max_seq_length: x_total = x_total[: self.max_seq_length, :] else: - padding = torch.zeros( - (self.max_seq_length - x_total.shape[0], x_total.shape[1]) - ).to(self.device) - x_total = torch.cat([x_total, padding], axis=0).to(self.device).float() + padding = torch.zeros((self.max_seq_length - x_total.shape[0], x_total.shape[1])).to(self.device) + x_total = torch.cat([x_total, padding], dim=0).to(self.device).float() y = loaded_data["zk_true"] + + # Prepare output dictionary + output = { + "x_total": x_total, + "mean": mean[None, ...], + "y": y, + "filter": loaded_data["header"]["FILTER"].split("_")[0], + "raftbay": loaded_data["header"]["RAFTBAY"] + "_SW0", + } + + # Sample coral data if coral_mode is enabled + if self.coral_mode: + try: + coral_output = self.sample_coral() + output.update(coral_output) + except RuntimeError as e: + logger.warning(f"Failed to sample coral data: {e}") + # Continue without coral data + # return the stack of embedings, mean zernike estimate and the true zernike in PSF - return x_total, mean[None, ...], y + # Return as tuple for backward compatibility + if self.coral_mode and "coral_x_total" in output: + return ( + output["x_total"], + output["mean"], + output["y"], + output["filter"], + output["raftbay"], + output["coral_x_total"], + output["coral_mean"], + output["coral_filter"], + output["coral_raftbay"], + ) + else: + return ( + output["x_total"], + output["mean"], + output["y"], + output["filter"], + output["raftbay"], + ) # Collate function for padding sequences @@ -759,18 +1051,29 @@ def zk_collate_fn(batch): the sample, shaped as `(1, features)`. - y (torch.Tensor) : True Zernike coefficients (target values) for the sample, shaped as `(1, features)`. + - filter (str) : Filter name. + - raftbay (str) : Raft bay sensor name. + And optionally (if coral_mode=True): + - coral_x_total (torch.Tensor) : Coral input features. + - coral_mean (torch.Tensor) : Coral mean Zernike coefficients. + - coral_filter (str) : Coral filter name. + - coral_raftbay (str) : Coral raft bay sensor name. Returns ------- tuple A tuple containing: - - (x_total, x_mean_total) : + - (x_total, x_mean_total, filter_total, chipid_total, + [coral_x_total, coral_x_mean_total, coral_filter_total, coral_chipid_total]) : - x_total (torch.Tensor) : A tensor of input features for the entire batch, shaped as `(batch_size, seq_length, features)`. - x_mean_total (torch.Tensor) : A tensor of mean Zernike coefficients for the entire batch, shaped as `(batch_size, features)`. + - filter_total (list) : List of filter names. + - chipid_total (list) : List of raft bay sensor names. + - [coral_*_total] : Optional coral data if available. - y_total (torch.Tensor) : - y_total (torch.Tensor) : A tensor of true Zernike coefficients (targets) for the entire batch, @@ -780,14 +1083,67 @@ def zk_collate_fn(batch): ----- - The resulting tensors (`x_total`, `x_mean_total`, and `y_total`) are returned in a format suitable for training a model. + - If coral_mode is enabled, coral data tensors are also included. """ - x_batch, x_mean_batch, y_batch = zip(*batch) + # Check if batch contains coral data (9 elements) or not (5 elements) + has_coral = len(batch[0]) == 9 + + if has_coral: + ( + x_batch, + x_mean_batch, + y_batch, + filter_batch, + chipid_batch, + coral_x_batch, + coral_mean_batch, + coral_filter_batch, + coral_chipid_batch, + ) = zip(*batch) + else: + x_batch, x_mean_batch, y_batch, filter_batch, chipid_batch = zip(*batch) + x_total = torch.zeros((len(x_batch), x_batch[0].shape[0], x_batch[0].shape[1])) y_total = torch.zeros((len(y_batch), y_batch[0].shape[1])) x_mean_total = torch.zeros((len(x_mean_batch), x_mean_batch[0].shape[-1])) + # match the parallel arrays together to get the values - for i, (x, x_mean, y) in enumerate(zip(x_batch, x_mean_batch, y_batch)): + filter_total = [] + chipid_total = [] + + for i, (x, x_mean, y, f, s) in enumerate(zip(x_batch, x_mean_batch, y_batch, filter_batch, chipid_batch)): x_total[i, :, :] = x y_total[i, :] = y[0, :] x_mean_total[i, :] = x_mean[0, 0, :] # <-- fix here - return (x_total, x_mean_total), y_total + filter_total.append(f) + chipid_total.append(s) + + # Process coral data if available + if has_coral: + coral_x_total = torch.zeros( + (len(coral_x_batch), coral_x_batch[0].shape[0], coral_x_batch[0].shape[1]) + ) + coral_x_mean_total = torch.zeros((len(coral_mean_batch), coral_mean_batch[0].shape[-1])) + coral_filter_total = [] + coral_chipid_total = [] + + for i, (cx, cm, cf, cs) in enumerate( + zip(coral_x_batch, coral_mean_batch, coral_filter_batch, coral_chipid_batch) + ): + coral_x_total[i, :, :] = cx + coral_x_mean_total[i, :] = cm[0, 0, :] + coral_filter_total.append(cf) + coral_chipid_total.append(cs) + + return ( + x_total, + x_mean_total, + filter_total, + chipid_total, + coral_x_total, + coral_x_mean_total, + coral_filter_total, + coral_chipid_total, + ), y_total + else: + return (x_total, x_mean_total, filter_total, chipid_total), y_total diff --git a/python/tarts/dataset_params.yaml b/python/tarts/dataset_params.yaml index b10a6cb..be35512 100644 --- a/python/tarts/dataset_params.yaml +++ b/python/tarts/dataset_params.yaml @@ -1,4 +1,5 @@ version: 0 +seed: 42 # Random seed for reproducibility across all training scripts adjustment_WaveNet: 10 adjustment_AlignNet: 120 refinements: 1 @@ -8,6 +9,9 @@ mm_pix : 0.01 alpha: 1 max_seq_len: 200 butler_repo_path: 'butler' + +# Model architecture parameters +cnn_model: 'mobilenetv4_conv_small' # Options: mobilenetv4_conv_small, resnet18, resnet34, resnet50 finetune_filepath: '/media/peterma/mnt2/peterma/research/LSST_FULL_FRAME/simulation_pretrain' finetune_filepath_train: '/media/peterma/mnt2/peterma/research/LSST_FULL_FRAME/simulation_pretrain/train/' finetune_filepath_val: '/media/peterma/mnt2/peterma/research/LSST_FULL_FRAME/simulation_pretrain/val/' @@ -22,13 +26,16 @@ aggregator_val_filepath: '/media/peterma/mnt2/peterma/research/LSST_FULL_FRAME/a fullframe_train_filepath: '/media/peterma/mnt2/peterma/research/LSST_FULL_FRAME/full_plane_ml_opsim_0925/train/' fullframe_val_filepath: '/media/peterma/mnt2/peterma/research/LSST_FULL_FRAME/full_plane_ml_opsim_0925/val/' fullframe_test_filepath: '/media/peterma/mnt2/peterma/research/LSST_FULL_FRAME/full_plane_ml_opsim_0925/test/' +aggregator_real_filepath: "/media/peterma/mnt2/peterma/research/LSST_FULL_FRAME/aggregator_real" +aggregator_real_train_filepath: "/media/peterma/mnt2/peterma/research/LSST_FULL_FRAME/aggregator_real/train" +aggregator_real_val_filepath: "/media/peterma/mnt2/peterma/research/LSST_FULL_FRAME/aggregator_real/val" # AggregatorNet model parameters aggregator_model: - d_model: 28 - nhead: 2 + d_model: 128 + nhead: 4 num_layers: 6 - dim_feedforward: 128 + dim_feedforward: 512 noll_zk: - 4 diff --git a/python/tarts/lightning_alignnet.py b/python/tarts/lightning_alignnet.py index 8b9397a..693b69c 100644 --- a/python/tarts/lightning_alignnet.py +++ b/python/tarts/lightning_alignnet.py @@ -1,13 +1,30 @@ """Wrapping everything for WaveNet in Pytorch Lightning.""" -from typing import Any, Tuple +# Standard library imports +from typing import Any, Dict, Tuple + +# Third-party imports import pytorch_lightning as pl import torch import torch.nn.functional as F from torch.optim.lr_scheduler import ReduceLROnPlateau from torch.utils.data import DataLoader -from .dataloader import Donuts + +# Local/application imports +from .constants import ( + BAND_MEAN, + BAND_STD, + BAND_VALUES_TENSOR, + CAMERA_TYPE, + DEFAULT_INPUT_SHAPE, + DEG_TO_RAD, + FIELD_MEAN, + FIELD_STD, + INTRA_MEAN, + INTRA_STD, +) from .alignnet import AlignNet +from .dataloader import Donuts class DonutLoader(pl.LightningDataModule): @@ -43,9 +60,7 @@ def __init__( super().__init__() self.save_hyperparameters() - def _build_loader( - self, mode: str, shuffle: bool = False, drop_last: bool = True - ) -> DataLoader: + def _build_loader(self, mode: str, shuffle: bool = False, drop_last: bool = True) -> DataLoader: """Build a DataLoader.""" return DataLoader( Donuts(mode=mode, **self.hparams), @@ -81,7 +96,8 @@ def __init__( alpha: float = 0, lr: float = 1e-3, lr_schedule: bool = False, - device='cuda', + weight_decay: float = 1e-4, + device="cuda", pretrained: bool = False, ) -> None: """Initialize the AlignNet model. @@ -93,8 +109,9 @@ def __init__( Parameters ---------- cnn_model : str, optional, default="mobilenetv4_conv_small" - The name of the pre-trained CNN model from torchvision or timm to be used as the feature extractor. - Common options include "resnet18", "resnet34", "mobilenetv4_conv_small", etc. + The name of the pre-trained CNN model from torchvision or timm to be used + as the feature extractor. Common options include "resnet18", "resnet34", + "mobilenetv4_conv_small", etc. freeze_cnn : bool, optional, default=False If True, the CNN weights will be frozen during training, meaning they will not be updated. @@ -108,12 +125,15 @@ def __init__( A value of 0 disables the regularization. lr : float, optional, default=1e-3 - The initial learning rate used by the Adam optimizer. + The initial learning rate used by the AdamW optimizer. lr_schedule : bool, optional, default=False If True, a learning rate scheduler (ReduceLROnPlateau) will be used to adjust the learning rate based on the validation loss during training. + weight_decay : float, optional, default=1e-4 + The weight decay (L2 penalty) coefficient for the AdamW optimizer. + device : str, optional, default='cuda' The device to use for computation ('cuda' or 'cpu'). @@ -128,17 +148,15 @@ def __init__( cnn_model=cnn_model, n_predictor_layers=n_predictor_layers, device=str(self.device_val), - pretrained=pretrained + pretrained=pretrained, ) # define some parameters that will be accessed by # the MachineLearningAlgorithm in ts_wep - self.camType = "LsstCam" - self.inputShape = (160, 160) + self.camType = CAMERA_TYPE + self.inputShape = DEFAULT_INPUT_SHAPE - def predict_step( - self, batch: dict, batch_idx: int - ) -> Tuple[torch.Tensor, torch.Tensor]: + def predict_step(self, batch: Dict[str, Any], batch_idx: int) -> Tuple[torch.Tensor, torch.Tensor]: """Predict Zernikes and return with truth.""" # unpack data from the dictionary img = batch["image"] @@ -152,7 +170,7 @@ def predict_step( return pred_offset, true_offset - def calc_losses(self, batch: dict, batch_idx: int) -> tuple: + def calc_losses(self, batch: Dict[str, Any], batch_idx: int) -> Tuple[torch.Tensor, torch.Tensor]: """Predict Zernikes and calculate the losses. The two losses considered are: @@ -177,7 +195,9 @@ def calc_losses(self, batch: dict, batch_idx: int) -> tuple: return loss, mRSSE - def calc_losses_pure(self, batch: dict, batch_idx: int) -> tuple: + def calc_losses_pure( + self, batch: Dict[str, Any], batch_idx: int + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: """Predict Zernikes and calculate the losses. The two losses considered are: @@ -200,23 +220,25 @@ def calc_losses_pure(self, batch: dict, batch_idx: int) -> tuple: return loss, mRSSE, pred_offset, true_offset - def training_step(self, batch: dict, batch_idx: int) -> torch.Tensor: + def training_step(self, batch: Dict[str, Any], batch_idx: int) -> torch.Tensor: """Execute training step on a batch.""" loss, mRSSE = self.calc_losses(batch, batch_idx) self.log("train_loss", loss, sync_dist=True, prog_bar=True) self.log("train_mRSSE", mRSSE, sync_dist=True) return loss - def validation_step(self, batch: dict, batch_idx: int) -> torch.Tensor: + def validation_step(self, batch: Dict[str, Any], batch_idx: int) -> torch.Tensor: """Execute validation step on a batch.""" loss, mRSSE = self.calc_losses(batch, batch_idx) self.log("val_loss", loss, sync_dist=True, prog_bar=True) self.log("val_mRSSE", mRSSE, sync_dist=True) return loss - def configure_optimizers(self) -> torch.optim.Optimizer: + def configure_optimizers(self) -> Any: """Configure the optimizer.""" - optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.lr) + optimizer = torch.optim.AdamW( + self.parameters(), lr=self.hparams.lr, weight_decay=self.hparams.weight_decay + ) if self.hparams.lr_schedule: return { @@ -239,12 +261,7 @@ def get_band_values(self, bands: torch.Tensor) -> torch.Tensor: Returns: torch.Tensor: A tensor of shape (batch_size, 1) with band values. """ - # Create a tensor with band values - band_values = torch.tensor( - [[0.3671], [0.4827], [0.6223], [0.7546], [0.8691], [0.9712]] - ).to(self.device_val) - - return band_values[bands] + return BAND_VALUES_TENSOR.to(self.device_val)[bands] def rescale_image_batched(self, data: torch.Tensor) -> torch.Tensor: """Rescale batched image data with normalization. @@ -281,11 +298,11 @@ def rescale_image_batched(self, data: torch.Tensor) -> torch.Tensor: data = data - min_vals data = data / (max_vals + 1e-8) means = data.view(data.shape[0], -1).mean(dim=1) # shape: [n] - stds = data.view(data.shape[0], -1).std(dim=1) # shape: [n] + stds = data.view(data.shape[0], -1).std(dim=1) # shape: [n] # Reshape for proper broadcasting with 4D tensor means = means.view(-1, 1, 1) # shape: [n, 1, 1, 1] - stds = stds.view(-1, 1, 1) # shape: [n, 1, 1, 1] + stds = stds.view(-1, 1, 1) # shape: [n, 1, 1, 1] data = (data - means) / (stds + 1e-8) return data @@ -307,25 +324,19 @@ def forward( img = self.rescale_image_batched(img) # convert angles to radians - fx *= torch.pi / 180 - fy *= torch.pi / 180 + fx *= DEG_TO_RAD + fy *= DEG_TO_RAD # normalize angles - field_mean = 0.000 - field_std = 0.021 - fx = (fx - field_mean) / field_std - fy = (fy - field_mean) / field_std + fx = (fx - FIELD_MEAN) / FIELD_STD + fy = (fy - FIELD_MEAN) / FIELD_STD # normalize the intrafocal flags - intra_mean = 0.5 - intra_std = 0.5 - focalFlag = (focalFlag - intra_mean) / intra_std + focalFlag = (focalFlag - INTRA_MEAN) / INTRA_STD band = self.get_band_values(band)[:, 0] # normalize the wavelength - band_mean = 0.710 - band_std = 0.174 - band = (band - band_mean) / band_std + band = (band - BAND_MEAN) / BAND_STD # predict zernikes in microns offset = self.alignnet(img, fx, fy, focalFlag, band) diff --git a/python/tarts/lightning_wavenet.py b/python/tarts/lightning_wavenet.py index d92e82a..70b2997 100644 --- a/python/tarts/lightning_wavenet.py +++ b/python/tarts/lightning_wavenet.py @@ -1,15 +1,36 @@ """Wrapping everything for WaveNet in Pytorch Lightning.""" -from typing import Any, Tuple +# Standard library imports +import logging +from typing import Any, Dict, Tuple + +# Third-party imports import pytorch_lightning as pl import torch import torch.nn.functional as F from torch.optim.lr_scheduler import ReduceLROnPlateau from torch.utils.data import DataLoader + +# Local/application imports +from .constants import ( + BAND_MEAN, + BAND_STD, + BAND_VALUES_TENSOR, + CAMERA_TYPE, + DEFAULT_INPUT_SHAPE, + DEG_TO_RAD, + FIELD_MEAN, + FIELD_STD, + INTRA_MEAN, + INTRA_STD, + ZERNIKE_SCALE_FACTOR, +) from .dataloader import Donuts, Donuts_Fullframe from .utils import convert_zernikes_deploy from .wavenet import WaveNet +logger = logging.getLogger(__name__) + class DonutLoader(pl.LightningDataModule): """Pytorch Lightning wrapper for the simulated Donuts DataSet.""" @@ -45,9 +66,7 @@ def __init__( self.save_hyperparameters() self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - def _build_loader( - self, mode: str, shuffle: bool = False, drop_last: bool = True - ) -> DataLoader: + def _build_loader(self, mode: str, shuffle: bool = False, drop_last: bool = True) -> DataLoader: """Build a DataLoader.""" return DataLoader( Donuts(mode=mode, **self.hparams), @@ -105,9 +124,7 @@ def __init__( super().__init__() self.save_hyperparameters() - def _build_loader( - self, mode: str, shuffle: bool = False, drop_last: bool = True - ) -> DataLoader: + def _build_loader(self, mode: str, shuffle: bool = False, drop_last: bool = True) -> DataLoader: """Build a DataLoader.""" return DataLoader( Donuts_Fullframe(mode=mode, **self.hparams), @@ -144,7 +161,8 @@ def __init__( alpha: float = 0, lr: float = 1e-3, lr_schedule: bool = False, - device: str = 'cuda', + weight_decay: float = 1e-4, + device: str = "cuda", pretrained: bool = False, ) -> None: """Create the WaveNet. @@ -152,8 +170,9 @@ def __init__( Parameters ---------- cnn_model: str, default="resnet34" - The name of the pre-trained CNN model from torchvision or timm. - Supports both torchvision models (e.g., "resnet34") and timm models (e.g., "mobilenetv4_conv_small"). + The name of the pre-trained CNN model from torchvision or timm. Supports + both torchvision models (e.g., "resnet34") and timm models + (e.g., "mobilenetv4_conv_small"). freeze_cnn: bool, default=False Whether to freeze the CNN weights. n_predictor_layers: tuple, default=(256) @@ -164,9 +183,11 @@ def __init__( alpha: float, default=0 Weight for the L2 penalty. lr: float, default=1e-3 - The initial learning rate for Adam. + The initial learning rate for AdamW. lr_schedule: bool, default=True Whether to use the ReduceLROnPlateau learning rate scheduler. + weight_decay: float, default=1e-4 + The weight decay (L2 penalty) coefficient for the AdamW optimizer. device: str, default='cuda' The device to use for computation ('cuda' or 'cpu'). pretrained: bool, default=False @@ -185,12 +206,10 @@ def __init__( # define some parameters that will be accessed by # the MachineLearningAlgorithm in ts_wep - self.camType = "LsstCam" - self.inputShape = (160, 160) + self.camType = CAMERA_TYPE + self.inputShape = DEFAULT_INPUT_SHAPE - def predict_step( - self, batch: dict, batch_idx: int - ) -> Tuple[torch.Tensor, torch.Tensor]: + def predict_step(self, batch: Dict[str, Any], batch_idx: int) -> Tuple[torch.Tensor, torch.Tensor]: """Predict Zernikes and return with truth.""" # unpack data from the dictionary img = batch["image"] @@ -198,7 +217,7 @@ def predict_step( fy = batch["field_y"] intra = batch["intrafocal"] band = batch["band"] - zk_true = batch["zernikes"].cuda() + zk_true = batch["zernikes"].to(self.device_val) # dof_true = batch["dof"] # noqa: F841 # predict zernikes @@ -206,7 +225,7 @@ def predict_step( return zk_pred, zk_true - def calc_losses(self, batch: dict, batch_idx: int) -> tuple: + def calc_losses(self, batch: Dict[str, Any], batch_idx: int) -> Tuple[torch.Tensor, torch.Tensor]: """Predict Zernikes and calculate the losses. The two losses considered are: @@ -216,24 +235,46 @@ def calc_losses(self, batch: dict, batch_idx: int) -> tuple: The mRSSE provides an estimate of the PSF degradation. """ - # predict zernikes - zk_pred, zk_true = self.predict_step(batch, batch_idx) - - # convert to FWHM contributions - zk_pred = convert_zernikes_deploy(zk_pred) - zk_true = convert_zernikes_deploy(zk_true) - - # pull out the weights from the final linear layer - *_, A, _ = self.wavenet.predictor.parameters() - - # calculate loss - sse = F.mse_loss(zk_pred, zk_true, reduction="none").sum(dim=-1) - loss = sse.mean() + self.hparams.alpha * A.square().sum() - mRSSE = torch.sqrt(sse).mean() - - return loss, mRSSE - - def calc_losses_pure(self, batch: dict, batch_idx: int) -> tuple: + try: + # predict zernikes + zk_pred, zk_true = self.predict_step(batch, batch_idx) + + # Check for NaN or Inf values in predictions/truth + if torch.any(torch.isnan(zk_pred)) or torch.any(torch.isnan(zk_true)): + logger.warning("NaN detected in predictions or truth values, returning zero loss") + return torch.tensor(0.0, device=self.device_val), torch.tensor(0.0, device=self.device_val) + if torch.any(torch.isinf(zk_pred)) or torch.any(torch.isinf(zk_true)): + logger.warning("Inf detected in predictions or truth values, returning zero loss") + return torch.tensor(0.0, device=self.device_val), torch.tensor(0.0, device=self.device_val) + + # convert to FWHM contributions + zk_pred = convert_zernikes_deploy(zk_pred) + zk_true = convert_zernikes_deploy(zk_true) + + # pull out the weights from the final linear layer + *_, A, _ = self.wavenet.predictor.parameters() + + # calculate loss + sse = F.mse_loss(zk_pred, zk_true, reduction="none").sum(dim=-1) + loss = sse.mean() + self.hparams.alpha * A.square().sum() + mRSSE = torch.sqrt(sse).mean() + + # Check for NaN or Inf in computed losses + if torch.any(torch.isnan(loss)) or torch.any(torch.isinf(loss)): + logger.warning("NaN/Inf detected in computed loss, returning zero loss") + return torch.tensor(0.0, device=self.device_val), torch.tensor(0.0, device=self.device_val) + if torch.any(torch.isnan(mRSSE)) or torch.any(torch.isinf(mRSSE)): + logger.warning("NaN/Inf detected in computed mRSSE, returning zero mRSSE") + mRSSE = torch.tensor(0.0, device=self.device_val) + + return loss, mRSSE + except (RuntimeError, ValueError, IndexError) as e: + logger.warning(f"Error in calc_losses: {e}, returning zero loss") + return torch.tensor(0.0, device=self.device_val), torch.tensor(0.0, device=self.device_val) + + def calc_losses_pure( + self, batch: Dict[str, Any], batch_idx: int + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: """Predict Zernikes and calculate the losses. The two losses considered are: @@ -243,24 +284,57 @@ def calc_losses_pure(self, batch: dict, batch_idx: int) -> tuple: The mRSSE provides an estimate of the PSF degradation. """ - # predict zernikes - zk_pred, zk_true = self.predict_step(batch, batch_idx) - - # convert to FWHM contributions - zk_pred = convert_zernikes_deploy(zk_pred, device=self.device) - zk_true = convert_zernikes_deploy(zk_true, device=self.device) - - # pull out the weights from the final linear layer - *_, A, _ = self.wavenet.predictor.parameters() - - # calculate loss - sse = F.mse_loss(zk_pred, zk_true, reduction="none").sum(dim=-1) - loss = sse.mean() + self.hparams.alpha * A.square().sum() - mRSSE = torch.sqrt(sse).mean() - - return loss, mRSSE, zk_pred, zk_true - - def training_step(self, batch: dict, batch_idx: int) -> torch.Tensor: + try: + # predict zernikes + zk_pred, zk_true = self.predict_step(batch, batch_idx) + + # Check for NaN or Inf values in predictions/truth + if torch.any(torch.isnan(zk_pred)) or torch.any(torch.isnan(zk_true)): + logger.warning("NaN detected in predictions or truth values, returning zero loss") + zero_tensor = torch.tensor(0.0, device=self.device_val) + return zero_tensor, zero_tensor, zk_pred, zk_true + if torch.any(torch.isinf(zk_pred)) or torch.any(torch.isinf(zk_true)): + logger.warning("Inf detected in predictions or truth values, returning zero loss") + zero_tensor = torch.tensor(0.0, device=self.device_val) + return zero_tensor, zero_tensor, zk_pred, zk_true + + # convert to FWHM contributions + zk_pred = convert_zernikes_deploy(zk_pred) + zk_true = convert_zernikes_deploy(zk_true) + + # pull out the weights from the final linear layer + *_, A, _ = self.wavenet.predictor.parameters() + + # calculate loss + sse = F.mse_loss(zk_pred, zk_true, reduction="none").sum(dim=-1) + loss = sse.mean() + self.hparams.alpha * A.square().sum() + mRSSE = torch.sqrt(sse).mean() + + # Check for NaN or Inf in computed losses + if torch.any(torch.isnan(loss)) or torch.any(torch.isinf(loss)): + logger.warning("NaN/Inf detected in computed loss, returning zero loss") + zero_tensor = torch.tensor(0.0, device=self.device_val) + return zero_tensor, zero_tensor, zk_pred, zk_true + if torch.any(torch.isnan(mRSSE)) or torch.any(torch.isinf(mRSSE)): + logger.warning("NaN/Inf detected in computed mRSSE, returning zero mRSSE") + mRSSE = torch.tensor(0.0, device=self.device_val) + + return loss, mRSSE, zk_pred, zk_true + except (RuntimeError, ValueError, IndexError) as e: + logger.warning(f"Error in calc_losses_pure: {e}, returning zero loss") + zero_tensor = torch.tensor(0.0, device=self.device_val) + # Return zero loss but preserve predictions for debugging + try: + zk_pred, zk_true = self.predict_step(batch, batch_idx) + return zero_tensor, zero_tensor, zk_pred, zk_true + except Exception: + # If even predict_step fails, create dummy tensors + dummy_shape = (batch.get("zernikes", torch.tensor([[]])).shape[0],) + dummy_pred = torch.zeros(dummy_shape, device=self.device_val) + dummy_true = torch.zeros(dummy_shape, device=self.device_val) + return zero_tensor, zero_tensor, dummy_pred, dummy_true + + def training_step(self, batch: Dict[str, Any], batch_idx: int) -> torch.Tensor: """Execute training step on a batch.""" loss, mRSSE = self.calc_losses(batch, batch_idx) self.log("train_loss", loss, sync_dist=True, prog_bar=True) @@ -268,7 +342,7 @@ def training_step(self, batch: dict, batch_idx: int) -> torch.Tensor: return loss - def validation_step(self, batch: dict, batch_idx: int) -> torch.Tensor: + def validation_step(self, batch: Dict[str, Any], batch_idx: int) -> torch.Tensor: """Execute validation step on a batch.""" loss, mRSSE = self.calc_losses(batch, batch_idx) self.log("val_loss", loss, sync_dist=True, prog_bar=True) @@ -276,9 +350,11 @@ def validation_step(self, batch: dict, batch_idx: int) -> torch.Tensor: return loss - def configure_optimizers(self) -> torch.optim.Optimizer: + def configure_optimizers(self) -> Any: """Configure the optimizer.""" - optimizer = torch.optim.AdamW(self.parameters(), lr=self.hparams.lr, weight_decay=1e-4) + optimizer = torch.optim.AdamW( + self.parameters(), lr=self.hparams.lr, weight_decay=self.hparams.weight_decay + ) # optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.lr) if self.hparams.lr_schedule: @@ -302,17 +378,7 @@ def get_band_values(self, bands: torch.Tensor) -> torch.Tensor: Returns: torch.Tensor: A tensor of shape (batch_size, 1) with band values. """ - # Create a tensor with band values - band_values = torch.tensor([ - [0.3671], - [0.4827], - [0.6223], - [0.7546], - [0.8691], - [0.9712] - ]).to(self.device_val) - - return band_values[bands] + return BAND_VALUES_TENSOR.to(self.device_val)[bands] def rescale_image(self, data): """Rescale image data to the range [0, 1]. @@ -367,31 +433,25 @@ def forward( img = self.rescale_image_batched(img) # convert angles to radians - fx *= torch.pi / 180 - fy *= torch.pi / 180 + fx *= DEG_TO_RAD + fy *= DEG_TO_RAD # normalize angles - field_mean = 0.000 - field_std = 0.021 - fx = (fx - field_mean) / field_std - fy = (fy - field_mean) / field_std + fx = (fx - FIELD_MEAN) / FIELD_STD + fy = (fy - FIELD_MEAN) / FIELD_STD # normalize the intrafocal flags - intra_mean = 0.5 - intra_std = 0.5 - focalFlag = (focalFlag - intra_mean) / intra_std + focalFlag = (focalFlag - INTRA_MEAN) / INTRA_STD band = self.get_band_values(band)[:, 0] # U G R I Z Y # normalize the wavelength - band_mean = 0.710 - band_std = 0.174 - band = (band - band_mean) / band_std + band = (band - BAND_MEAN) / BAND_STD # predict zernikes in microns zk_pred = self.wavenet(img, fx, fy, focalFlag, band) # convert to nanometers - zk_pred *= 1_000 + zk_pred *= ZERNIKE_SCALE_FACTOR return zk_pred diff --git a/python/tarts/lightning_wavenet_coral.py b/python/tarts/lightning_wavenet_coral.py index ad833be..472ec20 100644 --- a/python/tarts/lightning_wavenet_coral.py +++ b/python/tarts/lightning_wavenet_coral.py @@ -4,15 +4,36 @@ The method aligns inverse Gram matrices between source and target domains without requiring target labels. """ -from typing import Tuple +# Standard library imports +import logging +from typing import Any, Dict, Tuple + +# Third-party imports import pytorch_lightning as pl import torch import torch.nn as nn import torch.nn.functional as F from torch.optim.lr_scheduler import ReduceLROnPlateau + +# Local/application imports +from .constants import ( + BAND_MEAN, + BAND_STD, + BAND_VALUES_TENSOR, + CAMERA_TYPE, + DEFAULT_INPUT_SHAPE, + DEG_TO_RAD, + FIELD_MEAN, + FIELD_STD, + INTRA_MEAN, + INTRA_STD, + ZERNIKE_SCALE_FACTOR, +) from .utils import convert_zernikes_deploy from .wavenet import WaveNet +logger = logging.getLogger(__name__) + class WaveNetSystem_Coral(pl.LightningModule): """WaveNet with DARE-GRAM domain adaptation. @@ -30,7 +51,8 @@ def __init__( alpha: float = 0, lr: float = 1e-3, lr_schedule: bool = False, - device: str = 'cuda', + weight_decay: float = 1e-4, + device: str = "cuda", pretrained: bool = False, tradeoff_angle: float = 0.05, tradeoff_scale: float = 0.001, @@ -52,9 +74,11 @@ def __init__( alpha: float, default=0 Weight for the L2 penalty. lr: float, default=1e-3 - The initial learning rate for Adam. + The initial learning rate for AdamW. lr_schedule: bool, default=False Whether to use the ReduceLROnPlateau learning rate scheduler. + weight_decay: float, default=1e-4 + The weight decay (L2 penalty) coefficient for the AdamW optimizer. device: str, default='cuda' The device to use for computation ('cuda' or 'cpu'). pretrained: bool, default=False @@ -80,9 +104,9 @@ def __init__( pretrained=pretrained, ) - self.camType = "LsstCam" - self.inputShape = (160, 160) - self.val_mRSSE = None + self.camType = CAMERA_TYPE + self.inputShape = DEFAULT_INPUT_SHAPE + self.val_mRSSE: torch.Tensor | None = None def dare_gram_loss(self, features_source: torch.Tensor, features_target: torch.Tensor) -> torch.Tensor: """Compute DARE-GRAM loss between source and target features. @@ -143,9 +167,9 @@ def dare_gram_loss(self, features_source: torch.Tensor, features_target: torch.T index_A = torch.argwhere(eigen_A <= T_A) if len(index_A) > 0: - index_A = index_A[-1][0] + index_A_val = int(index_A[-1][0].item()) else: - index_A = 1 + index_A_val = 1 if eigen_B[1] > T: T_B = eigen_B[1] @@ -154,11 +178,11 @@ def dare_gram_loss(self, features_source: torch.Tensor, features_target: torch.T index_B = torch.argwhere(eigen_B <= T_B) if len(index_B) > 0: - index_B = index_B[-1][0] + index_B_val = int(index_B[-1][0].item()) else: - index_B = 1 + index_B_val = 1 - k = max(index_A, index_B) + k = max(index_A_val, index_B_val) # Ensure k is within valid range (avoid numerical issues) n_eigen = min(len(L_A), len(L_B)) @@ -179,9 +203,7 @@ def dare_gram_loss(self, features_source: torch.Tensor, features_target: torch.T # Compute cosine similarity for angle alignment cos_sim = nn.CosineSimilarity(dim=0, eps=1e-6) cos_distance = torch.dist( - torch.ones(n_features + 1).to(self.device_val), - cos_sim(A_pinv, B_pinv), - p=1 + torch.ones(n_features + 1).to(self.device_val), cos_sim(A_pinv, B_pinv), p=1 ) / (n_features + 1) # Compute scale alignment loss @@ -199,9 +221,7 @@ def dare_gram_loss(self, features_source: torch.Tensor, features_target: torch.T return dare_gram_loss - def predict_step( - self, batch: dict, batch_idx: int - ) -> Tuple[torch.Tensor, torch.Tensor]: + def predict_step(self, batch: Dict[str, Any], batch_idx: int) -> Tuple[torch.Tensor, torch.Tensor]: """Predict Zernikes and return with truth.""" img = batch["image"] fx = batch["field_x"] @@ -249,17 +269,26 @@ def exp_rise_flipped(self, loss, a=6.0): f = -f + 1 return f - def calc_losses(self, batch: dict, batch_idx: int, use_coral: bool = False) -> tuple: + def calc_losses( + self, + batch: Dict[str, Any], + batch_idx: int, + use_coral: bool = False, + add_dare_gram_to_loss: bool = True, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """Predict Zernikes and calculate losses with optional DARE-GRAM. Parameters ---------- - batch: dict + batch: Dict[str, Any] Batch of training data. If use_coral=True, should contain coral data. batch_idx: int Batch index. use_coral: bool, default=False Whether to compute DARE-GRAM loss with coral/target data. + add_dare_gram_to_loss: bool, default=True + Whether to add DARE-GRAM loss to the total loss. If False, DARE-GRAM is computed + for logging but not included in the total loss. Returns ------- @@ -310,19 +339,20 @@ def calc_losses(self, batch: dict, batch_idx: int, use_coral: bool = False) -> t # Compute DARE-GRAM loss for logging dare_gram_loss = self.dare_gram_loss(source_features, target_features) - except Exception as e: - print(f"⚠️ DARE-GRAM loss computation failed: {e}") + except (RuntimeError, ValueError, IndexError) as e: + logger.warning(f"DARE-GRAM loss computation failed: {e}") dare_gram_loss = torch.tensor(0.0, device=self.device_val) - if self.val_mRSSE is not None: - scale_loss = self.exp_rise_flipped(self.val_mRSSE) + # Add DARE-GRAM to loss only if requested + if add_dare_gram_to_loss: + scale_loss = self.exp_rise_flipped(self.val_mRSSE if self.val_mRSSE is not None else mRSSE) + total_loss = regression_loss + self.hparams.dare_gram_weight * scale_loss * dare_gram_loss else: - scale_loss = self.exp_rise_flipped(mRSSE) - total_loss = regression_loss + self.hparams.dare_gram_weight * scale_loss * dare_gram_loss + total_loss = regression_loss return total_loss, mRSSE, dare_gram_loss - def training_step(self, batch: dict, batch_idx: int) -> torch.Tensor: + def training_step(self, batch: Dict[str, Any], batch_idx: int) -> torch.Tensor: """Execute training step on a batch.""" loss, mRSSE, dare_gram_loss = self.calc_losses(batch, batch_idx, use_coral=True) self.log("train_loss", loss, sync_dist=True, prog_bar=True) @@ -331,18 +361,24 @@ def training_step(self, batch: dict, batch_idx: int) -> torch.Tensor: return loss - def validation_step(self, batch: dict, batch_idx: int) -> torch.Tensor: + def validation_step(self, batch: Dict[str, Any], batch_idx: int) -> torch.Tensor: """Execute validation step on a batch.""" - # Skip DARE-GRAM during validation for faster evaluation and focus on regression metrics - loss, mRSSE, dare_gram_loss = self.calc_losses(batch, batch_idx, use_coral=False) + # Compute DARE-GRAM for logging but don't add it to validation loss + # This allows monitoring domain adaptation without affecting validation metrics + loss, mRSSE, dare_gram_loss = self.calc_losses( + batch, batch_idx, use_coral=True, add_dare_gram_to_loss=False + ) self.log("val_loss", loss, sync_dist=True, prog_bar=True) self.log("val_mRSSE", mRSSE, sync_dist=True) - self.val_mRSSE = mRSSE + self.log("val_dare_gram_loss", dare_gram_loss, sync_dist=True) + self.val_mRSSE = mRSSE.clone().detach() # Store a copy to avoid tensor reference issues return loss - def configure_optimizers(self) -> torch.optim.Optimizer: + def configure_optimizers(self) -> Any: """Configure the optimizer.""" - optimizer = torch.optim.AdamW(self.parameters(), lr=self.hparams.lr, weight_decay=1e-4) + optimizer = torch.optim.AdamW( + self.parameters(), lr=self.hparams.lr, weight_decay=self.hparams.weight_decay + ) if self.hparams.lr_schedule: return { @@ -358,16 +394,7 @@ def configure_optimizers(self) -> torch.optim.Optimizer: def get_band_values(self, bands: torch.Tensor) -> torch.Tensor: """Retrieve band values for a batch of indices.""" - band_values = torch.tensor([ - [0.3671], - [0.4827], - [0.6223], - [0.7546], - [0.8691], - [0.9712] - ]).to(self.device_val) - - return band_values[bands] + return BAND_VALUES_TENSOR.to(self.device_val)[bands] def rescale_image_batched(self, data): """Rescale batched image data with normalization.""" @@ -398,25 +425,19 @@ def forward( """Predict zernikes for production.""" img = self.rescale_image_batched(img) - fx *= torch.pi / 180 - fy *= torch.pi / 180 + fx *= DEG_TO_RAD + fy *= DEG_TO_RAD - field_mean = 0.000 - field_std = 0.021 - fx = (fx - field_mean) / field_std - fy = (fy - field_mean) / field_std + fx = (fx - FIELD_MEAN) / FIELD_STD + fy = (fy - FIELD_MEAN) / FIELD_STD - intra_mean = 0.5 - intra_std = 0.5 - focalFlag = (focalFlag - intra_mean) / intra_std + focalFlag = (focalFlag - INTRA_MEAN) / INTRA_STD band = self.get_band_values(band)[:, 0] - band_mean = 0.710 - band_std = 0.174 - band = (band - band_mean) / band_std + band = (band - BAND_MEAN) / BAND_STD zk_pred = self.wavenet(img, fx, fy, focalFlag, band) - zk_pred *= 1_000 + zk_pred *= ZERNIKE_SCALE_FACTOR return zk_pred diff --git a/python/tarts/utils.py b/python/tarts/utils.py index 50d30ba..690b72b 100644 --- a/python/tarts/utils.py +++ b/python/tarts/utils.py @@ -1,45 +1,58 @@ """Utility functions.""" + +# Standard library imports +import logging import os +import warnings from pathlib import Path from random import randint +from typing import Any, Dict, Optional, Tuple, cast + import numpy as np -import torch -import torch.nn.functional as F -import matplotlib.pyplot as plt -from torch import nn -# from lsst.summit.utils import ConsDbClient +from numpy.linalg import svd -from .KERNEL import CUTOUT as DONUT +from lsst.ts.ofc import SensitivityMatrix +from lsst.ts.ofc.utils.ofc_data_helpers import get_intrinsic_zernikes +# Third-party imports +import matplotlib.pyplot as plt import pytorch_lightning as pl -from torch.ao.quantization.qconfig import default_qat_qconfig -from typing import Optional +import torch +import torch.nn.functional as F import yaml -import warnings +from torch import nn # Optional imports try: import git + GIT_AVAILABLE = True except ImportError: GIT_AVAILABLE = False git = None -LSST_AVAILABLE = True +# Local/application imports +from .constants import ( + BAND_MAP, + BAND_STR_INT, + BAND_MEAN, + BAND_STD, + DONUT_TEMPLATE, + FIELD_MEAN, + FIELD_POSITIONS, + FIELD_STD, + INTRA_MEAN, + INTRA_STD, + LSST_AVAILABLE, +) + +# Alias for backward compatibility +DONUT = DONUT_TEMPLATE -MAP_DETECTOR_TO_NUMBER = { - 'R00_SW0': 191, - 'R00_SW1': 192, - 'R04_SW0': 195, - 'R04_SW1': 196, - 'R40_SW0': 199, - 'R40_SW1': 200, - 'R44_SW0': 203, - 'R44_SW1': 204, -} +logger = logging.getLogger(__name__) -def safe_yaml_load(file_path: str): +def safe_yaml_load(file_path: str) -> Dict[str, Any]: """Safely load YAML files that may contain Python objects like tuples. Parameters @@ -49,111 +62,45 @@ def safe_yaml_load(file_path: str): Returns ------- - dict + Dict[str, Any] Loaded YAML content as dictionary """ try: # First try standard loader - with open(file_path, 'r') as f: - return yaml.safe_load(f) + with open(file_path, "r") as f: + result: Any = yaml.safe_load(f) + if result is not None and isinstance(result, dict): + return cast(Dict[str, Any], result) + return {} except yaml.YAMLError as e: # If that fails, try with FullLoader which can handle more Python objects try: - with open(file_path, 'r') as f: - return yaml.load(f, Loader=yaml.FullLoader) + with open(file_path, "r") as f: + result2: Any = yaml.load(f, Loader=yaml.FullLoader) + if result2 is not None and isinstance(result2, dict): + return cast(Dict[str, Any], result2) + return {} except yaml.YAMLError: # If both fail, try to load and fix common issues - with open(file_path, 'r') as f: + with open(file_path, "r") as f: content = f.read() # Replace problematic tuple tags with list format - content = content.replace('!!python/tuple', '') - content = content.replace('tag:yaml.org,2002:python/tuple', '') + content = content.replace("!!python/tuple", "") + content = content.replace("tag:yaml.org,2002:python/tuple", "") # Try to load the cleaned content try: - return yaml.safe_load(content) + result3: Any = yaml.safe_load(content) + if result3 is not None and isinstance(result3, dict): + return cast(Dict[str, Any], result3) + return {} except yaml.YAMLError: # Last resort: return empty dict and warn warnings.warn(f"Could not parse YAML file {file_path}: {e}") return {} -class QuantizationAwareTrainingCallback(pl.Callback): - """Generic Callback for Quantization Aware Training (QAT) with PyTorch Lightning. - - Allows specifying the model attribute name (e.g., 'alignnet', 'wavenet'). - """ - def __init__( - self, - model_attr: str = "alignnet", - start_epoch: int = 5, - quantization_backend: str = "fbgemm", - qconfig_dict: Optional[dict] = None - ): - """Initialize the QuantizationAwareTrainingCallback. - - Parameters - ---------- - model_attr : str, default="alignnet" - Name of the model attribute to quantize. - start_epoch : int, default=5 - Epoch to start quantization. - quantization_backend : str, default="fbgemm" - Backend for quantization. - qconfig_dict : Optional[dict], default=None - Quantization configuration dictionary. - """ - super().__init__() - self.model_attr = model_attr - self.start_epoch = start_epoch - self.quantization_backend = quantization_backend - self.qconfig_dict = qconfig_dict - self.qat_enabled = False - - def on_train_epoch_start(self, trainer, pl_module): - """Enable QAT at the start of training epoch.""" - if not self.qat_enabled and trainer.current_epoch >= self.start_epoch: - self._enable_qat(pl_module) - self.qat_enabled = True - - def _enable_qat(self, pl_module): - print(f"🔥 Enabling Quantization Aware Training at epoch {self.start_epoch}") - torch.backends.quantized.engine = self.quantization_backend - - model = getattr(pl_module, self.model_attr) - model.qconfig = default_qat_qconfig - - if self.qconfig_dict: - for module_name, qconfig in self.qconfig_dict.items(): - module = model - for attr in module_name.split('.'): - module = getattr(module, attr) - module.qconfig = qconfig - - torch.quantization.prepare_qat(model, inplace=True) - print("✅ Model prepared for Quantization Aware Training") - - def on_train_end(self, trainer, pl_module): - """Disable QAT at the end of training.""" - if self.qat_enabled: - print("🔧 Converting QAT model to quantized model...") - model = getattr(pl_module, self.model_attr) - model.eval() - quantized_model = torch.quantization.convert(model) - quantized_path = f"{trainer.default_root_dir}/quantized_{self.model_attr}_model.pth" - torch.save({ - 'model_state_dict': quantized_model.state_dict(), - 'model_architecture': type(quantized_model).__name__, - 'quantization_config': self.qconfig_dict, - 'backend': self.quantization_backend, - }, quantized_path) - print(f"💾 Quantized model saved to: {quantized_path}") - original_size = sum(p.numel() * 4 for p in model.parameters()) / 1024 / 1024 - print(f"📊 Original model parameters: {original_size:.2f}MB equivalent") - print("📊 Quantized model should be ~4x smaller for int8 quantization") - - class LearningRateThresholdCallback(pl.Callback): """Stop training when learning rate drops below threshold.""" @@ -178,35 +125,18 @@ def on_train_epoch_end(self, trainer, pl_module): The PyTorch Lightning module being trained. """ # Get current learning rate - current_lr = trainer.optimizers[0].param_groups[0]['lr'] + current_lr = trainer.optimizers[0].param_groups[0]["lr"] if current_lr < self.threshold: - print(f"Learning rate {current_lr:.2e} below threshold {self.threshold:.2e}. Stopping training.") + logger.info( + f"Learning rate {current_lr:.2e} below threshold {self.threshold:.2e}. Stopping training." + ) trainer.should_stop = True -BAND_MAP = { # type: ignore - 0: 0.3671, - 1: 0.4827, - 2: 0.6223, - 3: 0.7546, - 4: 0.8691, - 5: 0.9712, - } -BAND_str_int = { # type: ignore - 'u': 0, - 'g': 1, - 'r': 2, - 'i': 3, - 'z': 4, - 'y': 5, -} -FIELD = { - 'R00': {'fieldx': -1.1897, 'fieldy': -1.1897}, - 'R04': {'fieldx': -1.1897, 'fieldy': 1.1897}, - 'R40': {'fieldx': 1.1897, 'fieldy': -1.1897}, - 'R44': {'fieldx': 1.1897, 'fieldy': 1.1897} -} +# Alias for backward compatibility +BAND_str_int = BAND_STR_INT +FIELD = FIELD_POSITIONS def convert_zernikes(zernikes: torch.Tensor) -> torch.Tensor: @@ -252,9 +182,7 @@ def convert_zernikes(zernikes: torch.Tensor) -> torch.Tensor: return zernikes * arcsec_per_micron -def convert_zernikes_deploy( - zernikes: torch.Tensor, device='cpu' -) -> torch.Tensor: +def convert_zernikes_deploy(zernikes: torch.Tensor, device="cpu") -> torch.Tensor: """Convert zernike units from microns to quadrature contribution to FWHM. Parameters @@ -299,7 +227,7 @@ def convert_zernikes_deploy( 1.26437424, 1.26437424, 0.75241174, - 0.75241174 + 0.75241174, ] ).to(zernikes.device) @@ -366,31 +294,31 @@ def count_parameters(model: torch.nn.Module, trainable: bool = True) -> int: The number of trainable parameters """ if trainable: - return sum( - params.numel() for params in model.parameters() if params.requires_grad - ) + return sum(params.numel() for params in model.parameters() if params.requires_grad) else: return sum(params.numel() for params in model.parameters()) def printOnce(msg: str, header: bool = False) -> None: - """Print message once to the terminal. + """Log message once to avoid duplicate messages in distributed settings. - This avoids the problem where statements get printed multiple times in + This avoids the problem where statements get logged multiple times in a distributed setting. Parameters ---------- msg: str - Message to print + Message to log header: bool, default=False Whether to add extra space and underline for the message """ rank = os.environ.get("LOCAL_RANK", None) if rank is None or rank == "0": if header: - msg = f"\n{msg}\n{'-'*len(msg)}\n" - print(msg) + formatted_msg = f"\n{msg}\n{'-'*len(msg)}\n" + logger.info(formatted_msg) + else: + logger.info(msg) def transform_inputs( @@ -399,7 +327,7 @@ def transform_inputs( fy: float, intra: bool, band: int, -) -> tuple[np.ndarray, float, float, float, float]: +) -> Tuple[np.ndarray, float, float, float, float]: """Transform inputs to the neural network. Parameters @@ -431,23 +359,17 @@ def transform_inputs( image = (image - image_mean) / image_std # normalize angles - field_mean = 0.000 - field_std = 0.021 - fx = (fx - field_mean) / field_std - fy = (fy - field_mean) / field_std + fx = (fx - FIELD_MEAN) / FIELD_STD + fy = (fy - FIELD_MEAN) / FIELD_STD # normalize the intrafocal flags - intra_mean = 0.5 - intra_std = 0.5 - intra = (intra - intra_mean) / intra_std # type: ignore + intra = (intra - INTRA_MEAN) / INTRA_STD # type: ignore # get the effective wavelength in microns band = BAND_MAP[band] # type: ignore # normalize the wavelength - band_mean = 0.710 - band_std = 0.174 - band = (band - band_mean) / band_std # type: ignore + band = (band - BAND_MEAN) / BAND_STD # type: ignore return image, fx, fy, intra, band @@ -462,7 +384,11 @@ def get_root() -> Path: if not GIT_AVAILABLE: # If git is not available, return current working directory return Path.cwd() - root = Path(git.Repo(".", search_parent_directories=True).working_tree_dir) + assert git is not None # Type narrowing for mypy + repo_root = git.Repo(".", search_parent_directories=True).working_tree_dir + if repo_root is None: + return Path.cwd() + root = Path(repo_root) return root @@ -495,10 +421,13 @@ def noise_est(frame): std_2, mean_2 = torch.std_mean(corner_2) std_3, mean_3 = torch.std_mean(corner_3) std_4, mean_4 = torch.std_mean(corner_4) - stds = torch.tensor([std_1, std_2, std_3, std_4]) - means = torch.tensor([mean_1, mean_2, mean_3, mean_4]) + stds = torch.stack([std_1, std_2, std_3, std_4]) + means = torch.stack([mean_1, mean_2, mean_3, mean_4]) ind = torch.argmin(stds) - return stds[ind], means[ind], ind + # Use gather for vmap-friendly indexing + selected_std = torch.gather(stds, 0, ind.unsqueeze(0)).squeeze(0) + selected_mean = torch.gather(means, 0, ind.unsqueeze(0)).squeeze(0) + return selected_std, selected_mean, ind def detect_direction(frame): @@ -552,13 +481,13 @@ def detect_direction(frame): means = torch.tensor([mean_1, mean_2, mean_3, mean_4]) ind = torch.argmax(means) if ind == 0: - return 'up' + return "up" elif ind == 1: - return 'left' + return "left" elif ind == 2: - return 'down' + return "down" else: - return 'right' + return "right" def shift_offcenter(frame, adjust=0, return_offset=True): @@ -597,25 +526,267 @@ def shift_offcenter(frame, adjust=0, return_offset=True): used for the shift will be discarded. """ direction = detect_direction(frame) - if direction == 'left': + if direction == "left": random_x = randint(-adjust, 0) random_y = randint(-adjust, adjust) - elif direction == 'right': + elif direction == "right": random_x = randint(0, adjust) random_y = randint(-adjust, adjust) - elif direction == 'down': + elif direction == "down": random_x = randint(-adjust, adjust) random_y = randint(-adjust, 0) else: random_x = randint(-adjust, adjust) random_y = randint(0, adjust) std, mean, _ = noise_est(frame) - backplate = torch.empty(160*3, 160*3).normal_(mean=mean, std=std) - backplate[160 + random_y:160*2 + random_y, 160+random_x:160*2+random_x] = frame + backplate = torch.empty(160 * 3, 160 * 3).normal_(mean=mean, std=std) + backplate[160 + random_y : 160 * 2 + random_y, 160 + random_x : 160 * 2 + random_x] = frame if return_offset: - return backplate[160:160*2, 160:160*2], [random_x, random_y] + return backplate[160 : 160 * 2, 160 : 160 * 2], [random_x, random_y] else: - return backplate[160:160*2, 160:160*2] + return backplate[160 : 160 * 2, 160 : 160 * 2] + + +def compute_2d_fft_torch(image, pixel_scale=1.0): + """Compute 2D FFT of an image. + + Parameters + ---------- + image : torch.Tensor + Input image as a PyTorch tensor. Can be 2D or 3D (will squeeze if 3D). + pixel_scale : float, default=1.0 + Pixel scale for frequency computation. + + Returns + ------- + tuple + (fshift, kx, ky, magnitude_spectrum, phase_spectrum) + - fshift: Shifted FFT + - kx, ky: Frequency grids + - magnitude_spectrum: Log-magnitude spectrum + - phase_spectrum: Phase spectrum + """ + image = torch.as_tensor(image, dtype=torch.float32) + if image.dim() == 3: + image = image.squeeze(0) + + f = torch.fft.fft2(image) + fshift = torch.fft.fftshift(f) + + ny, nx = image.shape + kx = torch.fft.fftshift(torch.fft.fftfreq(nx, d=pixel_scale)) + ky = torch.fft.fftshift(torch.fft.fftfreq(ny, d=pixel_scale)) + + magnitude_spectrum = torch.log1p(torch.abs(fshift)) + phase_spectrum = torch.angle(fshift) + + return fshift, kx, ky, magnitude_spectrum, phase_spectrum + + +def inverse_2d_fft_torch(fshift): + """Inverse 2D FFT. + + Parameters + ---------- + fshift : torch.Tensor + Shifted FFT coefficients. + + Returns + ------- + torch.Tensor + Reconstructed real-space image. + """ + f_ishift = torch.fft.ifftshift(fshift) + reconstructed = torch.real(torch.fft.ifft2(f_ishift)) + return reconstructed + + +def apply_k_filter_torch(fshift, kx, ky, kmin=0.0, kmax=float("inf"), sigma_frac=0.1): + """Apply smooth circular band-pass filter in Fourier space. + + Parameters + ---------- + fshift : torch.Tensor + Shifted FFT coefficients. + kx : torch.Tensor + Frequency grid in x direction. + ky : torch.Tensor + Frequency grid in y direction. + kmin : float, default=0.0 + Minimum frequency magnitude to keep. + kmax : float, default=float("inf") + Maximum frequency magnitude to keep. + sigma_frac : float, default=0.1 + Fractional Gaussian width for smooth filter edges. + + Returns + ------- + tuple + (f_filtered, weight) + - f_filtered: Filtered FFT coefficients + - weight: Filter weight mask + """ + device = fshift.device + KX, KY = torch.meshgrid(kx.to(device), ky.to(device), indexing="xy") + K_mag = torch.sqrt(KX**2 + KY**2) + kmax_valid = torch.max(K_mag) + kmax = min(kmax, kmax_valid.item()) + + sigma = sigma_frac * (kmax - kmin) + sigma = max(sigma, 1e-6) + + low_edge = 1 / (1 + torch.exp(-(K_mag - kmin) / sigma)) + high_edge = 1 / (1 + torch.exp((K_mag - kmax) / sigma)) + weight = low_edge * high_edge + + f_filtered = fshift * weight + return f_filtered, weight + + +def augment_data_torch(sim, real, scale=0.1, kmin=0.1): + """Augment simulation image by injecting randomized high-frequency structure from real image. + + This function extracts high-frequency components from a real (coral) image, randomizes + their phase, and injects them into the simulation image in regions where the simulation + has signal (above noise threshold). + + Parameters + ---------- + sim : torch.Tensor + Simulation image (2D tensor). + real : torch.Tensor + Real/coral image (2D tensor) to extract high-frequency structure from. + Should have the same shape as sim. + scale : float, default=0.1 + Scaling factor for the injected high-frequency structure. + kmin : float, default=0.1 + Minimum frequency magnitude to extract from real image. + + Returns + ------- + torch.Tensor + Augmented simulation image with injected high-frequency structure. + """ + # Ensure images are 2D and on the same device + sim = torch.as_tensor(sim, dtype=torch.float32) + real = torch.as_tensor(real, dtype=torch.float32) + + # Remove extra dimensions if present + while sim.dim() > 2: + sim = sim.squeeze(0) + while real.dim() > 2: + real = real.squeeze(0) + + # Ensure real image has same shape as sim (crop or pad if needed) + if real.shape != sim.shape: + # Crop or pad to match sim shape + if real.shape[0] > sim.shape[0]: + real = real[: sim.shape[0], :] + elif real.shape[0] < sim.shape[0]: + pad_h = sim.shape[0] - real.shape[0] + # F.pad format for 2D: (pad_left, pad_right, pad_top, pad_bottom) + # Add batch and channel dims for F.pad, then remove them + real = ( + F.pad(real.unsqueeze(0).unsqueeze(0), (0, 0, 0, pad_h), mode="reflect").squeeze(0).squeeze(0) + ) + if real.shape[1] > sim.shape[1]: + real = real[:, : sim.shape[1]] + elif real.shape[1] < sim.shape[1]: + pad_w = sim.shape[1] - real.shape[1] + # F.pad format for 2D: (pad_left, pad_right, pad_top, pad_bottom) + real = ( + F.pad(real.unsqueeze(0).unsqueeze(0), (0, pad_w, 0, 0), mode="reflect").squeeze(0).squeeze(0) + ) + + device = sim.device + real = real.to(device) + + # --- FFT of real image --- + fshift_real, kx, ky, mag_real, _ = compute_2d_fft_torch(real) + f_high, _ = apply_k_filter_torch(fshift_real, kx, ky, kmin=kmin, kmax=float("inf")) + + # --- Randomize phase of high-frequency component --- + random_phase = torch.exp(1j * 2 * torch.pi * torch.rand(f_high.shape, device=device, dtype=torch.float32)) + f_high_randomized = torch.abs(f_high) * random_phase + + # --- Background reconstruction --- + # Avoid division by zero in magnitude spectrum + mag_real_safe = mag_real + 1e-8 + background = inverse_2d_fft_torch(f_high_randomized / mag_real_safe) + background = background.to(device) + + # Normalize background + bg_max = background.abs().max() + if bg_max > 1e-8: + background_norm = background / bg_max + else: + background_norm = background + + # --- Threshold mask on simulation --- + std, mean, ind = noise_est(sim) + mask = sim > (mean + 3 * std) + # --- Combine --- + sim_max = sim.abs().max() + augmented_data = sim + mask.float() * sim_max * background_norm * scale + return augmented_data + + +def add_random_hot_pixel(image, sigma=1.0, prob=0.5, min_scale=5.0, max_scale=20): + """Randomly adds a hot pixel (with small Gaussian spread) to a 2D image tensor. + + Parameters + ---------- + image : torch.Tensor + 2D tensor (H, W) representing the image. + sigma : float + Standard deviation (in pixels) of the Gaussian spread. + prob : float + Probability of applying the augmentation. + min_scale : float + Minimum multiple of the image max value for the hot pixel intensity. + max_scale : float + Maximum multiple of the image max value for the hot pixel intensity. + + Returns + ------- + torch.Tensor + Image with (possibly) one hot pixel added. + """ + if torch.rand(1).item() > prob: + return image # skip augmentation randomly + + # Ensure image is 2D + original_shape = image.shape + while image.dim() > 2: + image = image.squeeze(0) + + H, W = image.shape + + # --- Pick random location --- + y = torch.randint(0, H, (1,), device=image.device).item() + x = torch.randint(0, W, (1,), device=image.device).item() + + # --- Determine random intensity --- + image_max = image.max().item() if image.numel() > 0 else 1.0 + intensity_scale = torch.empty(1, device=image.device).uniform_(min_scale, max_scale).item() + intensity = image_max * intensity_scale + + # --- Build Gaussian hot pixel --- + yy, xx = torch.meshgrid( + torch.arange(H, device=image.device), torch.arange(W, device=image.device), indexing="ij" + ) + gauss = torch.exp(-((xx - x) ** 2 + (yy - y) ** 2) / (2 * sigma**2)) + gauss = gauss / gauss.max() * intensity + + # --- Add to image --- + augmented = image + gauss + + # Restore original shape if needed + if len(original_shape) > 2: + while len(augmented.shape) < len(original_shape): + augmented = augmented.unsqueeze(0) + + return augmented def batched_crop(image_tensor: torch.Tensor, centers: torch.Tensor, crop_size: int) -> torch.Tensor: @@ -636,10 +807,7 @@ def batched_crop(image_tensor: torch.Tensor, centers: torch.Tensor, crop_size: i top = torch.clamp(centers[:, 1] - crop_size // 2, 0, H - crop_size) # Use advanced indexing to extract crops - crops = torch.stack([ - image_tensor[:, t:t + crop_size, l:l + crop_size] - for t, l in zip(top, left) - ]) + crops = torch.stack([image_tensor[:, t : t + crop_size, l : l + crop_size] for t, l in zip(top, left)]) return crops @@ -647,9 +815,13 @@ def batched_crop(image_tensor: torch.Tensor, centers: torch.Tensor, crop_size: i def get_centers(image: torch.Tensor, crop_size: int) -> torch.Tensor: """Generate grid of center coordinates for cropping patches from an image. + This function generates centers using two methods and combines them: + 1. Regular grid-based centers at fixed intervals + 2. Refined centers based on brightest regions using pooling + Parameters ---------- - image : torch.Tensor + image : torch.Tensor or np.ndarray Input image tensor of shape (H, W). crop_size : int Size of square crops to generate centers for. @@ -657,20 +829,62 @@ def get_centers(image: torch.Tensor, crop_size: int) -> torch.Tensor: Returns ------- torch.Tensor - Tensor of shape (N, 2) containing (x, y) center coordinates for non-overlapping crops. + Tensor of shape (N, 2) containing (x, y) center coordinates combining both + grid-based and refined bright-region centers. """ + # Convert to torch tensor if numpy array + if isinstance(image, np.ndarray): + image = torch.from_numpy(image) + H, W = image.shape + + # --- Part 1: Regular grid centers --- x_coords = torch.arange(crop_size // 2, W, crop_size) y_coords = torch.arange(crop_size // 2, H, crop_size) # Create a grid of center coordinates grid_y, grid_x = torch.meshgrid(y_coords, x_coords, indexing="ij") - centers = torch.stack([grid_x.reshape(-1), grid_y.reshape(-1)], dim=1) # Shape: [N, 2] + grid_centers = torch.stack([grid_x.reshape(-1), grid_y.reshape(-1)], dim=1) # Shape: [N, 2] + + # --- Part 2: Refined centers from bright regions --- + img = image.unsqueeze(0).unsqueeze(0) # (1, 1, H, W) + + # Mean pooling to find bright regions + stride = 80 + pooled = F.avg_pool2d(img, kernel_size=160, stride=stride) + + pooled2d = pooled.squeeze(0).squeeze(0) # (H', W') + H_p, W_p = pooled2d.shape + + # Flatten and get top-30 indices + flat = pooled2d.flatten() + values, indices = torch.topk(flat, k=30) - return centers + # Convert flattened indices into pooled coords + coords = torch.stack([indices // W_p, indices % W_p], dim=1) # y in pooled # x in pooled + # Convert pooled coords -> original coords + refined_coords = coords * stride # stride size -def single_conv(image, device='cuda'): + # Filter out points within 160px of any border + y = refined_coords[:, 0] + x = refined_coords[:, 1] + + margin = 160 + mask = (y >= margin) & (y < H - margin) & (x >= margin) & (x < W - margin) + + refined_centers = refined_coords[mask] + + # Swap x and y to match grid_centers format (x, y) + refined_centers = torch.stack([refined_centers[:, 1], refined_centers[:, 0]], dim=1) + + # Combine both sets of centers + all_centers = torch.cat([grid_centers, refined_centers], dim=0) + all_centers = grid_centers + return all_centers + + +def single_conv(image, device="cuda"): """Compute signal-to-noise ratio for donut detection using template matching. Parameters @@ -692,13 +906,13 @@ def single_conv(image, device='cuda'): """ device = image.device # donut = np.loadtxt("/home/peterma/research/Rubin_LSST/Rubin_AO_ML/training/extra_template-R22_S11.txt") - donut = DONUT + donut = DONUT_TEMPLATE donut = donut[40:200, 40:200].float().to(device) - not_donut = (1-donut).bool().float().to(device) + not_donut = (1 - donut).bool().float().to(device) donut_mean = torch.mean(image * donut) not_donut_mean = torch.mean(image * not_donut) not_donut_std = torch.std(image * not_donut) - sigma_dev = abs(donut_mean-not_donut_mean)/not_donut_std + sigma_dev = abs(donut_mean - not_donut_mean) / not_donut_std return sigma_dev @@ -726,12 +940,12 @@ def filter_SNR(images, alpha): img_index.append(i) snr_list.append(ratio) - if len(keep_images) == 0: + if not keep_images: return np.array([[]]), np.array([[]]), np.array([[]]) else: - keep_images = torch.concatenate(keep_images) - img_index = torch.tensor(img_index) - return keep_images, img_index, snr_list + keep_images_tensor = torch.concatenate(keep_images) + img_index_tensor = torch.tensor(img_index) + return keep_images_tensor, img_index_tensor, snr_list class CORALLoss(nn.Module): @@ -740,9 +954,10 @@ class CORALLoss(nn.Module): This loss function aligns the second-order statistics of source and target feature distributions to reduce domain shift. """ + def __init__(self): """Initialize the CORAL loss.""" - super(CORALLoss, self).__init__() + super().__init__() def forward(self, source: torch.Tensor, target: torch.Tensor) -> torch.Tensor: """Forward pass of CORAL loss. @@ -917,26 +1132,44 @@ def getTable(day): def getzk(row): """Get Zernike coefficients.""" - zk4 = row['z4'].tolist() - zk5 = row['z5'].tolist() - zk6 = row['z6'].tolist() - zk7 = row['z7'].tolist() - zk8 = row['z8'].tolist() - zk9 = row['z9'].tolist() - zk10 = row['z10'].tolist() - zk11 = row['z11'].tolist() - zk12 = row['z12'].tolist() - zk13 = row['z13'].tolist() - zk14 = row['z14'].tolist() - zk15 = row['z15'].tolist() - zk20 = row['z20'].tolist() - zk21 = row['z21'].tolist() - zk22 = row['z22'].tolist() - zk27 = row['z27'].tolist() - zk28 = row['z28'].tolist() - table_val = np.stack([zk4, zk5, zk6, zk7, zk8, zk9, zk10, - zk11, zk12, zk13, zk14, zk15, zk20, zk21, - zk22, zk27, zk28]) + zk4 = row["z4"].tolist() + zk5 = row["z5"].tolist() + zk6 = row["z6"].tolist() + zk7 = row["z7"].tolist() + zk8 = row["z8"].tolist() + zk9 = row["z9"].tolist() + zk10 = row["z10"].tolist() + zk11 = row["z11"].tolist() + zk12 = row["z12"].tolist() + zk13 = row["z13"].tolist() + zk14 = row["z14"].tolist() + zk15 = row["z15"].tolist() + zk20 = row["z20"].tolist() + zk21 = row["z21"].tolist() + zk22 = row["z22"].tolist() + zk27 = row["z27"].tolist() + zk28 = row["z28"].tolist() + table_val = np.stack( + [ + zk4, + zk5, + zk6, + zk7, + zk8, + zk9, + zk10, + zk11, + zk12, + zk13, + zk14, + zk15, + zk20, + zk21, + zk22, + zk27, + zk28, + ] + ) return table_val @@ -945,20 +1178,626 @@ def getRealData(butler, cdb_table, ind): if not LSST_AVAILABLE: raise ImportError("LSST dependencies not available. This function requires LSST installation.") - exposure_id = cdb_table['visit_id'][ind] - detector_name = cdb_table['detector'][ind] + exposure_id = cdb_table["visit_id"][ind] + detector_name = cdb_table["detector"][ind] data_id1 = { - "instrument": "LSSTCam", # Replace with your instrument - "exposure": int(exposure_id), # Replace with your exposure ID - "detector": detector_name - } + "instrument": "LSSTCam", # Replace with your instrument + "exposure": int(exposure_id), # Replace with your exposure ID + "detector": detector_name, + } data_id2 = { - "instrument": "LSSTCam", # Replace with your instrument - "exposure": int(exposure_id), # Replace with your exposure ID - "detector": detector_name + 1 - } - data_1 = butler.get('raw', dataId=data_id1, collections="LSSTCam/raw/all") - data_2 = butler.get('raw', dataId=data_id2, collections="LSSTCam/raw/all") - row = cdb_table[(cdb_table['visit_id'] == exposure_id) & (cdb_table['detector'] == detector_name)] + "instrument": "LSSTCam", # Replace with your instrument + "exposure": int(exposure_id), # Replace with your exposure ID + "detector": detector_name + 1, + } + data_1 = butler.get("raw", dataId=data_id1, collections="LSSTCam/raw/all") + data_2 = butler.get("raw", dataId=data_id2, collections="LSSTCam/raw/all") + row = cdb_table[(cdb_table["visit_id"] == exposure_id) & (cdb_table["detector"] == detector_name)] zk = getzk(row) return (data_1, detector_name, zk), (data_2, detector_name + 1, zk) + + +def zernikes_to_dof( + filter_name: str, + measured_zk: np.ndarray, + sensor_names: list[str], + rotation_angle: float, + ofc_data, + trunc_index: int | None = 12, + verbose: bool = True, +): + """Estimate DOF state from measured Zernikes. + + Solves y = A * (W * x_dof), where W is normalization_weights. + + Parameters + ---------- + filter_name : str + Optical filter name. + measured_zk : ndarray + Measured Zernike coefficients [#sensors, #Zernikes]. + sensor_names : list[str] + List of sensors to use. + rotation_angle : float + Rotation angle in degrees. + ofc_data : OfcData + OFC configuration. + trunc_index : int | None + If set, number of singular values to keep in SVD (truncated SVD). + If None, all nonzero singular values are used. + verbose : bool + Print debug diagnostics. + """ + # --- Adjust Zernike range --- + n_zk_meas = measured_zk.shape[1] + zn_idx = np.arange(n_zk_meas) + + # --- Field rotation --- + field_angles = np.array([ofc_data.sample_points[s] for s in sensor_names]) + rot_rad = np.deg2rad(-rotation_angle) + rot_mat = np.array([[np.cos(rot_rad), -np.sin(rot_rad)], [np.sin(rot_rad), np.cos(rot_rad)]]) + field_angles = field_angles @ rot_mat + + # --- Sensitivity matrix (Zernike × DOF) --- + dz_matrix = SensitivityMatrix(ofc_data) + sens = dz_matrix.evaluate(field_angles, 0.0) + sens = sens[:, zn_idx, :].reshape((-1, sens.shape[-1])) + valid_idx = [i for i in ofc_data.dof_idx if i < sens.shape[-1]] + sens = sens[:, valid_idx] + + # --- Apply normalization once (LSST convention) --- + norm_mat = np.diag(ofc_data.normalization_weights[valid_idx]) + sens = sens @ norm_mat + + # --- Build target vector (measured - intrinsic - static) --- + intrinsic = get_intrinsic_zernikes(ofc_data, filter_name, sensor_names, rotation_angle)[:, zn_idx] + y2_corr = np.array([ofc_data.y2_correction[s] for s in sensor_names])[:, zn_idx] + y = (measured_zk - intrinsic - y2_corr).reshape(-1, 1) + + # --- SVD pseudo-inverse with truncation index --- + U, S, Vh = svd(sens, full_matrices=False) + if trunc_index is None: + trunc_index = len(S) # keep all + trunc_index = min(trunc_index, len(S)) # safety + + if verbose: + print(f"Using {trunc_index}/{len(S)} singular values") + + # Zero-out smaller singular values + S_inv = np.zeros_like(S) + S_inv[:trunc_index] = 1.0 / S[:trunc_index] + + # Reconstruct pseudo-inverse + A_pinv = Vh.T @ np.diag(S_inv) @ U.T + + # --- Solve for DOFs --- + x_dof = A_pinv @ y + + if verbose: + print(f"[Z→DOF] sens: {sens.shape}, y: {y.shape}, x_dof: {x_dof.shape}") + print(f"||y||={np.linalg.norm(y):.3f}, ||A@x||={np.linalg.norm(sens @ x_dof):.3f}") + print(f"x_dof range: {x_dof.min():.3f} → {x_dof.max():.3f}") + + return x_dof.ravel() + + +def dof_to_zernikes( + filter_name: str, + x_dof: np.ndarray, + sensor_names: list[str], + rotation_angle: float, + ofc_data, + n_zk_target: int | None = None, + measured_zk: np.ndarray | None = None, + verbose: bool = True, +): + """Predict Zernikes from DOF state (forward model). + + Returns total wavefront = intrinsic + static + misalignment term. + """ + # --- Field rotation --- + field_angles = np.array([ofc_data.sample_points[s] for s in sensor_names]) + rot_rad = np.deg2rad(-rotation_angle) + rot_mat = np.array([[np.cos(rot_rad), -np.sin(rot_rad)], [np.sin(rot_rad), np.cos(rot_rad)]]) + field_angles = field_angles @ rot_mat + + # --- Sensitivity matrix --- + dz_matrix = SensitivityMatrix(ofc_data) + sens = dz_matrix.evaluate(field_angles, 0.0) + n_zk_total = sens.shape[1] + if n_zk_target is None or n_zk_target > n_zk_total: + n_zk_target = n_zk_total + zn_idx = np.arange(n_zk_target) + sens = sens[:, zn_idx, :].reshape((-1, sens.shape[-1])) + sens = sens[:, ofc_data.dof_idx] + + # --- Normalize --- + norm_mat = np.diag(ofc_data.normalization_weights[ofc_data.dof_idx]) + x_dof = x_dof.reshape(-1, 1) + zk_pred = (sens @ norm_mat @ x_dof).reshape(len(sensor_names), n_zk_target) + + # --- Add intrinsic + static --- + intrinsic = get_intrinsic_zernikes(ofc_data, filter_name, sensor_names, rotation_angle) + y2_corr = np.array([ofc_data.y2_correction[s] for s in sensor_names]) + zk_total = intrinsic[:, zn_idx] + y2_corr[:, zn_idx] + zk_pred + + # --- Diagnostics --- + if verbose: + print(f"[DOF→Z] sens: {sens.shape}, x_dof: {x_dof.shape}, zk_total: {zk_total.shape}") + if measured_zk is not None: + n_meas = min(measured_zk.shape[1], n_zk_target) + diff = measured_zk[:, :n_meas] - zk_total[:, :n_meas] + rms_nm = np.sqrt(np.mean(diff**2)) * 1e3 # assuming µm input + print(f"RMS difference (meas vs recon): {rms_nm:.3f} nm") + if rms_nm > 100: + print("⚠️ Warning: Possible unit or normalization mismatch.") + + return zk_total + + +# ============================================================================ +# PyTorch DOF Conversion Functions +# ============================================================================ + +# Global cache for precomputed PyTorch matrices +_PRECOMPUTED_PYTORCH_MATRICES: Optional[Dict[str, Any]] = None + + +def precompute_pytorch_dof_matrices( + ofc_data, + sensor_names: Optional[list[str]] = None, + filter_names: Optional[list[str]] = None, + n_zk_max: int = 28, + device: str = "cpu", +) -> Dict[str, torch.Tensor]: + """Precompute PyTorch matrices for DOF conversion at rotation_angle=0. + + This function precomputes sensitivity matrices, intrinsic Zernikes, and + corrections for each sensor individually at rotation_angle=0. These matrices + can then be used by the PyTorch conversion functions. + + Parameters + ---------- + ofc_data : OFCData + OFC configuration data. + sensor_names : list[str], optional + List of sensor names to precompute. Defaults to ["R00_SW0", "R04_SW0", "R40_SW0", "R44_SW0"]. + filter_names : list[str], optional + List of filter names to precompute intrinsic Zernikes for. + Defaults to ["U", "G", "R", "I", "Z", "Y"]. + n_zk_max : int, default=28 + Maximum number of Zernikes to precompute. + device : str, default="cpu" + Device to store tensors on. + + Returns + ------- + Dict[str, torch.Tensor] + Dictionary containing precomputed matrices: + - 'sensor_sensitivity_matrices': Dict[str, torch.Tensor] - Per-sensor sensitivity matrices + - 'intrinsic_zernikes': Dict[str, Dict[str, torch.Tensor]] - Per-filter, per-sensor intrinsic Zernikes + - 'y2_corrections': Dict[str, torch.Tensor] - Per-sensor y2 corrections + - 'normalization_weights': torch.Tensor - DOF normalization weights + - 'dof_idx': torch.Tensor - DOF indices + - 'n_zk_total': int - Total number of Zernikes + - 'n_dof': int - Number of DOFs + """ + global _PRECOMPUTED_PYTORCH_MATRICES + + if sensor_names is None: + sensor_names = ["R00_SW0", "R04_SW0", "R40_SW0", "R44_SW0"] + if filter_names is None: + filter_names = ["U", "G", "R", "I", "Z", "Y"] + + logger.info(f"Precomputing PyTorch DOF matrices for {len(sensor_names)} sensors at rotation_angle=0") + + # Initialize sensitivity matrix evaluator + dz_matrix = SensitivityMatrix(ofc_data) + + # Get DOF indices and normalization weights + dof_idx_np = np.array(ofc_data.dof_idx) + dof_idx = torch.tensor(ofc_data.dof_idx, dtype=torch.long, device=device) + normalization_weights = torch.tensor(ofc_data.normalization_weights, dtype=torch.float64, device=device) + + # Determine valid DOF indices by checking first sensor + # (all sensors should have same DOF structure) + first_sensor_field = np.array([ofc_data.sample_points[sensor_names[0]]]) + first_sens = dz_matrix.evaluate(first_sensor_field, 0.0) + n_dof_total = first_sens.shape[-1] + valid_dof_mask_np = dof_idx_np < n_dof_total + valid_dof_idx_np = dof_idx_np[valid_dof_mask_np] + valid_dof_mask = torch.tensor(valid_dof_mask_np, dtype=torch.bool, device=device) + valid_dof_idx = dof_idx[valid_dof_mask] + + # Precompute per-sensor sensitivity matrices at rotation_angle=0 + sensor_sensitivity_matrices: Dict[str, torch.Tensor] = {} + sensor_intrinsic_zernikes: Dict[str, Dict[str, torch.Tensor]] = {filt: {} for filt in filter_names} + sensor_y2_corrections: Dict[str, torch.Tensor] = {} + + for sensor_name in sensor_names: + # Get field angle for this sensor (no rotation) + field_angle = np.array([ofc_data.sample_points[sensor_name]]) + + # Evaluate sensitivity matrix for this sensor + sens = dz_matrix.evaluate(field_angle, 0.0) # Shape: (1, n_zk, n_dof) + sens = sens[0, :n_zk_max, :] # Shape: (n_zk_max, n_dof) + + # Select valid DOF indices (use precomputed mask) + sens = sens[:, valid_dof_idx_np] # Shape: (n_zk_max, n_valid_dof) + + # Apply normalization + norm_weights = normalization_weights[valid_dof_mask] + norm_mat = torch.diag(norm_weights) + sens_normalized = torch.tensor(sens, dtype=torch.float64, device=device) @ norm_mat + + # Store per-sensor matrix: (n_zk_max, n_valid_dof) + sensor_sensitivity_matrices[sensor_name] = sens_normalized + + # Precompute intrinsic Zernikes for each filter (at rotation_angle=0) + for filter_name in filter_names: + intrinsic = get_intrinsic_zernikes(ofc_data, filter_name, [sensor_name], 0.0) + sensor_intrinsic_zernikes[filter_name][sensor_name] = torch.tensor( + intrinsic[0, :n_zk_max], dtype=torch.float64, device=device + ) + + # Store y2 correction for this sensor + y2_corr = ofc_data.y2_correction[sensor_name] + sensor_y2_corrections[sensor_name] = torch.tensor( + y2_corr[:n_zk_max], dtype=torch.float64, device=device + ) + + n_zk_total = n_zk_max + n_dof = len(valid_dof_idx) + + result = { + "sensor_sensitivity_matrices": sensor_sensitivity_matrices, + "intrinsic_zernikes": sensor_intrinsic_zernikes, + "y2_corrections": sensor_y2_corrections, + "normalization_weights": normalization_weights, + "dof_idx": dof_idx, + "valid_dof_mask": valid_dof_mask, + "valid_dof_idx": valid_dof_idx, + "n_zk_total": n_zk_total, + "n_dof": n_dof, + "sensor_names": sensor_names, + "filter_names": filter_names, + } + + _PRECOMPUTED_PYTORCH_MATRICES = result + logger.info(f"Precomputation complete: {n_zk_total} Zernikes, {n_dof} DOFs") + + return result + + +def _get_precomputed_matrices() -> Dict[str, Any]: + """Get precomputed matrices, raising error if not initialized.""" + if _PRECOMPUTED_PYTORCH_MATRICES is None: + raise RuntimeError( + "Precomputed matrices not initialized. Call precompute_pytorch_dof_matrices() first." + ) + return _PRECOMPUTED_PYTORCH_MATRICES + + +def _build_rotated_sensitivity_matrix( + sensor_names: list[str], + rotation_angle: float, + ofc_data, + n_zk: int, + device: str = "cpu", +) -> torch.Tensor: + """Build sensitivity matrix for given sensors and rotation angle. + + This function rotates field angles and evaluates the sensitivity matrix. + Used when rotation is needed at runtime. + """ + # Rotate field angles + field_angles = np.array([ofc_data.sample_points[s] for s in sensor_names]) + rot_rad = np.deg2rad(-rotation_angle) + rot_mat = np.array([[np.cos(rot_rad), -np.sin(rot_rad)], [np.sin(rot_rad), np.cos(rot_rad)]]) + field_angles_rotated = field_angles @ rot_mat + + # Evaluate sensitivity matrix + dz_matrix = SensitivityMatrix(ofc_data) + sens = dz_matrix.evaluate(field_angles_rotated, 0.0) # Shape: (n_sensors, n_zk, n_dof) + sens = sens[:, :n_zk, :] # Shape: (n_sensors, n_zk, n_dof) + + # Reshape to (n_sensors * n_zk, n_dof) + n_sensors, n_zk, n_dof_total = sens.shape + sens = sens.reshape(-1, n_dof_total) + + # Select valid DOF indices + valid_idx = [i for i in ofc_data.dof_idx if i < sens.shape[-1]] + sens = sens[:, valid_idx] + + # Apply normalization + norm_mat = np.diag(ofc_data.normalization_weights[valid_idx]) + sens_normalized = sens @ norm_mat + + return torch.tensor(sens_normalized, dtype=torch.float64, device=device) + + +def zernikes_to_dof_torch( + filter_name: str, + measured_zk: torch.Tensor, + sensor_names: list[str], + rotation_angle: float = 0.0, + ofc_data=None, + trunc_index: int | None = 50, + device: str = "cpu", + verbose: bool = True, +) -> torch.Tensor: + """Estimate DOF state from measured Zernikes using Pytorch. + + This function uses precomputed matrices when rotation_angle=0, otherwise + it requires ofc_data to compute rotated matrices. + + Parameters + ---------- + filter_name : str + Optical filter name (e.g., "R", "Z", "I"). + measured_zk : torch.Tensor + Measured Zernike coefficients [#sensors, #Zernikes]. + sensor_names : list[str] + List of sensors to use. Must be a subset of precomputed sensors. + rotation_angle : float, default=0.0 + Rotation angle in degrees. If 0.0, uses precomputed matrices. + ofc_data : OFCData, optional + OFC configuration. Required if rotation_angle != 0.0. + trunc_index : int | None, default=12 + If set, number of singular values to keep in SVD (truncated SVD). + If None, all nonzero singular values are used. + device : str, default="cpu" + Device for computation. + verbose : bool, default=True + Print debug diagnostics. + + Returns + ------- + torch.Tensor + DOF state vector [n_dof]. + """ + # Ensure measured_zk is on correct device and dtype + measured_zk = measured_zk.to(device=device, dtype=torch.float64) + n_sensors, n_zk_meas = measured_zk.shape + + # Handle rotation: if rotation_angle != 0, we need ofc_data to recompute + if abs(rotation_angle) > 1e-6: + if ofc_data is None: + raise ValueError( + "ofc_data is required when rotation_angle != 0. " + "Either set rotation_angle=0 or provide ofc_data." + ) + # Build rotated sensitivity matrix + sens = _build_rotated_sensitivity_matrix(sensor_names, rotation_angle, ofc_data, n_zk_meas, device) + # Get intrinsic and y2 corrections with rotation + intrinsic_list = [] + y2_corr_list = [] + for sensor_name in sensor_names: + intrinsic = get_intrinsic_zernikes(ofc_data, filter_name, [sensor_name], rotation_angle) + intrinsic_list.append(torch.tensor(intrinsic[0, :n_zk_meas], dtype=torch.float64, device=device)) + y2_corr = ofc_data.y2_correction[sensor_name] + y2_corr_list.append(torch.tensor(y2_corr[:n_zk_meas], dtype=torch.float64, device=device)) + intrinsic = torch.stack(intrinsic_list) + y2_corr = torch.stack(y2_corr_list) + else: + # Use precomputed matrices + matrices = _get_precomputed_matrices() + + # Verify sensor_names are in precomputed set + precomputed_sensors = matrices["sensor_names"] + if not all(s in precomputed_sensors for s in sensor_names): + raise ValueError( + f"All sensor_names must be in precomputed set {precomputed_sensors}. " f"Got: {sensor_names}" + ) + + # Build combined sensitivity matrix from precomputed per-sensor matrices + sens_list = [] + intrinsic_list = [] + y2_corr_list = [] + + for sensor_name in sensor_names: + # Get precomputed sensitivity matrix for this sensor + sens_sensor = matrices["sensor_sensitivity_matrices"][sensor_name] + # Select only the Zernikes we need + sens_sensor = sens_sensor[:n_zk_meas, :] + sens_list.append(sens_sensor) + + # Get intrinsic Zernikes for this filter and sensor + intrinsic_sensor = matrices["intrinsic_zernikes"][filter_name.upper()][sensor_name] + intrinsic_list.append(intrinsic_sensor[:n_zk_meas]) + + # Get y2 correction for this sensor + y2_sensor = matrices["y2_corrections"][sensor_name] + y2_corr_list.append(y2_sensor[:n_zk_meas]) + + # Stack: (n_sensors * n_zk_meas, n_dof) + sens = torch.cat(sens_list, dim=0) + intrinsic = torch.stack(intrinsic_list) + y2_corr = torch.stack(y2_corr_list) + + # Build target vector: measured - intrinsic - static + y = (measured_zk - intrinsic - y2_corr).reshape(-1, 1) + + # SVD pseudo-inverse with truncation index + U, S, Vh = torch.linalg.svd(sens, full_matrices=False) + if trunc_index is None: + trunc_index = len(S) # keep all + trunc_index = min(trunc_index, len(S)) # safety + + if verbose: + print(f"Using {trunc_index}/{len(S)} singular values") + + # Zero-out smaller singular values + S_inv = torch.zeros_like(S) + S_inv[:trunc_index] = 1.0 / S[:trunc_index] + + # Reconstruct pseudo-inverse + A_pinv = Vh.T @ torch.diag(S_inv) @ U.T + + # Solve for DOFs + x_dof = A_pinv @ y + + if verbose: + print(f"[Z→DOF] sens: {sens.shape}, y: {y.shape}, x_dof: {x_dof.shape}") + y_norm = torch.linalg.norm(y).item() + Ax_norm = torch.linalg.norm(sens @ x_dof).item() + print(f"||y||={y_norm:.3f}, ||A@x||={Ax_norm:.3f}") + print(f"x_dof range: {x_dof.min().item():.3f} → {x_dof.max().item():.3f}") + + return x_dof.ravel() + + +def dof_to_zernikes_torch( + filter_name: str, + x_dof: torch.Tensor, + sensor_names: list[str], + rotation_angle: float = 0.0, + ofc_data=None, + n_zk_target: int | None = None, + measured_zk: torch.Tensor | None = None, + device: str = "cpu", + verbose: bool = True, +) -> torch.Tensor: + """Predict Zernikes from DOF state using Pytorch (forward model). + + This function uses precomputed matrices when rotation_angle=0, otherwise + it requires ofc_data to compute rotated matrices. + + Parameters + ---------- + filter_name : str + Optical filter name (e.g., "R", "Z", "I"). + x_dof : torch.Tensor + DOF state vector [n_dof] or [n_dof, 1]. + sensor_names : list[str] + List of sensors to use. Must be a subset of precomputed sensors. + rotation_angle : float, default=0.0 + Rotation angle in degrees. If 0.0, uses precomputed matrices. + ofc_data : OFCData, optional + OFC configuration. Required if rotation_angle != 0.0. + n_zk_target : int, optional + Number of Zernikes to predict. Defaults to n_zk_meas or precomputed max. + measured_zk : torch.Tensor, optional + Measured Zernikes for comparison/validation. + device : str, default="cpu" + Device for computation. + verbose : bool, default=True + Print debug diagnostics. + + Returns + ------- + torch.Tensor + Predicted Zernike coefficients [#sensors, #Zernikes]. + """ + # Ensure x_dof is on correct device and dtype + x_dof = x_dof.to(device=device, dtype=torch.float64) + if x_dof.dim() == 1: + x_dof = x_dof.unsqueeze(1) + + # Determine number of Zernikes + # If not specified, infer from measured_zk if provided, otherwise use precomputed max + if n_zk_target is None: + if measured_zk is not None: + n_zk_target = measured_zk.shape[1] + else: + # Default to precomputed max, but this might need adjustment based on actual usage + matrices = _get_precomputed_matrices() + n_zk_target = matrices["n_zk_total"] + # Note: n_zk_target may be adjusted later based on actual sensitivity matrix shape + + # Handle rotation: if rotation_angle != 0, we need ofc_data to recompute + if abs(rotation_angle) > 1e-6: + if ofc_data is None: + raise ValueError( + "ofc_data is required when rotation_angle != 0. " + "Either set rotation_angle=0 or provide ofc_data." + ) + # Build rotated sensitivity matrix + sens = _build_rotated_sensitivity_matrix(sensor_names, rotation_angle, ofc_data, n_zk_target, device) + # Get intrinsic and y2 corrections with rotation + intrinsic_list = [] + y2_corr_list = [] + for sensor_name in sensor_names: + intrinsic = get_intrinsic_zernikes(ofc_data, filter_name, [sensor_name], rotation_angle) + intrinsic_list.append( + torch.tensor(intrinsic[0, :n_zk_target], dtype=torch.float64, device=device) + ) + y2_corr = ofc_data.y2_correction[sensor_name] + y2_corr_list.append(torch.tensor(y2_corr[:n_zk_target], dtype=torch.float64, device=device)) + intrinsic = torch.stack(intrinsic_list) + y2_corr = torch.stack(y2_corr_list) + else: + # Use precomputed matrices + matrices = _get_precomputed_matrices() + + # Verify sensor_names are in precomputed set + precomputed_sensors = matrices["sensor_names"] + if not all(s in precomputed_sensors for s in sensor_names): + raise ValueError( + f"All sensor_names must be in precomputed set {precomputed_sensors}. " f"Got: {sensor_names}" + ) + + # Build combined sensitivity matrix from precomputed per-sensor matrices + sens_list = [] + intrinsic_list = [] + y2_corr_list = [] + + for sensor_name in sensor_names: + # Get precomputed sensitivity matrix for this sensor + sens_sensor = matrices["sensor_sensitivity_matrices"][sensor_name] + # Select only the Zernikes we need + sens_sensor = sens_sensor[:n_zk_target, :] + sens_list.append(sens_sensor) + + # Get intrinsic Zernikes for this filter and sensor + intrinsic_sensor = matrices["intrinsic_zernikes"][filter_name.upper()][sensor_name] + intrinsic_list.append(intrinsic_sensor[:n_zk_target]) + + # Get y2 correction for this sensor + y2_sensor = matrices["y2_corrections"][sensor_name] + y2_corr_list.append(y2_sensor[:n_zk_target]) + + # Stack: (n_sensors * n_zk_target, n_dof) + sens = torch.cat(sens_list, dim=0) + intrinsic = torch.stack(intrinsic_list) + y2_corr = torch.stack(y2_corr_list) + + # Predict Zernikes from DOF: sens @ x_dof + # Infer n_zk from sensitivity matrix shape (more reliable than n_zk_target parameter) + # This handles cases where n_zk_target doesn't match the actual matrix dimensions + n_sensors_actual = len(sensor_names) + n_zk_from_sens = sens.shape[0] // n_sensors_actual + + # Verify the shape makes sense + if sens.shape[0] % n_sensors_actual != 0: + raise ValueError( + f"Sensitivity matrix shape {sens.shape} is not compatible with " + f"{n_sensors_actual} sensors. Expected n_rows to be divisible by n_sensors." + ) + zk_pred_flat = sens @ x_dof # Shape: (n_sensors * n_zk_from_sens, 1) + zk_pred = zk_pred_flat.reshape(n_sensors_actual, n_zk_from_sens) + + # Trim intrinsic and y2_corr to match if needed + if intrinsic.shape[1] > n_zk_from_sens: + intrinsic = intrinsic[:, :n_zk_from_sens] + if y2_corr.shape[1] > n_zk_from_sens: + y2_corr = y2_corr[:, :n_zk_from_sens] + + # Add intrinsic + static corrections + zk_total = intrinsic + y2_corr + zk_pred + + # Diagnostics + if verbose: + print(f"[DOF→Z] sens: {sens.shape}, x_dof: {x_dof.shape}, zk_total: {zk_total.shape}") + print(f"Using {n_sensors_actual} sensors, {n_zk_from_sens} Zernikes per sensor") + if measured_zk is not None: + measured_zk = measured_zk.to(device=device, dtype=torch.float64) + n_meas = min(measured_zk.shape[1], n_zk_from_sens) + n_meas_sensors = min(measured_zk.shape[0], n_sensors_actual) + diff = measured_zk[:n_meas_sensors, :n_meas] - zk_total[:n_meas_sensors, :n_meas] + rms_nm = torch.sqrt(torch.mean(diff**2)).item() * 1e3 # assuming µm input + print(f"RMS difference (meas vs recon): {rms_nm:.3f} nm") + if rms_nm > 100: + print("⚠️ Warning: Possible unit or normalization mismatch.") + + return zk_total diff --git a/python/tarts/wavenet.py b/python/tarts/wavenet.py index 2bb8618..787b48c 100644 --- a/python/tarts/wavenet.py +++ b/python/tarts/wavenet.py @@ -1,93 +1,13 @@ """Neural network to predict zernike coefficients from donut images and positions.""" +# Third-party imports +import timm import torch from torch import nn from torchvision import models as cnn_models -import timm -from .KERNEL import CUTOUT as DONUT -# import torch.nn.functional as F_ # Unused import - -# # Global cache for Gaussian kernels -# _GAUSSIAN_KERNEL_CACHE = {} - -# def get_gaussian_kernel2d(kernel_size: int, sigma: float, device=None, dtype=torch.float32) -> torch.Tensor: -# """ -# Returns a 2D Gaussian kernel as a [1, 1, k, k] tensor for depthwise conv. -# Uses caching to avoid recomputation. - -# Parameters: -# - kernel_size (int): Must be odd. -# - sigma (float): Standard deviation of Gaussian. - -# Returns: -# - kernel (torch.Tensor): Shape [1, 1, k, k]. -# """ -# if kernel_size % 2 == 0: -# raise ValueError("Kernel size must be odd") - -# # Create cache key -# cache_key = (kernel_size, sigma, device, dtype) - -# if cache_key in _GAUSSIAN_KERNEL_CACHE: -# return _GAUSSIAN_KERNEL_CACHE[cache_key] - -# ax = torch.arange(kernel_size, device=device, dtype=dtype) - kernel_size // 2 -# xx, yy = torch.meshgrid(ax, ax, indexing='ij') -# kernel = torch.exp(-(xx**2 + yy**2) / (2 * sigma**2)) -# kernel /= kernel.sum() -# kernel = kernel.unsqueeze(0).unsqueeze(0) # [1, 1, k, k] - -# # Cache the kernel -# _GAUSSIAN_KERNEL_CACHE[cache_key] = kernel -# return kernel - -# def apply_gaussian_blur(image: torch.Tensor, kernel_size: int, sigma: float, donut_mask=None) -> torch.Tensor: -# """ -# Apply 2D Gaussian blur to a 4D PyTorch tensor [B, C, H, W]. -# Optimized version with kernel caching and reduced memory operations. - -# Parameters: -# - image: torch.Tensor - Input image tensor [B, C, H, W] -# - kernel_size: int - Size of Gaussian kernel -# - sigma: float - Standard deviation of Gaussian -# - donut_mask: torch.Tensor, optional - Pre-computed donut mask to avoid repeated computation - -# Returns: -# - torch.Tensor: Blurred image of same shape as input -# """ -# if image.dim() != 4: -# raise ValueError("Input must be a 4D tensor [B, C, H, W]") - -# B, C, H, W = image.shape -# device = image.device -# dtype = image.dtype -# # Get cached kernel -# kernel = get_gaussian_kernel2d(kernel_size, sigma, device, dtype) # [1, 1, k, k] -# kernel = kernel.repeat(C, 1, 1, 1) # [C, 1, k, k] for depthwise conv - -# # Pad to keep image size -# padding = kernel_size // 2 - -# # Use provided donut mask or compute it -# if donut_mask is None: -# donut = DONUT[40:200, 40:200].float().to(device) -# else: -# donut = donut_mask - -# # Optimize the operations: combine operations to reduce memory usage -# # Instead of: back = image - donut * image -# # Use: back = image * (1 - donut) -# back = image * (1 - donut) - -# # Apply blur -# blurred = F_.conv2d(image, kernel, padding=padding, groups=C) - -# # Combine operations: blurred = donut * blurred + back -# # This reduces one memory allocation -# result = donut * blurred + back - -# return result +# Local/application imports +from .KERNEL import CUTOUT as DONUT class WaveNet(nn.Module): @@ -99,7 +19,7 @@ def __init__( freeze_cnn: bool = False, n_predictor_layers: tuple = (256,), n_zernikes: int = 25, - device='cuda', + device="cuda", pretrained: bool = True, ) -> None: """Create the WaveNet. @@ -107,8 +27,9 @@ def __init__( Parameters ---------- cnn_model: str, default="resnet18" - The name of the pre-trained CNN model from torchvision or timm. - Supports both torchvision models (e.g., "resnet18") and timm models (e.g., "mobilenetv4_conv_small"). + The name of the pre-trained CNN model from torchvision or timm. Supports + both torchvision models (e.g., "resnet18") and timm models + (e.g., "mobilenetv4_conv_small"). freeze_cnn: bool, default=False Whether to freeze the CNN weights. n_predictor_layers: tuple, default=(256) @@ -150,7 +71,7 @@ def __init__( nn.Linear(n_features, n_predictor_layers[0]), nn.BatchNorm1d(n_predictor_layers[0]), nn.ReLU(), - nn.Dropout(p=0.2) + nn.Dropout(p=0.2), ] # add any additional layers @@ -158,7 +79,7 @@ def __init__( layers += [ nn.Linear(n_predictor_layers[i - 1], n_predictor_layers[i]), nn.BatchNorm1d(n_predictor_layers[i]), - nn.ReLU() + nn.ReLU(), ] # add the final layer @@ -170,7 +91,10 @@ def __init__( self.predictor = nn.Sequential(*layers).to(self.device_val) # Cache the donut mask to avoid repeated computation - self._donut_mask = None + self._donut_mask: torch.Tensor | None = None + + # Initialize predictor_features for OOD detection + self.predictor_features: torch.Tensor | None = None # Ensure all model parameters are in float32 to avoid dtype mismatches self.float() @@ -188,11 +112,15 @@ def _load_cnn_backbone(self, cnn_model: str, pretrained: bool) -> None: # Check if it's a timm model (MobileNetV4, etc.) if cnn_model.startswith("mobilenetv4") or cnn_model in timm.list_models(): # Load from timm - self.cnn = timm.create_model( - cnn_model, - pretrained=pretrained, - num_classes=0 # This removes the classifier and returns pooled features - ).to(self.device_val).float() # Explicitly convert to float32 + self.cnn = ( + timm.create_model( + cnn_model, + pretrained=pretrained, + num_classes=0, # This removes the classifier and returns pooled features + ) + .to(self.device_val) + .float() + ) # Explicitly convert to float32 self.is_timm_model = True # Get actual feature dimension by doing a dummy forward pass @@ -210,7 +138,10 @@ def _load_cnn_backbone(self, cnn_model: str, pretrained: bool) -> None: else: # Load from torchvision weights_param = "DEFAULT" if pretrained else None - self.cnn = getattr(cnn_models, cnn_model)(weights=weights_param).to(self.device_val).float() # Explicitly convert to float32 + if not hasattr(cnn_models, cnn_model): + raise ValueError(f"Unknown torchvision model: {cnn_model}") + model_fn = getattr(cnn_models, cnn_model) + self.cnn = model_fn(weights=weights_param).to(self.device_val).float() # Get feature dimension self.n_cnn_features = self.cnn.fc.in_features self.is_timm_model = False @@ -222,12 +153,14 @@ def _remove_final_layer(self) -> None: pass else: # For torchvision models, remove the final fully connected layer - if hasattr(self.cnn, 'fc'): - self.cnn.fc = nn.Identity() + # torchvision models always have fc layer + self.cnn.fc = nn.Identity() def _get_donut_mask(self, device): """Get cached donut mask.""" - if self._donut_mask is None or self._donut_mask.device != device: + if self._donut_mask is None: + self._donut_mask = DONUT[40:200, 40:200].float().to(device) + elif self._donut_mask.device != device: self._donut_mask = DONUT[40:200, 40:200].float().to(device) return self._donut_mask @@ -237,15 +170,12 @@ def _reshape_image(self, image: torch.Tensor) -> torch.Tensor: image = image[..., None, :, :] # Get the number of input channels required by the CNN - if hasattr(self.cnn, 'conv1'): - # torchvision models (ResNet, etc.) - n_channels = self.cnn.conv1.in_channels - elif hasattr(self.cnn, 'conv_stem'): + if self.is_timm_model: # timm models (MobileNet, etc.) n_channels = self.cnn.conv_stem.in_channels else: - # Default to 3 channels if we can't determine - n_channels = 3 + # torchvision models (ResNet, etc.) + n_channels = self.cnn.conv1.in_channels # duplicate image for each channel image = image.repeat_interleave(n_channels, dim=-3) diff --git a/requirements.txt b/requirements.txt index 56faac4..a889713 100644 --- a/requirements.txt +++ b/requirements.txt @@ -28,6 +28,8 @@ asttokens async-lru==2.0.4 attrs autograd +black +pre-commit aws-xray-sdk babel==2.16.0 backoff diff --git a/setup.py b/setup.py index 141194a..dc28735 100644 --- a/setup.py +++ b/setup.py @@ -5,6 +5,7 @@ neural network models for processing out-of-focus donut images to predict wavefront aberrations and enable real-time telescope optics correction. """ + from setuptools import setup, find_packages setup(