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Merge pull request #24 from Using-Namespace-System/main
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‎mosaiQue/README.md

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# mosaiQue
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variable quanvolutional kernel
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A Python library for applying quantum convolutions with PennyLane, designed to facilitate image processing and transformation through custom convolution layers and quantum computations.
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## Table of Contents
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- [Installation](#installation)
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- [Usage](#usage)
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- [API Reference](#api-reference)
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- [Contributing](#contributing)
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- [License](#license)
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## Installation
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To install the library, clone the repository and install the required dependencies including conda-build
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use develop to install the library
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sudo /opt/conda/bin/conda-develop -n QML-QPF PATH /workspaces/QML-QPF/mosaiQue
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## Usage
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Overview of the main classes in the library:
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### ConvolutionLayer4x4
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- Create a convolution layer with a custom kernel shape.
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- Fit the kernel to your dataset.
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- Transform the dataset using convolution.
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- Save a portion of the dataset or load a previously saved dataset.
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### QuantumLayer
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- Define a quantum kernel operation using PennyLane.
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- Create an operation layer using the quantum kernel.
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- Preprocess images using a Keras model that incorporates the quantum layer.
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### Kernel2d4x4
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- Initialize the kernel with a specific shape.
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- Fit the kernel to the image data.
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- Transform the image data into patches and reconstruct the original image data from patches.
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- Merge channels back to the original shape.
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## API Reference
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### `ConvolutionLayer4x4`
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#### Attributes
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- **name** (str): The name of the convolution layer.
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- **kernel** (kernels.Kernel2d4x4): The kernel used for convolution operations.
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#### Methods
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- **fit(dataset: np.ndarray)**: Fits the kernel to the provided dataset.
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- **transform(dataset: np.ndarray) -> np.ndarray**: Applies the convolution transformation to the dataset.
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- **post_transform(dataset: np.ndarray) -> np.ndarray**: Applies post-processing to the dataset after transformation.
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- **channel_merge(dataset: np.ndarray) -> np.ndarray**: Merges channels in the dataset.
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- **save(dataset: np.ndarray, variant: List[int])**: Saves a portion of the dataset to a `.npy` file based on the variant.
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- **open(variant: List[int]) -> np.ndarray**: Loads a saved dataset from a `.npy` file based on the variant.
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### `QuantumLayer`
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#### Attributes
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- **q_node** (qml.QNode): The quantum node to execute quantum computations.
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#### Methods
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- **call(inputs: tf.Tensor) -> tf.Tensor**: Executes the quantum layer on the input tensor.
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### `OperationLayer`
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#### Attributes
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- **q_layer** (QuantumLayer): The quantum layer for processing inputs.
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- **kernel_operation** (qml.QNode): The QNode for the kernel operation.
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#### Methods
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- **pre_op() -> keras.Model**: Initializes and returns a custom Keras model used to preprocess images.
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### `Kernel2d4x4`
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#### Attributes
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- **input_shape** (list[int]): The shape of the input data.
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- **kernel_shape** (list[int]): The shape of the kernel.
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#### Methods
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- **fit(X: np.ndarray)**: Fits the kernel to the input data by storing its shape.
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- **transform(X: np.ndarray) -> np.ndarray**: Splits the input data into smaller patches based on the kernel shape.
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- **post_transform(X: np.ndarray) -> np.ndarray**: Reconstructs the image data from patches.
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- **channel_merge(X: np.ndarray) -> np.ndarray**: Merges channels to restore the original order.
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## Contributing
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Contributions are welcome! Please feel free to submit issues or pull requests. To contribute:
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1. Fork the repository.
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2. Create a new branch for your feature or bug fix.
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3. Make your changes and commit them.
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4. Push to your branch and create a pull request.
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## License
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This project is licensed under the GPL-3.0 License. See the license file for details.

‎mosaiQue/image.png

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‎mosaiQue/mosaique/models/__init__.py

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# __init__.py
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# This module defines custom transformations for image convolution.
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# Author: Brian Recktenwall-Calvet
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# Date: 12-18-2024
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# Version: 1.0
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import os
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import pathlib
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import mosaique.models.kernels
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import numpy as np
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from typing import Any, Callable
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from typing import Any, List, Optional
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class ConvolutionLayer4x4:
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"""
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A class representing a 4x4 convolution layer using a specified kernel.
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Attributes:
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_name (str): The name of the convolution layer.
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_kernel (kernels.Kernel2d4x4): An instance of the kernel used for convolution operations.
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Methods:
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fit(dataset): Fits the kernel to the provided dataset.
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transform(dataset): Applies the convolution transformation to the dataset.
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post_transform(dataset): Applies post-processing to the dataset after transformation.
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channel_merge(dataset): Merges channels in the dataset.
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save(dataset, variant): Saves a portion of the dataset to a .npy file based on the variant.
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open(variant): Loads a saved dataset from a .npy file based on the variant.
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"""
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_name: str
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_kernel: kernels.Kernel2d4x4
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@property
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def name(self) -> str :
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def name(self) -> str:
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"""Gets the name of the convolution layer."""
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return self._name
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@name.setter
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def name(self, value: str):
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"""Sets the name of the convolution layer."""
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self._name = value
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@property
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def kernel(self) -> kernels.Kernel2d4x4:
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"""Gets the kernel used for convolution operations."""
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return self._kernel
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@kernel.setter
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def kernel(self, value: kernels.Kernel2d4x4):
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"""Sets the kernel used for convolution operations."""
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self._kernel = value
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def __init__(self, name: str, kernel_shape:[int]=None):
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def __init__(self, name: str, kernel_shape: Optional[List[int]] = None):
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"""
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Initializes the ConvolutionLayer4x4 with a given name and kernel shape.
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Args:
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name (str): The name of the convolution layer.
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kernel_shape (Optional[List[int]]): The shape of the kernel (default is [2, 2]).
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Raises:
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ValueError: If kernel_shape is not a valid shape for the kernel.
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"""
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if kernel_shape is None:
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kernel_shape = [2, 2]
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self.name = name
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self.kernel = kernels.Kernel2d4x4(kernel_shape)
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def fit(self, dataset: np.ndarray[..., np.dtype[Any]]):
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"""
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Fits the kernel to the provided dataset.
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Args:
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dataset (np.ndarray): The input dataset used to fit the kernel.
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"""
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self.kernel.fit(dataset)
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def transform(self, dataset: np.ndarray[..., np.dtype[Any]]):
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def transform(self, dataset: np.ndarray[..., np.dtype[Any]]) -> np.ndarray:
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"""
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Applies the convolution transformation to the dataset.
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Args:
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dataset (np.ndarray): The input dataset to transform.
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Returns:
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np.ndarray: The transformed dataset after applying the convolution.
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"""
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return self.kernel.transform(dataset)
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def post_transform(self, dataset: np.ndarray[..., np.dtype[Any]]):
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def post_transform(self, dataset: np.ndarray[..., np.dtype[Any]]) -> np.ndarray:
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"""
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Applies post-processing to the dataset after transformation.
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Args:
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dataset (np.ndarray): The input dataset to post-process.
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Returns:
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np.ndarray: The post-processed dataset.
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"""
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return self.kernel.post_transform(dataset)
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def channel_merge(self, dataset: np.ndarray[..., np.dtype[Any]]):
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def channel_merge(self, dataset: np.ndarray[..., np.dtype[Any]]) -> np.ndarray:
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"""
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Merges channels in the dataset, typically for multi-channel inputs.
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Args:
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dataset (np.ndarray): The input dataset whose channels will be merged.
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Returns:
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np.ndarray: The dataset with merged channels.
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"""
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return self.kernel.channel_merge(dataset)
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def save(self, dataset: np.ndarray[..., np.dtype[Any]], variant:[int]):
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variant_string = ''.join(map(str,variant))
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def save(self, dataset: np.ndarray[..., np.dtype[Any]], variant: List[int]):
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"""
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Saves a portion of the dataset to a .npy file based on the variant.
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Args:
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dataset (np.ndarray): The dataset to save.
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variant (List[int]): A list of indices specifying which channels to save.
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"""
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variant_string = ''.join(map(str, variant))
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workdir = str(pathlib.Path().resolve()) + "/" + self.name
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os.makedirs(workdir, exist_ok=True)
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np.save(os.path.join(workdir, variant_string), dataset[:,:,:,variant])
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np.save(os.path.join(workdir, variant_string), dataset[:, :, :, variant])
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def open(self, variant: List[int]) -> np.ndarray:
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"""
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Loads a saved dataset from a .npy file based on the variant.
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def open(self, variant:[int]):
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variant_string = ''.join(map(str,variant))
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Args:
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variant (List[int]): A list of indices specifying which channels to load.
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Returns:
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np.ndarray: The loaded dataset.
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Raises:
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FileNotFoundError: If the specified file does not exist.
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"""
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variant_string = ''.join(map(str, variant))
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workdir = str(pathlib.Path().resolve()) + "/" + self.name
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return np.load(os.path.join(workdir, variant_string + '.npy'))
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