Sourced from pytorch-lightning's releases.

Pruning & Quantization & SWA

[1.2.0] - 2021-02-18

Added

• Added DataType, AverageMethod and MDMCAverageMethod enum in metrics (#5657)
• Added support for summarized model total params size in megabytes (#5590)
• Added support for multiple train loaders (#1959)
• Added Accuracy metric now generalizes to Top-k accuracy for (multi-dimensional) multi-class inputs using the top_k parameter (#4838)
• Added Accuracy metric now enables the computation of subset accuracy for multi-label or multi-dimensional multi-class inputs with the subset_accuracy parameter (#4838)
• Added HammingDistance metric to compute the hamming distance (loss) (#4838)
• Added max_fpr parameter to auroc metric for computing partial auroc metric (#3790)
• Added StatScores metric to compute the number of true positives, false positives, true negatives and false negatives (#4839)
• Added R2Score metric (#5241)
• Added LambdaCallback (#5347)
• Added BackboneLambdaFinetuningCallback (#5377)
• Accelerator all_gather supports collection (#5221)
• Added image_gradients functional metric to compute the image gradients of a given input image. (#5056)
• Added MetricCollection (#4318)
• Added .clone() method to metrics (#4318)
• Added IoU class interface (#4704)
• Support to tie weights after moving model to TPU via on_post_move_to_device hook
• Added missing val/test hooks in LightningModule (#5467)
• The Recall and Precision metrics (and their functional counterparts recall and precision) can now be generalized to Recall@K and Precision@K with the use of top_k parameter (#4842)
• Added ModelPruning Callback (#5618, #5825, #6045)
• Added PyTorchProfiler (#5560)
• Added compositional metrics (#5464)
• Added Trainer method predict(...) for high performence predictions (#5579)
• Added on_before_batch_transfer and on_after_batch_transfer data hooks (#3671)
• Added AUC/AUROC class interface (#5479)
• Added PredictLoop object (#5752)
• Added QuantizationAwareTraining callback (#5706, #6040)
• Added LightningModule.configure_callbacks to enable the definition of model-specific callbacks (#5621)
• Added dim to PSNR metric for mean-squared-error reduction (#5957)
• Added promxial policy optimization template to pl_examples (#5394)
• Added log_graph to CometLogger (#5295)
• Added possibility for nested loaders (#5404)
• Added sync_step to Wandb logger (#5351)
• Added StochasticWeightAveraging callback (#5640)
• Added LightningDataModule.from_datasets(...) (#5133)
• Added PL_TORCH_DISTRIBUTED_BACKEND env variable to select backend (#5981)
• Added Trainer flag to activate Stochastic Weight Averaging (SWA) Trainer(stochastic_weight_avg=True) (#6038)
• Added DeepSpeed integration (#5954, #6042)

Changed

• Changed stat_scores metric now calculates stat scores over all classes and gains new parameters, in line with the new StatScores metric (#4839)
• Changed computer_vision_fine_tunning example to use BackboneLambdaFinetuningCallback (#5377)
• Changed automatic casting for LoggerConnector metrics (#5218)
• Changed iou [func] to allow float input (#4704)

... (truncated)

Sourced from pytorch-lightning's changelog.

[1.2.0] - 2021-02-18

Added

• Added DataType, AverageMethod and MDMCAverageMethod enum in metrics (#5657
• Added support for summarized model total params size in megabytes (#5590)
• Added support for multiple train loaders (#1959)
• Added Accuracy metric now generalizes to Top-k accuracy for (multi-dimensional) multi-class inputs using the top_k parameter (#4838)
• Added Accuracy metric now enables the computation of subset accuracy for multi-label or multi-dimensional multi-class inputs with the subset_accuracy parameter (#4838)
• Added HammingDistance metric to compute the hamming distance (loss) (#4838)
• Added max_fpr parameter to auroc metric for computing partial auroc metric (#3790)
• Added StatScores metric to compute the number of true positives, false positives, true negatives and false negatives (#4839)
• Added R2Score metric (#5241)
• Added LambdaCallback (#5347)
• Added BackboneLambdaFinetuningCallback (#5377)
• Accelerator all_gather supports collection (#5221)
• Added image_gradients functional metric to compute the image gradients of a given input image. (#5056)
• Added MetricCollection (#4318)
• Added .clone() method to metrics (#4318)
• Added IoU class interface (#4704)
• Support to tie weights after moving model to TPU via on_post_move_to_device hook
• Added missing val/test hooks in LightningModule (#5467)
• The Recall and Precision metrics (and their functional counterparts recall and precision) can now be generalized to Recall@K and Precision@K with the use of top_k parameter (#4842)
• Added ModelPruning Callback (#5618, #5825, #6045)
• Added PyTorchProfiler (#5560)
• Added compositional metrics (#5464)
• Added Trainer method predict(...) for high performence predictions (#5579)
• Added on_before_batch_transfer and on_after_batch_transfer data hooks (#3671)
• Added AUC/AUROC class interface (#5479)
• Added PredictLoop object (#5752)
• Added QuantizationAwareTraining callback (#5706, #6040)
• Added LightningModule.configure_callbacks to enable the definition of model-specific callbacks (#5621)
• Added dim to PSNR metric for mean-squared-error reduction (#5957)
• Added promxial policy optimization template to pl_examples (#5394)
• Added log_graph to CometLogger (#5295)
• Added possibility for nested loaders (#5404)
• Added sync_step to Wandb logger (#5351)
• Added StochasticWeightAveraging callback (#5640)
• Added LightningDataModule.from_datasets(...) (#5133)
• Added PL_TORCH_DISTRIBUTED_BACKEND env variable to select backend (#5981)
• Added Trainer flag to activate Stochastic Weight Averaging (SWA) Trainer(stochastic_weight_avg=True) (#6038)
• Added DeepSpeed integration (#5954, #6042)

Changed

• Changed stat_scores metric now calculates stat scores over all classes and gains new parameters, in line with the new StatScores metric (#4839)

... (truncated)

• 3645eb1 pypi azure badges - tags (#6068)
• e12c8a7 add Azure tags trigger (#6066)
• 4574023 v1.2.0 (#6065)
• 5d6a091 default sched (#6062)
• 8f82823 Raise AttributeError in lightning_getattr and lightning_setattr when attribut...
• b0074a4 Update auto-opt docs (#6037)
• c46c23a v1.2.0rc2 (#6063)
• 2cf39dc Add warnings to on_before/after_batch_transfer hooks (#6059)
• d3a31bc fix docs links (#6057)
• fc9bb53 fix flake8 for new plugins (#5951)
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