Migrate underlying framework from TensorFlow to PyTorch #1266
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* refactor: correct data type for specifying input_size * refactor: replace tf apis w/ torch, add input_size specification * refactor: tensor manipulation and testing for expected shape * refactor: partial working with attention reducer * code: clean out tf commented out code * debug: placehoder code * refactor: use proper Torch container for dependency_reducers * refactor: back-off special handling of attention reducer This will need to revaluated becuase this reducer has parameters that need special handling to support attention reducer initialization.
* Updated GitHub actions * Fixed check * Updated AverageMeter to MeanMetric * Lower bound torchmetrics
…assing test_experiment. (#1443) * WIP for test_experiment. * Fixes to StackedTransformer, image_utils, sequence_feature, and how images are read in test_experiment to address all test failures. * Minor cleanup and test parameterization.
* refactor: removed deprecated sequence encoder test mark decoder related test to be reworked at later date. * test: working version of sequence input feature test
* Remove keras/tf references from test_collect integration tests and detach tensors before serializing to numpy (required by torch). * Fix (and simplify) implementation for collect_weights. * Cleanup. * Fix _get_layers() helper. * Use torch.allclose() to compare tensor values. * Simplify test_collect_activations. * Use named children instead of layers. * Remove import logging
- Removed per module regularization - Added regularization at the ECD-model level - Updated regularization tests to check for end-to-end regularization.
…uropod, and savedmodel (#1452)
- Updated MeanMetric to return a tensor of dimension torch.Size([])
…raining as an additional parameter everywhere. (#1459)
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Continuation of #1155.