testing on rotated MNIST

Author: renebidart

.flake8

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+[flake8]
+ignore = F401, F403
+max-line-length = 120
+exclude =
+    .git,
+    __pycache__,

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
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+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# 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/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# input data, saved log, checkpoints
+data/
+input/
+saved/
+datasets/
+
+# editor, os cache directory
+.vscode/
+.idea/
+__MACOSX/

README.md

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+# equivariance

base/__init__.py

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+from .base_data_loader import *
+from .base_model import *
+from .base_trainer import *

base/base_data_loader.py

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+import numpy as np
+from torch.utils.data import DataLoader
+from torch.utils.data.dataloader import default_collate
+from torch.utils.data.sampler import SubsetRandomSampler
+
+
+class BaseDataLoader(DataLoader):
+    """
+    Base class for all data loaders
+    """
+    def __init__(self, dataset, batch_size, shuffle, validation_split, num_workers, collate_fn=default_collate):
+        self.validation_split = validation_split
+        self.shuffle = shuffle
+        
+        self.batch_idx = 0
+        self.n_samples = len(dataset)
+
+        self.sampler, self.valid_sampler = self._split_sampler(self.validation_split)
+
+        self.init_kwargs = {
+            'dataset': dataset,
+            'batch_size': batch_size,
+            'shuffle': self.shuffle,
+            'collate_fn': collate_fn,
+            'num_workers': num_workers
+            }
+        super(BaseDataLoader, self).__init__(sampler=self.sampler, **self.init_kwargs)
+
+    def _split_sampler(self, split):
+        if split == 0.0:
+            return None, None
+
+        idx_full = np.arange(self.n_samples)
+
+        np.random.seed(0) 
+        np.random.shuffle(idx_full)
+
+        len_valid = int(self.n_samples * split)
+
+        valid_idx = idx_full[0:len_valid]
+        train_idx = np.delete(idx_full, np.arange(0, len_valid))
+        
+        train_sampler = SubsetRandomSampler(train_idx)
+        valid_sampler = SubsetRandomSampler(valid_idx)
+        
+        # turn off shuffle option which is mutually exclusive with sampler
+        self.shuffle = False
+        self.n_samples = len(train_idx)
+
+        return train_sampler, valid_sampler
+        
+    def split_validation(self):
+        if self.valid_sampler is None:
+            return None
+        else:
+            return DataLoader(sampler=self.valid_sampler, **self.init_kwargs)
+    

base/base_model.py

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+import logging
+import torch.nn as nn
+import numpy as np
+
+
+class BaseModel(nn.Module):
+    """
+    Base class for all models
+    """
+    def __init__(self):
+        super(BaseModel, self).__init__()
+        self.logger = logging.getLogger(self.__class__.__name__)
+
+    def forward(self, *input):
+        """
+        Forward pass logic
+
+        :return: Model output
+        """
+        raise NotImplementedError
+
+    def summary(self):
+        """
+        Model summary
+        """
+        model_parameters = filter(lambda p: p.requires_grad, self.parameters())
+        params = sum([np.prod(p.size()) for p in model_parameters])
+        self.logger.info('Trainable parameters: {}'.format(params))
+        self.logger.info(self)
+
+    def __str__(self):
+        """
+        Model prints with number of trainable parameters
+        """
+        model_parameters = filter(lambda p: p.requires_grad, self.parameters())
+        params = sum([np.prod(p.size()) for p in model_parameters])
+        return super(BaseModel, self).__str__() + '\nTrainable parameters: {}'.format(params)
+        # print(super(BaseModel, self))