adapt to medical dataset

.gitignore

@@ -0,0 +1,136 @@
+#ignore some dirs and files 
+checkpoints
+.neptune
+runs
+sbatch_log
+exceptions
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

custom_datasets.py

@@ -0,0 +1,228 @@
+import os
+import os.path
+import numpy as np
+from PIL import Image
+import torch.utils.data as data
+import torchvision.datasets as datasets
+import tifffile 
+import h5py
+
+
+
+
+def pil_loader(path):
+    # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
+    with open(path, 'rb') as f:
+        img = Image.open(f)
+        return img.convert('RGB')
+
+def default_loader(path):
+    return pil_loader(path)
+
+def pil_loader_tifffile(path):
+    with tifffile.TiffFile(path) as tif:
+        for page in tif.pages:
+            img = page.asarray()
+        img = Image.fromarray(img)
+    return img.convert('RGB')
+
+
+
+
+class custom_histopathology_classic(datasets.ImageFolder):
+
+    """histopathology Dataset object: https://zenodo.org/record/1214456#.ZBf4GnbMKck
+    Args:
+        root (string): Root directory path of dataset.
+        train (bool): load either training set (True) or test set (False) (default: True)
+        transform: A function/transform that takes in
+            a sample and returns a transformed version.
+            E.g, ``transforms.RandomCrop`` for images.
+        target_transform: A function/transform that takes
+            in the target and transforms it.
+     Attributes:
+        samples (list): List of (sample path, class_index) tuples
+        targets (list): The class_index value for each image in the dataset
+
+    """
+    def __init__(self, root, transform, train = True):
+        super().__init__(root, transform)
+        self.root = root 
+        self.transform = transform
+        self.train = train
+        self.loader = pil_loader_tifffile
+
+    def __getitem__(self, index):
+        """
+        Args:
+            index (int): Index
+
+        Returns:
+            tuple: (sample, target) where target is class_index of the target class.
+        """
+        path, target = self.samples[index]
+        #print (path,target)
+        sample = self.loader(path)
+
+        if self.transform is not None:
+            sample = self.transform(sample)
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return sample, target
+
+    def __len__(self) -> int:
+        return len(self.samples)
+
+
+
+
+class custom_COVID19_Xray_faster(data.Dataset):
+    """COVID-QU-Ex Dataset object
+    Args:
+        root (string): Root directory path of dataset.
+        train (bool): load either training set (True) or test set (False) (default: True)
+        transform: A function/transform that takes in
+            a sample and returns a transformed version.
+            E.g, ``transforms.RandomCrop`` for images.
+        target_transform: A function/transform that takes
+            in the target and transforms it.
+        seed: random seed for shuffling classes or instances (default=10)
+     Attributes:
+        samples (list): List of (sample path, class_index) tuples
+        targets (list): The class_index value for each image in the dataset
+    """
+
+    def __init__(self, root = "C:/Users/Station5/learning-with-noisy-labels-benchmark/data/", train=True, transform=None, target_transform=None, num_classes= 3, seed=1):
+
+        self.root = root
+        self.as_rgb = True
+        if train:
+           self.mode = "train"
+        else:
+           self.mode = "valid"
+
+        with h5py.File(os.path.join(root,"COVID-QU-Dataset/", str(self.mode)+".hdf5"), 'r') as hf:
+            self.targets = hf["dataset"]["targets"][:]
+            self.images = hf["dataset"]["images"][:]
+
+        self.transform = transform
+        self.target_transform = target_transform
+
+    def __getitem__(self, index):
+        """
+        Args:
+            index (int): Index
+        Returns:
+            tuple: (image, target) where target is class_index of the target class.
+        """
+
+        index = int(index)
+        image, target = self.images[index], self.targets[index]
+        image = Image.fromarray(image)
+
+        if self.as_rgb:
+            image = image.convert('RGB')
+
+        if self.transform is not None:
+            image = self.transform(image)
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+
+        return image, target
+
+    def __len__(self):
+        return len(self.targets)
+
+    def __repr__(self):
+        fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
+        fmt_str += '    Number of datapoints: {}\n'.format(self.__len__())
+        fmt_str += '    Root Location: {}\n'.format(self.root)
+        tmp = '    Transforms (if any): '
+        fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
+        tmp = '    Target Transforms (if any): '
+        fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
+        return fmt_str
+
+
+class custom_histopathology_faster(data.Dataset):
+    """histopathology Dataset object: https://zenodo.org/record/1214456#.ZBf4GnbMKck
+    Args:
+        root (string): Root directory path of dataset.
+        train (bool): load either training set (True) or test set (False) (default: True)
+        transform: A function/transform that takes in
+            a sample and returns a transformed version.
+            E.g, ``transforms.RandomCrop`` for images.
+        target_transform: A function/transform that takes
+            in the target and transforms it.
+     Attributes:
+        samples (list): List of (sample path, class_index) tuples
+        targets (list): The class_index value for each image in the dataset
+
+    """
+
+    def __init__(self, root = "C:/Users/Station5/learning-with-noisy-labels-benchmark/data/", train=True, transform=None, target_transform=None, num_classes= 9, seed=1):
+
+        self.root = root
+        self.as_rgb = True
+        if train:
+           self.mode = "train"
+        else:
+           self.mode = "valid"
+
+        with h5py.File(os.path.join(root,"histopathology/", str(self.mode)+".hdf5"), 'r') as hf:
+            self.targets = hf["dataset"]["targets"][:]
+            self.images = hf["dataset"]["images"][:]
+
+        ### select only top 100 examples of each class, this is done for debugging only
+        # all_targets = np.unique(self.targets)
+        # curated_path_list = []
+        # curated_target_list =[]
+        # images = np.array(self.images)
+        # targets = np.array(self.targets)
+        # for i in all_targets:
+        #     curated_path_list.extend(images[np.where(targets == i)][0:1000])
+        #     curated_target_list.extend(targets[np.where(targets == i)][0:1000])
+        # self.images = curated_path_list 
+        # self.targets = curated_target_list
+
+        self.transform = transform
+        self.target_transform = target_transform
+
+    def __getitem__(self, index):
+        """
+        Args:
+            index (int): Index
+        Returns:
+            tuple: (image, target) where target is class_index of the target class.
+        """
+
+        index = int(index)
+        image, target = self.images[index], self.targets[index]
+        image = Image.fromarray(image)
+
+        if self.as_rgb:
+            image = image.convert('RGB')
+
+        if self.transform is not None:
+            image = self.transform(image)
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return image, target
+
+    def __len__(self):
+        return len(self.targets)
+
+    def __repr__(self):
+        fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
+        fmt_str += '    Number of datapoints: {}\n'.format(self.__len__())
+        fmt_str += '    Root Location: {}\n'.format(self.root)
+        tmp = '    Transforms (if any): '
+        fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
+        tmp = '    Target Transforms (if any): '
+        fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
+        return fmt_str
+
+

hubconf.py

@@ -6,6 +6,7 @@
 
 import torch
 from torchvision.models.resnet import resnet50 as _resnet50
+from torchvision.models.resnet import resnet18 as _resnet18
 
 dependencies = ['torch', 'torchvision']
 
@@ -17,3 +18,7 @@ def resnet50(pretrained=True, **kwargs):
         state_dict = torch.hub.load_state_dict_from_url(url, map_location='cpu')
         model.load_state_dict(state_dict, strict=False)
     return model
+
+def resnet18(pretrained=True, **kwargs):
+    model = _resnet18(pretrained=False, **kwargs)
+    return model