DEV: Renaming Classes and Functions

docs/usage.rst

@@ -99,7 +99,7 @@ Engine
 
 - :obj:`Patch Prediction <tiatoolbox.models.engine.patch_predictor.PatchPredictor>`
 - :obj:`Semantic Segmentation <tiatoolbox.models.engine.semantic_segmentor.SemanticSegmentor>`
-- :obj:`Feature Extraction <tiatoolbox.models.engine.semantic_segmentor.FeatureExtractor>`
+- :obj:`Feature Extraction <tiatoolbox.models.engine.semantic_segmentor.DeepFeatureExtractor>`
 - :obj:`Nucleus Instance Segmnetation <tiatoolbox.models.engine.nucleus_instance_segmentor.NucleusInstanceSegmentor>`
 
 ----------------------------

examples/full-pipelines/slide-graph.ipynb

@@ -532,8 +532,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from tiatoolbox.models import FeatureExtractor, IOSegmentorConfig\n",
-    "from tiatoolbox.models.architecture import CNNExtractor\n",
+    "from tiatoolbox.models import DeepFeatureExtractor, IOSegmentorConfig\n",
+    "from tiatoolbox.models.architecture import CNNBackbone\n",
     "\n",
     "\n",
     "def extract_deep_features(\n",
@@ -553,8 +553,8 @@
     "        stride_shape=[512, 512],\n",
     "        save_resolution={\"units\": \"mpp\", \"resolution\": 8.0},\n",
     "    )\n",
-    "    model = CNNExtractor(\"resnet50\")\n",
-    "    extractor = FeatureExtractor(\n",
+    "    model = CNNBackbone(\"resnet50\")\n",
+    "    extractor = DeepFeatureExtractor(\n",
     "        batch_size=16, model=model, num_loader_workers=4)\n",
     "    # Injecting customized preprocessing functions,\n",
     "    # check the document or sample code below for API.\n",
@@ -603,7 +603,7 @@
     "and count the nuclei of each type in each patch.\n",
     "We encapsulate this process in the function `get_composition_features`.\n",
     "\n",
-    "Unlike the `FeatureExtractor` above, the `NucleusInstanceSegmentor` engine\n",
+    "Unlike the `DeepFeatureExtractor` above, the `NucleusInstanceSegmentor` engine\n",
     "returns a single output file when given a single WSI input. Their corresponding\n",
     "output files are named as `['*/0.dat', '*/1.dat', etc.]` and we need to rename\n",
     "them accordingly. We generate the cell composition features from each\n",

examples/inference-pipelines/slide-graph.ipynb

@@ -443,8 +443,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from tiatoolbox.models import FeatureExtractor, IOSegmentorConfig\n",
-    "from tiatoolbox.models.architecture import CNNExtractor\n",
+    "from tiatoolbox.models import DeepFeatureExtractor, IOSegmentorConfig\n",
+    "from tiatoolbox.models.architecture import CNNBackbone\n",
     "\n",
     "\n",
     "def extract_deep_features(\n",
@@ -464,8 +464,8 @@
     "        stride_shape=[512, 512],\n",
     "        save_resolution={\"units\": \"mpp\", \"resolution\": 8.0},\n",
     "    )\n",
-    "    model = CNNExtractor(\"resnet50\")\n",
-    "    extractor = FeatureExtractor(\n",
+    "    model = CNNBackbone(\"resnet50\")\n",
+    "    extractor = DeepFeatureExtractor(\n",
     "        batch_size=32, model=model, num_loader_workers=4)\n",
     "    # Injecting customized preprocessing functions,\n",
     "    # check the document or sample code below for API.\n",
@@ -514,10 +514,10 @@
     "and count the nuclei of each type in each patch.\n",
     "We encapsulate this process in the function `get_composition_features`.\n",
     "\n",
-    "Unlike the `FeatureExtractor` above, the `NucleusInstanceSegmentor` engine\n",
+    "Unlike the `DeepFeatureExtractor` above, the `NucleusInstanceSegmentor` engine\n",
     "returns a single output file given a single WSI input. The corresponding output\n",
     "files are named as `['*/0.dat', '*/1.dat', etc.]`. Each of these `.dat` files is used to generate\n",
-    "two files named `*.features.npy` and `*.position.npy`. As in the case of `FeatureExtractor`,\n",
+    "two files named `*.features.npy` and `*.position.npy`. As in the case of `DeepFeatureExtractor`,\n",
     "the wildcard _* is, by default, replaced by sequentially ordered names,\n",
     "for easier management and to avoid inadvertent overwriting."
    ]

tests/models/test_feature_extractor.py

@@ -26,9 +26,9 @@
 import numpy as np
 import torch
 
-from tiatoolbox.models.architecture.vanilla import CNNExtractor
+from tiatoolbox.models.architecture.vanilla import CNNBackbone
 from tiatoolbox.models.engine.semantic_segmentor import (
-    FeatureExtractor,
+    DeepFeatureExtractor,
     IOSegmentorConfig,
 )
 from tiatoolbox.wsicore.wsireader import get_wsireader
@@ -57,7 +57,7 @@ def test_functional(remote_sample, tmp_path):
 
     # * test providing pretrained from torch vs pretrained_model.yaml
     _rm_dir(save_dir)  # default output dir test
-    extractor = FeatureExtractor(batch_size=1, pretrained_model="fcn-tissue_mask")
+    extractor = DeepFeatureExtractor(batch_size=1, pretrained_model="fcn-tissue_mask")
     output_list = extractor.predict(
         [mini_wsi_svs],
         mode="wsi",
@@ -87,8 +87,8 @@ def test_functional(remote_sample, tmp_path):
         save_resolution={"units": "mpp", "resolution": 8.0},
     )
 
-    model = CNNExtractor("resnet50")
-    extractor = FeatureExtractor(batch_size=4, model=model)
+    model = CNNBackbone("resnet50")
+    extractor = DeepFeatureExtractor(batch_size=4, model=model)
     # should still run because we skip exception
     output_list = extractor.predict(
         [mini_wsi_svs],

tiatoolbox/models/__init__.py

@@ -28,7 +28,7 @@
     WSIPatchDataset,
 )
 from tiatoolbox.models.engine.semantic_segmentor import (
-    FeatureExtractor,
+    DeepFeatureExtractor,
     IOSegmentorConfig,
     SemanticSegmentor,
     WSIStreamDataset,

tiatoolbox/models/architecture/__init__.py

@@ -28,7 +28,7 @@
 import torch
 
 from tiatoolbox import rcParam
-from tiatoolbox.models.architecture.vanilla import CNNExtractor, CNNModel
+from tiatoolbox.models.architecture.vanilla import CNNBackbone, CNNModel
 from tiatoolbox.models.dataset.classification import predefined_preproc_func
 from tiatoolbox.utils.misc import download_data
 

tiatoolbox/models/architecture/vanilla.py

@@ -162,7 +162,7 @@ def infer_batch(model, batch_data, on_gpu):
         return output.cpu().numpy()
 
 
-class CNNExtractor(ModelABC):
+class CNNBackbone(ModelABC):
     """Retrieve the model backbone and strip the classification layer.
 
     This is a wrapper for pretrained models within pytorch.
@@ -193,7 +193,7 @@ class CNNExtractor(ModelABC):
         >>> # Creating resnet50 architecture from default pytorch
         >>> # without the classification layer with its associated
         >>> # weights loaded
-        >>> model = CNNExtractor(backbone="resnet50")
+        >>> model = CNNBackbone(backbone="resnet50")
         >>> model.eval()  # set to evaluation mode
         >>> # dummy sample in NHWC form
         >>> samples = torch.random.rand(4, 3, 512, 512)

tiatoolbox/models/engine/semantic_segmentor.py

@@ -1116,7 +1116,7 @@ def predict(
         return outputs
 
 
-class FeatureExtractor(SemanticSegmentor):
+class DeepFeatureExtractor(SemanticSegmentor):
     """Generic CNN Feature Extractor.
 
     A engine for using any CNN model as a feature extractor.
@@ -1131,7 +1131,7 @@ class FeatureExtractor(SemanticSegmentor):
           for processing the data. By default, the corresponding pretrained weights
           will also be downloaded. However, you can override with your own set of
           weights via the `pretrained_weights` argument. Argument is case insensitive.
-          Refer to :class:`tiatoolbox.models.architecture.vanilla.CNNExtractor`
+          Refer to :class:`tiatoolbox.models.architecture.vanilla.CNNBackbone`
           for list of supported pretrained models.
         pretrained_weights (str): Path to the weight of the corresponding
           `pretrained_model`.
@@ -1148,11 +1148,11 @@ class FeatureExtractor(SemanticSegmentor):
 
     Examples:
         >>> # Sample output of a network
-        >>> from tiatoolbox.models.architecture.vanilla import CNNExtractor
+        >>> from tiatoolbox.models.architecture.vanilla import CNNBackbone
         >>> wsis = ['A/wsi.svs', 'B/wsi.svs']
         >>> # create resnet50 with pytorch pretrained weights
-        >>> model = CNNExtractor('resnet50')
-        >>> predictor = FeatureExtractor(model=model)
+        >>> model = CNNBackbone('resnet50')
+        >>> predictor = DeepFeatureExtractor(model=model)
         >>> output = predictor.predict(wsis, mode='wsi')
         >>> list(output.keys())
         [('A/wsi.svs', 'output/0') , ('B/wsi.svs', 'output/1')]
@@ -1296,11 +1296,11 @@ def predict(
 
         Examples:
             >>> # Sample output of a network
-            >>> from tiatoolbox.models.architecture.vanilla import CNNExtractor
+            >>> from tiatoolbox.models.architecture.vanilla import CNNBackbone
             >>> wsis = ['A/wsi.svs', 'B/wsi.svs']
             >>> # create resnet50 with pytorch pretrained weights
-            >>> model = CNNExtractor('resnet50')
-            >>> predictor = FeatureExtractor(model=model)
+            >>> model = CNNBackbone('resnet50')
+            >>> predictor = DeepFeatureExtractor(model=model)
             >>> output = predictor.predict(wsis, mode='wsi')
             >>> list(output.keys())
             [('A/wsi.svs', 'output/0') , ('B/wsi.svs', 'output/1')]