Change import pattern in TM

CHANGELOG.md

@@ -32,6 +32,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Scalar metrics will now consistently have additional dimensions squeezed ([#622](https://github.com/PyTorchLightning/metrics/pull/622))
 
 
+- Metrics having third party dependencies removed from global import ([#463](https://github.com/PyTorchLightning/metrics/pull/463))
+
+
 - `BLEUScore` now expects untokenized input to stay consistent with all the other text metrics ([#640](https://github.com/PyTorchLightning/metrics/pull/640))
 
 

docs/source/references/functional.rst

@@ -14,7 +14,7 @@ Audio Metrics
 pesq [func]
 ~~~~~~~~~~~
 
-.. autofunction:: torchmetrics.functional.pesq
+.. autofunction:: torchmetrics.functional.audio.pesq.pesq
 
 
 pit [func]
@@ -55,7 +55,7 @@ snr [func]
 stoi [func]
 ~~~~~~~~~~~
 
-.. autofunction:: torchmetrics.functional.stoi
+.. autofunction:: torchmetrics.functional.audio.stoi.stoi
  :noindex:
 
 
@@ -433,7 +433,7 @@ Text
 bert_score [func]
 ~~~~~~~~~~~~~~~~~~
 
-.. autofunction:: torchmetrics.functional.bert_score
+.. autofunction:: torchmetrics.functional.text.bert.bert_score
 
 bleu_score [func]
 ~~~~~~~~~~~~~~~~~
@@ -462,7 +462,7 @@ match_error_rate [func]
 rouge_score [func]
 ~~~~~~~~~~~~~~~~~~
 
-.. autofunction:: torchmetrics.functional.rouge_score
+.. autofunction:: torchmetrics.functional.text.rouge.rouge_score
  :noindex:
 
 sacre_bleu_score [func]

docs/source/references/modules.rst

@@ -77,7 +77,7 @@ the metric will be computed over the ``time`` dimension.
 PESQ
 ~~~~
 
-.. autoclass:: torchmetrics.PESQ
+.. autoclass:: torchmetrics.audio.pesq.PESQ
 
 PIT
 ~~~
@@ -112,7 +112,7 @@ SNR
 STOI
 ~~~~
 
-.. autoclass:: torchmetrics.STOI
+.. autoclass:: torchmetrics.audio.stoi.STOI
  :noindex:
 
 
@@ -369,25 +369,25 @@ learning algorithms such as `Generative Adverserial Networks (GANs) <https://en.
 FID
 ~~~
 
-.. autoclass:: torchmetrics.FID
+.. autoclass:: torchmetrics.image.fid.FID
  :noindex:
 
 IS
 ~~
 
-.. autoclass:: torchmetrics.IS
+.. autoclass:: torchmetrics.image.inception.IS
  :noindex:
 
 KID
 ~~~
 
-.. autoclass:: torchmetrics.KID
+.. autoclass:: torchmetrics.image.kid.KID
  :noindex:
 
 LPIPS
 ~~~~~
 
-.. autoclass:: torchmetrics.LPIPS
+.. autoclass:: torchmetrics.image.lpip_similarity.LPIPS
  :noindex:
 
 PSNR
@@ -411,7 +411,7 @@ Object detection metrics can be used to evaluate the predicted detections with g
 MAP
 ~~~
 
-.. autoclass:: torchmetrics.MAP
+.. autoclass:: torchmetrics.detection.map.MAP
  :noindex:
 
 ******************
@@ -613,7 +613,7 @@ Text
 BERTScore
 ~~~~~~~~~~
 
-.. autoclass:: torchmetrics.BERTScore
+.. autoclass:: torchmetrics.text.bert.BERTScore
  :noindex:
 
 BLEUScore
@@ -643,7 +643,7 @@ MatchErrorRate
 ROUGEScore
 ~~~~~~~~~~
 
-.. autoclass:: torchmetrics.ROUGEScore
+.. autoclass:: torchmetrics.text.rouge.ROUGEScore
  :noindex:
 
 SacreBLEUScore

tests/audio/test_pesq.py

@@ -21,8 +21,8 @@
 
 from tests.helpers import seed_all
 from tests.helpers.testers import MetricTester
-from torchmetrics.audio import PESQ
-from torchmetrics.functional import pesq
+from torchmetrics.audio.pesq import PESQ
+from torchmetrics.functional.audio.pesq import pesq
 from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_1_6
 
 seed_all(42)

tests/audio/test_stoi.py

@@ -21,8 +21,8 @@
 
 from tests.helpers import seed_all
 from tests.helpers.testers import MetricTester
-from torchmetrics.audio import STOI
-from torchmetrics.functional import stoi
+from torchmetrics.audio.stoi import STOI
+from torchmetrics.functional.audio.stoi import stoi
 from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_1_6
 
 seed_all(42)

tests/text/test_bertscore.py

@@ -7,8 +7,8 @@
 import torch.distributed as dist
 import torch.multiprocessing as mp
 
-from torchmetrics.functional import bert_score as metrics_bert_score
-from torchmetrics.text import BERTScore
+from torchmetrics.functional.text.bert import bert_score as metrics_bert_score
+from torchmetrics.text.bert import BERTScore
 from torchmetrics.utilities.imports import _BERTSCORE_AVAILABLE
 
 if _BERTSCORE_AVAILABLE:

torchmetrics/__init__.py

@@ -13,7 +13,7 @@
 
 from torchmetrics import functional # noqa: E402
 from torchmetrics.aggregation import CatMetric, MaxMetric, MeanMetric, MinMetric, SumMetric # noqa: E402
-from torchmetrics.audio import PESQ, PIT, SDR, SI_SDR, SI_SNR, SNR, STOI # noqa: E402
+from torchmetrics.audio import PIT, SDR, SI_SDR, SI_SNR, SNR # noqa: E402
 from torchmetrics.classification import ( # noqa: E402
  AUC,
  AUROC,
@@ -40,8 +40,7 @@
  StatScores,
 )
 from torchmetrics.collections import MetricCollection # noqa: E402
-from torchmetrics.detection import MAP # noqa: E402
-from torchmetrics.image import FID, IS, KID, LPIPS, PSNR, SSIM # noqa: E402
+from torchmetrics.image import PSNR, SSIM # noqa: E402
 from torchmetrics.metric import Metric # noqa: E402
 from torchmetrics.regression import ( # noqa: E402
  CosineSimilarity,
@@ -68,12 +67,10 @@
 )
 from torchmetrics.text import ( # noqa: E402
  WER,
- BERTScore,
  BLEUScore,
  CharErrorRate,
  CHRFScore,
  MatchErrorRate,
- ROUGEScore,
  SacreBLEUScore,
  SQuAD,
  WordInfoLost,
@@ -90,7 +87,6 @@
  "BinnedAveragePrecision",
  "BinnedPrecisionRecallCurve",
  "BinnedRecallAtFixedPrecision",
- "BERTScore",
  "BLEUScore",
  "BootStrapper",
  "CalibrationError",
@@ -103,15 +99,10 @@
  "ExplainedVariance",
  "F1",
  "FBeta",
- "FID",
  "HammingDistance",
  "Hinge",
  "IoU",
- "IS",
- "KID",
  "KLDivergence",
- "LPIPS",
- "MAP",
  "MatthewsCorrcoef",
  "MaxMetric",
  "MeanAbsoluteError",
@@ -126,7 +117,6 @@
  "MinMetric",
  "MultioutputWrapper",
  "PearsonCorrcoef",
- "PESQ",
  "PIT",
  "Precision",
  "PrecisionRecallCurve",
@@ -142,7 +132,6 @@
  "RetrievalRecall",
  "RetrievalRPrecision",
  "ROC",
- "ROUGEScore",
  "SacreBLEUScore",
  "SDR",
  "SI_SDR",
@@ -153,7 +142,6 @@
  "SQuAD",
  "SSIM",
  "StatScores",
- "STOI",
  "SumMetric",
  "SymmetricMeanAbsolutePercentageError",
  "WER",

torchmetrics/audio/__init__.py

@@ -11,10 +11,8 @@
 # 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 torchmetrics.audio.pesq import PESQ # noqa: F401
 from torchmetrics.audio.pit import PIT # noqa: F401
 from torchmetrics.audio.sdr import SDR # noqa: F401
 from torchmetrics.audio.si_sdr import SI_SDR # noqa: F401
 from torchmetrics.audio.si_snr import SI_SNR # noqa: F401
 from torchmetrics.audio.snr import SNR # noqa: F401
-from torchmetrics.audio.stoi import STOI # noqa: F401

torchmetrics/audio/pesq.py

@@ -62,7 +62,7 @@ class PESQ(Metric):
  If ``mode`` is not either ``"wb"`` or ``"nb"``
 
  Example:
- >>> from torchmetrics.audio import PESQ
+ >>> from torchmetrics.audio.pesq import PESQ
  >>> import torch
  >>> g = torch.manual_seed(1)
  >>> preds = torch.randn(8000)

torchmetrics/audio/stoi.py

@@ -63,7 +63,7 @@ class STOI(Metric):
  If ``pystoi`` package is not installed
 
  Example:
- >>> from torchmetrics.audio import STOI
+ >>> from torchmetrics.audio.stoi import STOI
  >>> import torch
  >>> g = torch.manual_seed(1)
  >>> preds = torch.randn(8000)

torchmetrics/detection/__init__.py

@@ -11,4 +11,3 @@
 # 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 torchmetrics.detection.map import MAP # noqa: F401

torchmetrics/detection/map.py

@@ -179,6 +179,42 @@ class MAP(Metric):
  Callback that performs the allgather operation on the metric state. When ``None``, DDP
  will be used to perform the allgather
 
+ Example:
+ >>> import torch
+ >>> from torchmetrics.detection.map import MAP
+ >>> preds = [
+ ... dict(
+ ... boxes=torch.Tensor([[258.0, 41.0, 606.0, 285.0]]),
+ ... scores=torch.Tensor([0.536]),
+ ... labels=torch.IntTensor([0]),
+ ... )
+ ... ]
+ >>> target = [
+ ... dict(
+ ... boxes=torch.Tensor([[214.0, 41.0, 562.0, 285.0]]),
+ ... labels=torch.IntTensor([0]),
+ ... )
+ ... ]
+ >>> metric = MAP() # doctest: +SKIP
+ >>> metric.update(preds, target) # doctest: +SKIP
+ >>> from pprint import pprint
+ >>> pprint(metric.compute()) # doctest: +SKIP
+ {'map': tensor(0.6000),
+ 'map_50': tensor(1.),
+ 'map_75': tensor(1.),
+ 'map_small': tensor(-1.),
+ 'map_medium': tensor(-1.),
+ 'map_large': tensor(0.6000),
+ 'mar_1': tensor(0.6000),
+ 'mar_10': tensor(0.6000),
+ 'mar_100': tensor(0.6000),
+ 'mar_small': tensor(-1.),
+ 'mar_medium': tensor(-1.),
+ 'mar_large': tensor(0.6000),
+ 'map_per_class': tensor(-1.),
+ 'mar_100_per_class': tensor(-1.)
+ }
+
  Raises:
  ImportError:
  If ``torchvision`` is not installed or version installed is lower than 0.8.0

torchmetrics/functional/__init__.py

@@ -11,13 +11,11 @@
 # 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 torchmetrics.functional.audio.pesq import pesq
 from torchmetrics.functional.audio.pit import pit, pit_permutate
 from torchmetrics.functional.audio.sdr import sdr
 from torchmetrics.functional.audio.si_sdr import si_sdr
 from torchmetrics.functional.audio.si_snr import si_snr
 from torchmetrics.functional.audio.snr import snr
-from torchmetrics.functional.audio.stoi import stoi
 from torchmetrics.functional.classification.accuracy import accuracy
 from torchmetrics.functional.classification.auc import auc
 from torchmetrics.functional.classification.auroc import auroc
@@ -109,7 +107,6 @@
  "pairwise_linear_similarity",
  "pairwise_manhatten_distance",
  "pearson_corrcoef",
- "pesq",
  "pit",
  "pit_permutate",
  "precision",
@@ -138,7 +135,6 @@
  "squad",
  "ssim",
  "stat_scores",
- "stoi",
  "symmetric_mean_absolute_percentage_error",
  "wer",
  "char_error_rate",

torchmetrics/functional/audio/__init__.py

@@ -11,10 +11,8 @@
 # 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 torchmetrics.functional.audio.pesq import pesq # noqa: F401
 from torchmetrics.functional.audio.pit import pit, pit_permutate # noqa: F401
 from torchmetrics.functional.audio.sdr import sdr # noqa: F401
 from torchmetrics.functional.audio.si_sdr import si_sdr # noqa: F401
 from torchmetrics.functional.audio.si_snr import si_snr # noqa: F401
 from torchmetrics.functional.audio.snr import snr # noqa: F401
-from torchmetrics.functional.audio.stoi import stoi # noqa: F401

torchmetrics/functional/audio/pesq.py

@@ -58,7 +58,7 @@ def pesq(preds: Tensor, target: Tensor, fs: int, mode: str, keep_same_device: bo
  If ``mode`` is not either ``"wb"`` or ``"nb"``
 
  Example:
- >>> from torchmetrics.functional.audio import pesq
+ >>> from torchmetrics.functional.audio.pesq import pesq
  >>> import torch
  >>> g = torch.manual_seed(1)
  >>> preds = torch.randn(8000)

torchmetrics/functional/audio/stoi.py

@@ -59,7 +59,7 @@ def stoi(preds: Tensor, target: Tensor, fs: int, extended: bool = False, keep_sa
  If ``pystoi`` package is not installed
 
  Example:
- >>> from torchmetrics.functional.audio import stoi
+ >>> from torchmetrics.functional.audio.stoi import stoi
  >>> import torch
  >>> g = torch.manual_seed(1)
  >>> preds = torch.randn(8000)

torchmetrics/functional/text/bert.py

@@ -547,6 +547,7 @@ def bert_score(
  If invalid input is provided.
 
  Example:
+ >>> from torchmetrics.functional.text.bert import bert_score
  >>> predictions = ["hello there", "general kenobi"]
  >>> references = ["hello there", "master kenobi"]
  >>> bert_score(predictions=predictions, references=references, lang="en") # doctest: +SKIP

torchmetrics/functional/text/rouge.py

@@ -180,7 +180,7 @@ def _rouge_score_update(
  >>> preds = "My name is John".split()
  >>> from pprint import pprint
  >>> score = _rouge_score_update(preds, targets, rouge_keys_values=[1, 2, 3, 'L'])
- >>> pprint(score) # doctest: +NORMALIZE_WHITESPACE +SKIP
+ >>> pprint(score) # doctest: +SKIP
  {1: [{'fmeasure': tensor(0.), 'precision': tensor(0.), 'recall': tensor(0.)},
  {'fmeasure': tensor(0.), 'precision': tensor(0.), 'recall': tensor(0.)},
  {'fmeasure': tensor(0.), 'precision': tensor(0.), 'recall': tensor(0.)},
@@ -261,10 +261,11 @@ def rouge_score(
  Python dictionary of rouge scores for each input rouge key.
 
  Example:
+ >>> from torchmetrics.functional.text.rouge import rouge_score
  >>> targets = "Is your name John"
  >>> preds = "My name is John"
  >>> from pprint import pprint
- >>> pprint(rouge_score(preds, targets)) # doctest: +NORMALIZE_WHITESPACE +SKIP
+ >>> pprint(rouge_score(preds, targets)) # doctest: +SKIP
  {'rouge1_fmeasure': 0.25,
  'rouge1_precision': 0.25,
  'rouge1_recall': 0.25,