lightning/pytorch_lightning/metrics/__init__.py

40 lines
1.1 KiB
Python
Raw Normal View History

2020-10-13 11:18:07 +00:00
# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pytorch_lightning.metrics.metric import Metric, MetricCollection # noqa: F401
from pytorch_lightning.metrics.classification import ( # noqa: F401
Accuracy,
Classification metrics overhaul: accuracy metrics (2/n) (#4838) * Add stuff * Change metrics documentation layout * Add stuff * Change testing utils * Replace len(*.shape) with *.ndim * More descriptive error message for input formatting * Replace movedim with permute * PEP 8 compliance * Division with float * Style changes in error messages * More error message style improvements * Fix typo in docs * Add more descriptive variable names in utils * Change internal var names * Break down error checking for inputs into separate functions * Remove the (N, ..., C) option in MD-MC * Simplify select_topk * Remove detach for inputs * Fix typos * Update pytorch_lightning/metrics/classification/utils.py Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> * Update docs/source/metrics.rst Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> * Minor error message changes * Update pytorch_lightning/metrics/utils.py Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> * Reuse case from validation in formatting * Refactor code in _input_format_classification * Small improvements * PEP 8 * Update pytorch_lightning/metrics/classification/utils.py Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com> * Update pytorch_lightning/metrics/classification/utils.py Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com> * Update docs/source/metrics.rst Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com> * Update pytorch_lightning/metrics/classification/utils.py Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com> * Apply suggestions from code review Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com> * Alphabetical reordering of regression metrics * Change default value of top_k and add error checking * Extract basic validation into separate function * Update to new top_k default * Update desciption of parameters in input formatting * Apply suggestions from code review Co-authored-by: Nicki Skafte <skaftenicki@gmail.com> * Check that probabilities in preds sum to 1 (for MC) * Fix coverage * Split accuracy and hamming loss * Remove old redundant accuracy * Minor changes * Fix imports * Improve docstring descriptions * Fix edge case and simplify testing * Fix docs * PEP8 * Reorder imports * Update changelog * Update docstring * Update docstring * Reverse formatting changes for tests * Change parameter order * Remove formatting changes 2/2 * Remove formatting 3/3 * . * Improve description of top_k parameter * Apply suggestions from code review * Apply suggestions from code review Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com> * Remove unneeded assert * Update pytorch_lightning/metrics/functional/accuracy.py Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com> * Remove unneeded assert * Explicit checking of parameter values * Apply suggestions from code review Co-authored-by: Nicki Skafte <skaftenicki@gmail.com> * Apply suggestions from code review * Fix top_k checking * PEP8 * Don't check dist_sync in test * add back check_dist_sync_on_step * Make sure half-precision inputs are transformed (#5013) * Fix typo * Rename hamming loss to hamming distance * Fix tests for half precision * Fix docs underline length * Fix doc undeline length * Replace mdmc_accuracy parameter with subset_accuracy * Update changelog * Apply suggestions from code review Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com> * Suggestions from code review * Fix number in docs * Update pytorch_lightning/metrics/classification/accuracy.py * Replace topk by argsort in select_topk * Fix changelog * Add test for wrong params * Add Google Colab badges (#5111) * Add colab badges to notebook Add colab badges to notebook to notebooks 4 & 5 * Add colab badges Co-authored-by: chaton <thomas@grid.ai> * Fix hanging metrics tests (#5134) * Use torch.topk again as ddp hanging tests fixed in #5134 * Fix unwanted notebooks change * Fix too long line in hamming_distance * Apply suggestions from code review * Apply suggestions from code review * protect * Update CHANGELOG.md Co-authored-by: Teddy Koker <teddy.koker@gmail.com> Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: chaton <thomas@grid.ai> Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com> Co-authored-by: Nicki Skafte <skaftenicki@gmail.com> Co-authored-by: Justus Schock <12886177+justusschock@users.noreply.github.com> Co-authored-by: Roger Shieh <sh.rog@protonmail.ch> Co-authored-by: Shachar Mirkin <shacharmirkin@gmail.com>
2020-12-21 15:42:51 +00:00
HammingDistance,
IoU,
Precision,
Recall,
ConfusionMatrix,
PrecisionRecallCurve,
AveragePrecision,
ROC,
FBeta,
F1,
Classification metrics overhaul: stat scores (3/n) (#4839) * Add stuff * Change metrics documentation layout * Add stuff * Add stat scores * Change testing utils * Replace len(*.shape) with *.ndim * More descriptive error message for input formatting * Replace movedim with permute * PEP 8 compliance * WIP * Add reduce_scores function * Temporarily add back legacy class_reduce * Division with float * PEP 8 compliance * Remove precision recall * Replace movedim with permute * Add back tests * Add empty newlines * Add empty line * Fix permute * Fix some issues with old versions of PyTorch * Style changes in error messages * More error message style improvements * Fix typo in docs * Add more descriptive variable names in utils * Change internal var names * Break down error checking for inputs into separate functions * Remove the (N, ..., C) option in MD-MC * Simplify select_topk * Remove detach for inputs * Fix typos * Update pytorch_lightning/metrics/classification/utils.py Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> * Update docs/source/metrics.rst Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> * Minor error message changes * Update pytorch_lightning/metrics/utils.py Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> * Reuse case from validation in formatting * Refactor code in _input_format_classification * Small improvements * PEP 8 * Update pytorch_lightning/metrics/classification/utils.py Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com> * Update pytorch_lightning/metrics/classification/utils.py Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com> * Update docs/source/metrics.rst Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com> * Update pytorch_lightning/metrics/classification/utils.py Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com> * Apply suggestions from code review Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com> * Alphabetical reordering of regression metrics * Change default value of top_k and add error checking * Extract basic validation into separate function * Update to new top_k default * Update desciption of parameters in input formatting * Apply suggestions from code review Co-authored-by: Nicki Skafte <skaftenicki@gmail.com> * Check that probabilities in preds sum to 1 (for MC) * Fix coverage * Split accuracy and hamming loss * Remove old redundant accuracy * Minor changes * Fix imports * Improve docstring descriptions * Fix imports * Fix edge case and simplify testing * Fix docs * PEP8 * Reorder imports * Add top_k parameter * Update changelog * Update docstring * Update docstring * Reverse formatting changes for tests * Change parameter order * Remove formatting changes 2/2 * Remove formatting 3/3 * . * Improve description of top_k parameter * Apply suggestions from code review * Apply suggestions from code review Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com> * Remove unneeded assert * Update pytorch_lightning/metrics/functional/accuracy.py Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com> * Remove unneeded assert * Explicit checking of parameter values * Apply suggestions from code review Co-authored-by: Nicki Skafte <skaftenicki@gmail.com> * Apply suggestions from code review * Fix top_k checking * PEP8 * Don't check dist_sync in test * add back check_dist_sync_on_step * Make sure half-precision inputs are transformed (#5013) * Fix typo * Rename hamming loss to hamming distance * Fix tests for half precision * Fix docs underline length * Fix doc undeline length * Replace mdmc_accuracy parameter with subset_accuracy * Update changelog * Fix unwanted accuracy change * Enable top_k for ML prob inputs * Test that default threshold is 0.5 * Fix typo * Update top_k description in helpers * updates * Update styling and add back tests * Remove excess spaces * fix torch.where for old versions * fix linting * Update docstring * Fix docstring * Apply suggestions from code review (mostly docs) * Default threshold to None, accept only (0,1) * Change wrong threshold message * Improve documentation and add tests * Add back ddp tests * Change stat reduce method and default * Remove DDP tests and fix doctests * Fix doctest * Update changelog * Refactoring * Fix typo * Refactor * Increase coverage * Fix linting * Consistent use of backticks * Fix too long line in docs * Apply suggestions from code review * Fix deprecation test * Fix deprecation test * Default threshold back to 0.5 * Minor documentation fixes * Add types to tests Co-authored-by: Teddy Koker <teddy.koker@gmail.com> Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: chaton <thomas@grid.ai> Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com> Co-authored-by: Nicki Skafte <skaftenicki@gmail.com> Co-authored-by: Justus Schock <12886177+justusschock@users.noreply.github.com>
2020-12-30 19:49:50 +00:00
StatScores
)
from pytorch_lightning.metrics.regression import ( # noqa: F401
MeanSquaredError,
MeanAbsoluteError,
MeanSquaredLogError,
ExplainedVariance,
PSNR,
SSIM,
R2Score
)