moving files to support creation of python package

LICENSE.txt

+The MIT License (MIT)
+
+Copyright (c) 2021 Christopher J. MacLellan
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.rst

+Sparse Coding Library for PyTorch
+=================================
\ No newline at end of file

setup.cfg

+[metadata]
+name = sparse_coding_torch
+author = Christopher J. MacLellan
+author-email = christopher.maclellan@drexel.edu
+summary = A library for doing sparse coding using PyTorch
+description-file = README.rst
+description-content-type = text/x-rst; charset=UTF-8
+home-page = https://gitlab.cci.drexel.edu/teachable-ai-lab/sparse_coding_torch
+project_urls = 
+    Documentation = https://gitlab.cci.drexel.edu/teachable-ai-lab/sparse_coding_torch
+    Source Code = https://gitlab.cci.drexel.edu/teachable-ai-lab/sparse_coding_torch
+license = MIT
+license_file = LICENSE.txt
+classifier = 
+    Development Status :: 4 - Beta
+    Intended Audience :: Science/Research
+    Topic :: Scientific/Engineering :: Artificial Intelligence
+    License :: OSI Approved :: MIT License
+    Programming Language :: Python
+    Programming Language :: Python :: 2
+    Programming Language :: Python :: 2.7
+    Programming Language :: Python :: 3
+    Programming Language :: Python :: 3.7
+    Programming Language :: Python :: Implementation :: PyPy
+keywords = "machine-learning"
+
+[files]
+packages =
+    sparse_coding_torch
+
+[bdist_wheel]
+universal=1
+
+[aliases]
+test=pytest

setup.py

+from setuptools import setup
+
+setup(
+    setup_requires=['pbr'],
+    pbr=True,
+)

BamcPreprocessor.py → sparse_coding_torch/BamcPreprocessor.py

@@ -68,6 +68,8 @@ class BamcPreprocessor(nn.Module):
             y_coord_max = torch.max(torch.where(slice_frame != 0)[0])
             y_coord_min = torch.min(torch.where(slice_frame != 0)[0])
             
+            print("height: {}".format(y_coord_max - y_coord_min))
+            
             s = slice_frame[y_coord_max-21:y_coord_max, :]
             
             pred = self.scale_classifier(s.unsqueeze(0).unsqueeze(0).float())

load_data.py → sparse_coding_torch/load_data.py

@@ -21,68 +21,14 @@ def load_balls_data(batch_size):
 def load_bamc_data(batch_size):   
     video_path = "/shared_data/bamc_data"
     
-    scale = 0.401
-    
-    # base_width = 1920
-    # base_height = 1080
-    # cropped_width = round(140/320 * base_width)
-    # cropped_height = round(140/180 * base_height)
-    
-    base_width = 1440
-    base_height = 454
-    
-    cropped_width = round(0.7 * base_width)
-    cropped_height = base_height
-    
-    width = round(cropped_width * scale)
-    height = round(cropped_height * scale)
-    
-    transforms = torchvision.transforms.Compose([torchvision.transforms.Grayscale(num_output_channels=1),
-                                                 torchvision.transforms.CenterCrop(size=(cropped_width, cropped_height)),
-                                                 torchvision.transforms.Resize(size=(width, height)), 
-                                                 MinMaxScaler(0, 255)])
-    dataset = VideoLoader(video_path, transform=transforms, num_frames=60)
-    
-    targets = dataset.get_labels()
-    
-    train_idx, test_idx = train_test_split(np.arange(len(targets)), test_size=0.2, shuffle=True, stratify=targets)
-    
-    train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
-    test_sampler = torch.utils.data.SubsetRandomSampler(test_idx)
-    
-    train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
-                                               # shuffle=True,
-                                               sampler=train_sampler)
-    test_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
-                                                    # shuffle=True,
-                                                    sampler=test_sampler)
-
-    return train_loader, test_loader
-
-
-def load_bamc_data2(batch_size):   
-    video_path = "/shared_data/cropped_pleural_lines/"
-    
-    scale = 0.401
-    
-    # base_width = 1920
-    # base_height = 1080
-    # cropped_width = round(140/320 * base_width)
-    # cropped_height = round(140/180 * base_height)
-    
-    base_width = 1440
-    base_height = 454
-    
-    cropped_width = round(0.7 * base_width)
-    cropped_height = base_height
-    
-    width = round(cropped_width * scale)
-    height = round(cropped_height * scale)
-    
-    transforms = torchvision.transforms.Compose([torchvision.transforms.Grayscale(num_output_channels=1),
-                                                 torchvision.transforms.CenterCrop(size=(cropped_width, cropped_height)),
-                                                 torchvision.transforms.Resize(size=(width, height)), 
-                                                 MinMaxScaler(0, 255)])
+    width = 350
+    height = 200
+
+    transforms = torchvision.transforms.Compose([VideoGrayScaler(),
+                                                 MinMaxScaler(0, 255),
+                                                 BamcPreprocessor(),
+                                                 torchvision.transforms.Resize(size=(height, width))
+                                                ])
     dataset = VideoLoader(video_path, transform=transforms, num_frames=60)
     
     targets = dataset.get_labels()