update dependency versions

daart/callbacks.py

@@ -147,7 +147,7 @@ def on_epoch_end(self, data_generator, model, trainer, **kwargs):
                     batch[np.sum(batch, axis=1) == 0, 0] = 1
                     # turn into a one-hot vector
                     batch = np.argmax(batch, axis=1)
-                pseudo_labels_data.append(batch.astype(np.int))
+                pseudo_labels_data.append(batch.astype(int))
             pseudo_labels.append(pseudo_labels_data)
 
         # total_new_pseudos = \
@@ -210,7 +210,7 @@ def on_epoch_end(self, data_generator, model, trainer, **kwargs):
                     new_batch[np.sum(new_batch, axis=1) == 0, 0] = 1
                     # turn into a one-hot vector
                     new_batch = np.argmax(new_batch, axis=1)
-                pseudo_labels_data.append(new_batch.astype(np.int))
+                pseudo_labels_data.append(new_batch.astype(int))
             pseudo_labels.append(pseudo_labels_data)
 
         # update the data generator with the new psuedo-labels

daart/data.py

@@ -398,7 +398,7 @@ def load_data(self, sequence_length: int, input_type: str) -> None:
                     # if no path given, assume same size as markers and set all to background
                     if 'markers' in self.data.keys():
                         data_curr = np.zeros(
-                            (len(self.data['markers']) * sequence_length,), dtype=np.int)
+                            (len(self.data['markers']) * sequence_length,), dtype=int)
                     else:
                         raise FileNotFoundError(
                             'Could not load "labels_strong" from None file without markers')
@@ -636,12 +636,12 @@ def count_class_examples(self) -> np.array:
 
         assert 'labels_strong' in self.signals[0], 'Cannot count examples without hand labels'
 
-        totals = np.zeros(len(self.label_names), dtype=np.int)
+        totals = np.zeros(len(self.label_names), dtype=int)
         for dataset in self.datasets:
             pad = dataset.sequence_pad
             for b, batch in enumerate(dataset.data['labels_strong']):
                 # log number of examples for batch
-                counts = np.bincount(batch[pad:-pad].astype('int'))
+                counts = np.bincount(batch[pad:-pad].astype(int))
                 if len(counts) == len(totals):
                     totals += counts
                 else:
@@ -874,7 +874,7 @@ def load_label_csv(filepath: str) -> tuple:
 
     """
     labels = np.genfromtxt(
-        filepath, delimiter=',', dtype=np.int, encoding=None, skip_header=1)[:, 1:]
+        filepath, delimiter=',', dtype=int, encoding=None, skip_header=1)[:, 1:]
     label_names = list(
         np.genfromtxt(filepath, delimiter=',', dtype=None, encoding=None, max_rows=1)[1:])
     return labels, label_names

daart/train.py

@@ -81,7 +81,7 @@ def update_metrics(
             self,
             dtype: str,
             loss_dict: dict,
-            dataset: Union[int, np.int64, list, None] = None
+            dataset: Union[int, int, list, None] = None
     ) -> None:
         """Update metrics for a specific dtype/dataset.
 
@@ -123,12 +123,12 @@ def update_metrics(
     def create_metric_row(
             self,
             dtype: str,
-            epoch: Union[int, np.int64],
-            batch: Union[int, np.int64],
-            dataset: Union[int, np.int64],
-            trial: Union[int, np.int64, None],
-            best_epoch: Optional[Union[int, np.int64]] = None,
-            by_dataset: bool = False
+            epoch: Union[int, int],
+            batch: Union[int, int],
+            dataset: Union[int, int],
+            trial: Union[int, int, None],
+            best_epoch: Optional[Union[int, int]] = None,
+            by_dataset: bool = False,
     ) -> dict:
         """Export metrics and other data (e.g. epoch) for logging train progress.
 

docs/source/installation.rst

@@ -29,7 +29,7 @@ Then, create a conda environment:
 
 .. code-block:: console
 
-    conda create --name daart python=3.6
+    conda create --name daart python=3.10
 
 Active the new environment:
 

examples/fit_models.ipynb

@@ -210,7 +210,7 @@
    "source": [
     "# load hand labels\n",
     "from numpy import genfromtxt\n",
-    "labels = genfromtxt(hand_labels_file, delimiter=',', dtype=np.int, encoding=None)\n",
+    "labels = genfromtxt(hand_labels_file, delimiter=',', dtype=int, encoding=None)\n",
     "labels = labels[1:, 1:]  # get rid of headers, etc.\n",
     "states = np.argmax(labels, axis=1)\n",
     "\n",

setup.py

@@ -1,7 +1,7 @@
 from distutils.core import setup
 
 
-VERSION = '1.0.2'
+VERSION = '1.1.0'
 
 # add the README.md file to the long_description
 with open('README.md', 'r') as fh:
@@ -12,7 +12,7 @@
     'jupyter',
     'matplotlib',
     'numpy',
-    'opencv-python',
+    'opencv-python-headless',
     'pandas',
     'pytest',
     'pyyaml',
@@ -21,7 +21,7 @@
     'seaborn',
     'tables',
     'test-tube',
-    'torch==1.8.0',
+    'torch',
     'tqdm',
     'typeguard',
 ]