v0.1.33

README.md

@@ -1,4 +1,4 @@
-#HCAT - Hair Cell Analysis Tools
+# HCAT - Hair Cell Analysis Tools
 
 Hcat is a suite of machine learning enabled algorithms for performing common image analyses in the hearing field.
 At present, it performs two fully automated analyses: (1) Volumetric hair cell segmentation and (2) 2D hair cell detection. 

hcat/backends/detection.py

@@ -319,17 +319,16 @@ def FasterRCNN_from_url(url: str, device: str, model: FasterRCNN = HairCellFaste
     """ loads model from url """
     path = os.path.join(hcat.__path__[0], 'Max_project_detection_resnet.trch')
 
-    if not os.path.exists(path) and _is_url(url):
+    if not os.path.exists(path):
         print('Downloading Model File: ')
         wget.download(url=url, out=path)
         print(' ')
-    else:
-        raise ValueError(f'Url is not valid: {url}')
+
 
     model = model.requires_grad_(False)
     checkpoint = torch.load(path, map_location=torch.device('cpu'))
     model = model.to(device)
-    model.load_state_dict(checkpoint['model_state_dict'])
+    model.load_state_dict(checkpoint)
 
     for m in model.modules():
         if isinstance(m, nn.BatchNorm3d):

hcat/detect.py

@@ -41,6 +41,15 @@ def _detect(f: str, curve_path: str = None, cell_detection_threshold: float = 0.
 
     with torch.no_grad():
 
+        device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        if device == 'cuda':
+            print('\x1b[1;32;40mCUDA: GPU successfully initialized!\x1b[0m')
+        else:
+            print('\x1b[1;33;40m'
+                  'WARNING: GPU not present or CUDA is not correctly intialized for GPU accelerated computation. '
+                  'Analysis may be slow.'
+                  '\x1b[0m')
+
         # Load and preprocess Image
         image_base = load(f, 'TileScan 1 Merged', verbose=True)  # from hcat.lib.utils
         image_base = image_base[[2, 3],...].max(-1) if image_base.ndim == 4 else image_base
@@ -50,7 +59,7 @@ def _detect(f: str, curve_path: str = None, cell_detection_threshold: float = 0.
 
         dtype = image_base.dtype if dtype is None else dtype
         scale: int = hcat.lib.utils.get_dtype_offset(dtype)
-        device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
 
         temp = np.zeros(shape)
         temp = np.concatenate((temp, image_base)) / scale * 255
@@ -83,13 +92,6 @@ def _detect(f: str, curve_path: str = None, cell_detection_threshold: float = 0.
         image_base.sub_(0.5).div_(0.5)
 
 
-        if device == 'cuda':
-            print('\x1b[1;32;40mCUDA: GPU successfully initialized!\x1b[0m')
-        else:
-            print('\x1b[1;33;40m'
-                  'WARNING: GPU not present or CUDA is not correctly intialized for GPU accelerated computation. '
-                  'Analysis may be slow.'
-                  '\x1b[0m')
 
         # Initalize the model...
         model = FasterRCNN_from_url(url='https://github.com/buswinka/hcat/blob/master/modelfiles/detection.trch?raw=true', device=device)

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = hcat
-version = 0.1.32
+version = 0.1.33
 author = Chris Buswinka
 author_email = [email protected]
 classifiers = Programming Language :: Python :: 3

setup.py

@@ -4,7 +4,7 @@
     long_description = fh.read()
 
 setuptools.setup(
-    description="Whole cochlea hair cell segmentation toolkit",
+    description="A Hair Cell Analysis Toolbox",
     long_description=long_description,
     long_description_content_type="text/markdown",
     packages=setuptools.find_packages(),
@@ -13,7 +13,7 @@
     install_requires = [
         'kornia>=0.5.2',
         'numpy>=1.10.0',
-        'torch>=1.7.0',
+        'torch>=1.9.0',
         'matplotlib>=3.3.2',
         'scipy>=1.5.4',
         'scikit-image>=0.17.2',
@@ -22,7 +22,7 @@
         'lz4>=3.1.3',
         'scikit-learn>=0.24.2',
         'GPy>=1.10.0',
-        'torchvision>=0.8.1',
+        'torchvision>=0.10.0',
         'elasticdeform>=0.4.9',
         'wget>=3.2']
 )