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# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import torch
from monai._extensions.loader import load_module
__all__ = ["GaussianMixtureModel"]
class GaussianMixtureModel:
"""
Takes an initial labeling and uses a mixture of Gaussians to approximate each classes
distribution in the feature space. Each unlabeled element is then assigned a probability
of belonging to each class based on it's fit to each classes approximated distribution.
See:
https://en.wikipedia.org/wiki/Mixture_model
"""
def __init__(self, channel_count: int, mixture_count: int, mixture_size: int, verbose_build: bool = False):
"""
Args:
channel_count: The number of features per element.
mixture_count: The number of class distributions.
mixture_size: The number Gaussian components per class distribution.
verbose_build: If ``True``, turns on verbose logging of load steps.
"""
if not torch.cuda.is_available():
raise NotImplementedError("GaussianMixtureModel is currently implemented for CUDA.")
self.channel_count = channel_count
self.mixture_count = mixture_count
self.mixture_size = mixture_size
self.compiled_extension = load_module(
"gmm",
{"CHANNEL_COUNT": channel_count, "MIXTURE_COUNT": mixture_count, "MIXTURE_SIZE": mixture_size},
verbose_build=verbose_build,
)
self.params, self.scratch = self.compiled_extension.init()
def reset(self):
"""
Resets the parameters of the model.
"""
self.params, self.scratch = self.compiled_extension.init()
def learn(self, features, labels):
"""
Learns, from scratch, the distribution of each class from the provided labels.
Args:
features (torch.Tensor): features for each element.
labels (torch.Tensor): initial labeling for each element.
"""
self.compiled_extension.learn(self.params, self.scratch, features, labels)
def apply(self, features):
"""
Applies the current model to a set of feature vectors.
Args:
features (torch.Tensor): feature vectors for each element.
Returns:
output (torch.Tensor): class assignment probabilities for each element.
"""
return _ApplyFunc.apply(self.params, features, self.compiled_extension)
class _ApplyFunc(torch.autograd.Function):
@staticmethod
def forward(ctx, params, features, compiled_extension):
return compiled_extension.apply(params, features)
@staticmethod
def backward(ctx, grad_output):
raise NotImplementedError("GMM does not support backpropagation")
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