diff --git a/Project.toml b/Project.toml
index 5dbf3b2616df7ea4b67921f501acf9722250d0a6..4fefa105e15dca5838e8e13597c110207b17085b 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
name = "TaggingBackends"
uuid = "e551f703-3b82-4335-b341-d497b48d519b"
authors = ["François Laurent", "Institut Pasteur"]
-version = "0.7.1"
+version = "0.7.2"
[deps]
Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
diff --git a/pyproject.toml b/pyproject.toml
index 5cab35d6fc8a3a3d121ac999bb46f879935af816..84e30fe3132801c1d9a3d26c05b4f9a0f8e9f24e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
[tool.poetry]
name = "TaggingBackends"
-version = "0.7.1"
+version = "0.7.2"
description = "Backbone for LarvaTagger.jl tagging backends"
authors = ["François Laurent"]
diff --git a/src/taggingbackends/data/dataset.py b/src/taggingbackends/data/dataset.py
index 80b02804c80359c9aa7bca99d2b3267beb2865ca..9cc670b5102e27d911806c50ff1a26132ef6dcb9 100644
--- a/src/taggingbackends/data/dataset.py
+++ b/src/taggingbackends/data/dataset.py
@@ -101,12 +101,13 @@ class LarvaDataset:
train, val, test = torch.utils.data.random_split(TorchDataset(),
[ntrain, nval, ntest],
generator=self.generator)
- self._training_set = iter(itertools.cycle(
- torch.utils.data.DataLoader(train,
- batch_size=self.batch_size,
- shuffle=True,
- generator=g_train,
- drop_last=True)))
+ if 0 < ntrain:
+ self._training_set = iter(itertools.cycle(
+ torch.utils.data.DataLoader(train,
+ batch_size=self.batch_size,
+ shuffle=True,
+ generator=g_train,
+ drop_last=True)))
self._validation_set = iter(
torch.utils.data.DataLoader(val,
batch_size=self.batch_size))
@@ -186,14 +187,14 @@ def subset_size(ntot, train_share, val_share, test_share):
while ndelta < 0:
if 0 < val_share:
nval += 1
- ndelta -= 1
+ ndelta += 1
if ndelta < 0:
if 0 < test_share:
ntest += 1
- ndelta -= 1
+ ndelta += 1
if ndelta < 0:
ntrain += 1
- ndelta -= 1
+ ndelta += 1
while 0 < ndelta:
if 0 < train_share:
ntrain -= 1
diff --git a/src/taggingbackends/data/labels.py b/src/taggingbackends/data/labels.py
index 6c17d77bc1eabf8945a7103c93397995bb5b377f..d741a89ecbecd4960365a2532469729c828d73e0 100644
--- a/src/taggingbackends/data/labels.py
+++ b/src/taggingbackends/data/labels.py
@@ -8,6 +8,54 @@ TIME_PRECISION = 0.001
labels_file_extension = (".nyxlabel", ".label", ".labels")
+"""
+Python interface to JSON *.label* files.
+
+It is a poor parent of Julia struct `Dataset` from `PlanarLarvae.jl`.
+
+The following example function opens such a *.label* file and loads the
+associated track data assumed to be available as a *trx.mat* file.
+It iterates over the tracks defined in the *.label* file. The same tracks are
+expected to be found also in the *trx.mat* file.
+
+```
+import numpy as np
+from sklearn.metrics import confusion_matrix
+
+def retagged_trxmat_confusion_matrix(label_file):
+ # load the .label and associated trx.mat files
+ predicted_labels = Labels(label_file)
+ labels = predicted_labels.labelspec
+ track_data_file = TrxMat(label_file.parent / 'trx.mat')
+ expected_labels = track_data_file.read(labels)
+
+ # iterate the tracks
+ cm = None
+ for run, larva in predicted_labels:
+
+ ypred = np.array([labels.index(label)
+ for label in predicted_labels[(run, larva)].values()])
+ yexp = np.stack([expected_labels[label][run][larva]
+ for label in labels])
+ iexp, yexp = np.nonzero(yexp.T==1) # the order of dimensions matters
+ ypred = ypred[iexp] # if the trx.mat file mentions more tags than the
+ # .label file, some timesteps may appear as untagged
+
+ cm_ = confusion_matrix(yexp, ypred, labels=np.arange(len(labels)))
+ cm = cm_ if cm is None else cm + cm_
+ return cm
+```
+
+Note that unlike the `TrxMat` class, keys in `Labels` objects are
+(runid, trackid) pairs.
+A `Labels` object will typically describe a single run, but can technically
+represent several runs.
+
+The `labelspec` attribute is assumed to be a list, which is valid for *.label*
+generated by automatic tagging.
+Manual tagging will store label names as `Labels.labelspec['names']`, because
+label colors are also stored in the `labelspec` attribute.
+"""
class Labels:
def __init__(self, labels=None, labelspec=None, metadata=None, units=None,
@@ -23,8 +71,12 @@ class Labels:
self.labelspec, self.units = labelspec, units
self._tracking = tracking
self._input_labels = None
+ #
+ self.filepath = None # unused attribute; may help to manage data
+ # dependencies in the future
if isinstance(labels, (str, pathlib.Path)):
- self.load(labels)
+ self.filepath = pathlib.Path(labels) if isinstance(labels, str) else labels
+ self.load()
@property
def tracking(self):
@@ -182,10 +234,12 @@ class Labels:
else:
json.dump(self, file, cls=LabelEncoder, indent=indent, **kwargs)
- def load(self, file, format="json"):
+ def load(self, file=None, format="json"):
if format != "json":
raise ArgumentError(f"unsupported format: {format}")
- if isinstance(file, str):
+ if file is None:
+ file = self.filepath
+ elif isinstance(file, str):
file = pathlib.Path(file)
if isinstance(file, pathlib.Path):
if file.is_dir():
diff --git a/src/taggingbackends/explorer.py b/src/taggingbackends/explorer.py
index 4680ab233b0f40d822098cc615c887f2c90de4fc..c8d123adee3324458ca7e256225783ab820b6067 100644
--- a/src/taggingbackends/explorer.py
+++ b/src/taggingbackends/explorer.py
@@ -181,6 +181,11 @@ Cannot find any Python package in project root directory:
# Julia logging
elif line and line[0] in "[┌│└":
print(line)
+ # Torch logs
+ elif line == ' return torch._C._cuda_getDeviceCount() > 0':
+ # typically follows:
+ # UserWarning: CUDA initialization: CUDA unknown error
+ print(line)
# tensorflow logs
elif 26 < len(line):
# assume line[:26] to be e.g.: 2022-05-17 18:48:15.120981