diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index e3ca3db2..0aee0bb2 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -5,138 +5,133 @@ on: pull_request: push: release: - types: - - published + types: [published] + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true jobs: pre-commit: name: Format runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 - with: - python-version: "3.10" - - uses: pre-commit/action@v3.0.1 - with: - extra_args: --hook-stage manual --all-files - - checks: - name: Check Python ${{ matrix.python-version }} on ${{ matrix.runs-on }} - runs-on: ${{ matrix.runs-on }} + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: '3.10' + - uses: pre-commit/action@v3.0.1 + with: + extra_args: --hook-stage manual --all-files + + tests: + name: ${{ matrix.test-suite }}, py ${{ matrix.python-version }}, ${{ matrix.os }} + runs-on: ${{ matrix.os }} strategy: fail-fast: false matrix: - python-version: [ "3.9", "3.11", "3.12"] - runs-on: [ubuntu-latest, macos-latest, windows-latest] + python-version: ['3.10', '3.13'] + os: [ubuntu-latest, macos-latest, windows-latest] + test-suite: [core, actr, pytorch, neuroml, tensorflow, psyneulink] exclude: - - runs-on: macos-latest - python-version: "3.9" + - os: windows-latest + test-suite: tensorflow steps: - - uses: actions/checkout@v4 - - - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python-version }} - - # Rely on version of modelspec from PyPI as set by setup.cfg... - #- name: Install modelspec - # run: python -m pip install git+https://github.com/ModECI/modelspec.git@main - - # Rely on version of NeuroMLlite from PyPI as set by setup.py... - # - name: Install NeuroMLlite - # run: python -m pip install NeuroMLlite>=0.5.0 - - # Rely on version of PsyNeuLink from PyPI as set by setup.py... - #- name: Install specific PsyNeuLink branch - # run: python -m pip install git+https://github.com/ModECI/PsyNeuLink@devel - - - name: Install HDF5 for pytables on macos-14/latest - if: ${{ matrix.runs-on == 'macos-latest' }} - run: | - brew install hdf5 - - - name: Install core package - run: | - pip install 'numpy<2.0.0' # due to lingering issues with other modules & numpy v2... - python -m pip install .[dev] - - - name: Version info for installed packages - run: | - pip list - - - name: Test core package - run: | - python -m pytest -m coremdf tests/ - - - name: Install most optional dependencies - run: | - python -m pip install .[optional] - - - name: Version info for optional installed packages - run: | - pip list - - - name: Install graphviz - uses: ts-graphviz/setup-graphviz@v2 - with: - # Skip to run brew update command on macOS. - macos-skip-brew-update: 'true' # default false - - - name: Test interface ACT-R - run: | - python -m pytest -v -m "actr" tests/ - - - name: Test interface PyTorch - run: | - python -m pytest -v -m "pytorch" tests/ - - - name: Test interface NeuroML - run: | - python -m pip install .[neuroml] - python -m pytest -v -m "neuroml" tests/ - - - name: Test interface TensorFlow - run: | - python -m pip install .[tensorflow] - dot -V - python -m pytest -v -m "tensorflow" tests/ - - - name: Test interface PsyNeuLink - run: | - python -m pip install .[psyneulink] - python -m pytest -v -m "psyneulink" tests/ - - - name: Build Documentation - run: | - cd docs - python generate.py - cd sphinx - make clean - make html - - - name: Final version info for optional installed packages - run: | - pip list + - uses: actions/checkout@v4 + + - name: Setup Python + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + + - name: Install uv and set the python version + uses: astral-sh/setup-uv@v6 + with: + python-version: ${{ matrix.python-version }} + + - name: Install HDF5 for pytables on macos-14/latest + if: ${{ matrix.runs-on == 'macos-latest' }} + run: | + brew install hdf5 + + - name: Install graphviz + uses: ts-graphviz/setup-graphviz@v2 + with: + # Skip to run brew update command on macOS. + macos-skip-brew-update: 'true' # default false + + - name: Install extras for ${{ matrix.test-suite }} + shell: bash + run: | + uv sync --dev + case "${{ matrix.test-suite }}" in + pytorch) uv sync --extra "optional" --dev ;; + neuroml) uv sync --extra "neuroml" --dev ;; + tensorflow) uv sync --extra "tensorflow" --dev ;; + psyneulink) uv sync --extra "psyneulink" --dev ;; + esac + + - name: Run ${{ matrix.test-suite }} tests + shell: bash + run: | + MARKER="${{ matrix.test-suite }}" + if [ "${{ matrix.test-suite }}" = "core" ]; then + MARKER="coremdf" + fi + uv run pytest -v -m "${MARKER}" tests/ + + docs: + name: Build Documentation + needs: tests + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: '3.10' + - name: Install uv and set the python version + uses: astral-sh/setup-uv@v6 + with: + python-version: '3.10' + - name: Generate and Build Docs + run: | + uv sync --group docs + cd docs + uv run generate.py + cd sphinx + uv run make clean + uv run make html dist: name: Distribution build runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - - name: Build sdist and wheel - run: pipx run --spec build pyproject-build - - - uses: actions/upload-artifact@v4 - with: - path: dist - - - uses: pypa/gh-action-pypi-publish@v1.4.1 - if: github.event_name == 'release' && github.event.action == 'published' - with: - user: __token__ - # Remember to generate this and set it in "GitHub Secrets" - password: ${{ secrets.pypi_password }} + - uses: actions/checkout@v4 + - name: Build sdist & wheel + run: pipx run --spec build pyproject-build + - uses: actions/upload-artifact@v4 + with: + path: dist + - uses: pypa/gh-action-pypi-publish@v1.4.1 + if: github.event_name == 'release' && github.event.action == 'published' + with: + user: __token__ + password: ${{ secrets.pypi_password }} + + all-passed: + runs-on: ubuntu-latest + needs: + - pre-commit + - tests + - docs + - dist + if: always() + steps: + - name: Fail if any dependency did not succeed + run: | + echo "Upstream results: ${{ join(needs.*.result, ', ') }}" + # If any result is failure/cancelled/skipped, fail this job + if [[ "${{ contains(needs.*.result, 'failure') || contains(needs.*.result, 'cancelled') || contains(needs.*.result, 'skipped') }}" == "true" ]]; then + exit 1 + fi diff --git a/.github/workflows/ci_test_all.yml b/.github/workflows/ci_test_all.yml index e540681e..8fc523a3 100644 --- a/.github/workflows/ci_test_all.yml +++ b/.github/workflows/ci_test_all.yml @@ -2,9 +2,9 @@ name: CI Test script on: push: - branches: [ main, development, experimental, test*, nml* ] + branches: [ main, development, experimental, test*, nml*, version* ] pull_request: - branches: [ main, development, experimental, test*, nml* ] + branches: [ main, development, experimental, test*, nml*, version* ] jobs: @@ -14,7 +14,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: [ "3.10"] + python-version: [ "3.13"] runs-on: [ubuntu-latest] steps: @@ -43,10 +43,3 @@ jobs: - name: Version info for installed packages run: | pip list - - - - - - name: Final version info for optional installed packages - run: | - pip list diff --git a/README.md b/README.md index d5381e69..f9192355 100644 --- a/README.md +++ b/README.md @@ -13,6 +13,13 @@ **Note: MDF is still in development! See the [open issues related to the specification](https://github.com/ModECI/MDF/issues?q=is%3Aissue+is%3Aopen+label%3Aspecification) or go [here](http://modeci.org/#contactPage) to get in contact regarding MDF.** *The MDF format was first proposed following a meeting organised at Princeton in July 2019 by Russ Poldrack of the Center for Reproducible Neuroscience (CRN) at Stanford and the [Brain Imaging Data Standard (BIDS)](https://bids.neuroimaging.io/) initiative. For more on the previous work in this area, see [here](https://github.com/OpenSourceBrain/PsyNeuLinkShowcase/tree/master/BIDS-MDF).* +## Paper introducing MDF + +The background to the ModECI project, the motivation for developing the Model Description Format, and the initial Python implementation of the language have been described in a NeuroView article in the Neuron journal: + +*Integrating model development across computational neuroscience, cognitive science, and machine learning* +Padraig Gleeson, Sharon Crook, David Turner, Katherine Mantel, Mayank Raunak, Ted Willke and Jonathan D. Cohen, April 25, 2023 DOI: [https://doi.org/10.1016/j.neuron.2023.03.037](https://doi.org/10.1016/j.neuron.2023.03.037) + ## Overview diff --git a/docs/MDF_function_specifications.json b/docs/MDF_function_specifications.json index cde46305..9d0ea36a 100644 --- a/docs/MDF_function_specifications.json +++ b/docs/MDF_function_specifications.json @@ -686,7 +686,7 @@ "expression_string": "onnx_ops.lppool(X, auto_pad, kernel_shape, p, pads, strides)" }, "onnx::MatMul": { - "description": "\nMatrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html\n", + "description": "\nMatrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).\n", "arguments": [ "A", "B" @@ -694,7 +694,7 @@ "expression_string": "onnx_ops.matmul(A, B)" }, "onnx::MatMulInteger": { - "description": "\nMatrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html.\nThe production MUST never overflow. The accumulation may overflow if and only if in 32 bits.\n", + "description": "\nMatrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).\nThe production MUST never overflow. The accumulation may overflow if and only if in 32 bits.\n", "arguments": [ "A", "B", @@ -711,7 +711,7 @@ "expression_string": "onnx_ops.max(data_0)" }, "onnx::MaxPool": { - "description": "\n MaxPool consumes an input tensor X and applies max pooling across\n the tensor according to kernel sizes, stride sizes, and pad lengths.\n max pooling consisting of computing the max on all values of a\n subset of the input tensor according to the kernel size and downsampling the\n data into the output tensor Y for further processing. The output spatial shape is calculated differently\n depending on whether explicit padding is used, where pads is employed, or auto padding is used, where auto_pad is utilized.\n With explicit padding (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html?highlight=maxpool#torch.nn.MaxPool2d):\n ```\n output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)\n ```\n or\n ```\n output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)\n ```\n if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. Sliding windows that would start in the right padded region are ignored.\n\n `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled:\n ```\n VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i])\n SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])\n ```\n or when ceil_mode is disabled (https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D):\n ```\n VALID: output_spatial_shape[i] = floor((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i]) + 1\n SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = floor((input_spatial_shape[i] - 1) / strides_spatial_shape[i]) + 1\n ```\n And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:\n ```\n pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i]\n ```\n The output of each pooling window is maximum number of elements exclude pad. \n ", + "description": "\n MaxPool consumes an input tensor X and applies max pooling across\n the tensor according to kernel sizes, stride sizes, and pad lengths.\n max pooling consisting of computing the max on all values of a\n subset of the input tensor according to the kernel size and downsampling the\n data into the output tensor Y for further processing. The output spatial shape is calculated differently\n depending on whether explicit padding is used, where pads is employed, or auto padding is used, where auto_pad is utilized.\n With explicit padding (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html?highlight=maxpool#torch.nn.MaxPool2d):\n ```\n output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)\n ```\n or\n ```\n output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)\n ```\n if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`.\n\n `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled:\n ```\n VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i])\n SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])\n ```\n or when ceil_mode is disabled (https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D):\n ```\n VALID: output_spatial_shape[i] = floor((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i]) + 1\n SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = floor((input_spatial_shape[i] - 1) / strides_spatial_shape[i]) + 1\n ```\n And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:\n ```\n pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i]\n ```\n The output of each pooling window is maximum number of elements exclude pad. \n ", "arguments": [ "X" ], @@ -900,7 +900,7 @@ "expression_string": "onnx_ops.qlinearconv(x, x_scale, x_zero_point, w, w_scale, w_zero_point, y_scale, y_zero_point, B, auto_pad, dilations, group, kernel_shape, pads, strides)" }, "onnx::QLinearMatMul": { - "description": "\nMatrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html.\nIt consumes two quantized input tensors, their scales and zero points, scale and zero point of output,\nand computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point).\nFor (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details.\nScale and zero point must have same shape. They must be either scalar (per tensor) or N-D tensor\n(per row for 'a' and per column for 'b'). Scalar refers to per tensor quantization whereas N-D refers to per row\nor per column quantization. If the input is 2D of shape [M, K] then zero point and scale tensor may be\nan M element vector [v_1, v_2, ..., v_M] for per row quantization and K element vector of shape [v_1, v_2, ..., v_K]\nfor per column quantization. If the input is N-D tensor with shape [D1, D2, M, K] then zero point and scale tensor may\nhave shape [D1, D2, M, 1] for per row quantization and shape [D1, D2, 1, K] for per column quantization.\nProduction must never overflow, and accumulation may overflow if and only if in 32 bits.\n", + "description": "\nMatrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).\nIt consumes two quantized input tensors, their scales and zero points, scale and zero point of output,\nand computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point).\nFor (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details.\nScale and zero point must have same shape. They must be either scalar (per tensor) or N-D tensor\n(per row for 'a' and per column for 'b'). Scalar refers to per tensor quantization whereas N-D refers to per row\nor per column quantization. If the input is 2D of shape [M, K] then zero point and scale tensor may be\nan M element vector [v_1, v_2, ..., v_M] for per row quantization and K element vector of shape [v_1, v_2, ..., v_K]\nfor per column quantization. If the input is N-D tensor with shape [D1, D2, M, K] then zero point and scale tensor may\nhave shape [D1, D2, M, 1] for per row quantization and shape [D1, D2, 1, K] for per column quantization.\nProduction must never overflow, and accumulation may overflow if and only if in 32 bits.\n", "arguments": [ "a", "a_scale", diff --git a/docs/MDF_function_specifications.md b/docs/MDF_function_specifications.md index e8d6d894..32564fa1 100644 --- a/docs/MDF_function_specifications.md +++ b/docs/MDF_function_specifications.md @@ -1685,7 +1685,7 @@ Python version: `onnx_ops.lppool(X, auto_pad, kernel_shape, p, pads, strides)` ## MatMul

-Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html +Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).

Python version: `onnx_ops.matmul(A, B)` @@ -1695,7 +1695,7 @@ Python version: `onnx_ops.matmul(A, B)` ## MatMulInteger

-Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. +Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.

@@ -1732,7 +1732,7 @@ Python version: `onnx_ops.max(data_0)` ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` - if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. Sliding windows that would start in the right padded region are ignored. + if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled: ``` @@ -2237,7 +2237,7 @@ Python version: `onnx_ops.qlinearconv(x, x_scale, x_zero_point, w, w_scale, w_ze ## QLinearMatMul

-Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. +Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). It consumes two quantized input tensors, their scales and zero points, scale and zero point of output, and computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point). For (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. diff --git a/docs/MDF_function_specifications.yaml b/docs/MDF_function_specifications.yaml index af73c748..36129f8f 100644 --- a/docs/MDF_function_specifications.yaml +++ b/docs/MDF_function_specifications.yaml @@ -1617,7 +1617,7 @@ onnx::LpPool: onnx::MatMul: description: ' - Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html + Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). ' arguments: @@ -1627,7 +1627,7 @@ onnx::MatMul: onnx::MatMulInteger: description: ' - Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. + Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). The production MUST never overflow. The accumulation may overflow if and only if in 32 bits. @@ -1668,8 +1668,7 @@ onnx::MaxPool: \ 1)\n ```\n or\n ```\n output_spatial_shape[i] = ceil((input_spatial_shape[i]\ \ + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i]\ \ + 1)\n ```\n if ceil_mode is enabled. `pad_shape[i]` is the sum of pads\ - \ along axis `i`. Sliding windows that would start in the right padded region\ - \ are ignored.\n\n `auto_pad` is a DEPRECATED attribute. If you are using\ + \ along axis `i`.\n\n `auto_pad` is a DEPRECATED attribute. If you are using\ \ them currently, the output spatial shape will be following when ceil_mode\ \ is enabled:\n ```\n VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i]\ \ - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i])\n\ @@ -2090,7 +2089,7 @@ onnx::QLinearConv: onnx::QLinearMatMul: description: ' - Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. + Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). It consumes two quantized input tensors, their scales and zero points, scale and zero point of output, diff --git a/docs/MDF_specification.json b/docs/MDF_specification.json index 202a92c7..4f43de9b 100644 --- a/docs/MDF_specification.json +++ b/docs/MDF_specification.json @@ -6,7 +6,7 @@ "definition": "The top level construct in MDF is Model, which may contain multiple :class:`Graph` objects and model attribute(s)", "allowed_parameters": { "metadata": { - "type": "Union[Any, NoneType]", + "type": "Optional", "description": "Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values." }, "id": { @@ -22,13 +22,13 @@ "description": "Information on what application generated/saved this file" }, "onnx_opset_version": { - "type": "Union[str, NoneType]", + "type": "Optional", "description": "The ONNX opset used for any ONNX functions in this model." } }, "allowed_children": { "graphs": { - "type": "Graph", + "type": "List", "description": "The collection of graphs that make up the MDF model." } } @@ -37,7 +37,7 @@ "definition": "A directed graph consisting of :class:`~Node`s (with :class:`~Parameter`s and :class:`~Function`s evaluated internally) connected via :class:`~Edge`s.", "allowed_parameters": { "metadata": { - "type": "Union[Any, NoneType]", + "type": "Optional", "description": "Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values." }, "id": { @@ -45,21 +45,21 @@ "description": "A unique identifier for this Graph" }, "parameters": { - "type": "Union[Any, NoneType]", + "type": "Optional", "description": "Dictionary of global parameters for the Graph" }, "conditions": { - "type": "Union[ConditionSet, NoneType]", + "type": "Optional", "description": "The ConditionSet stored as dictionary for scheduling of the Graph" } }, "allowed_children": { "nodes": { - "type": "Node", + "type": "List", "description": "One or more :class:`Node`(s) present in the graph" }, "edges": { - "type": "Edge", + "type": "List", "description": "Zero or more :class:`Edge`(s) present in the graph" } } @@ -68,7 +68,7 @@ "definition": "A self contained unit of evaluation receiving input from other nodes on :class:`InputPort`(s). The values from these are processed via a number of :class:`Function`(s) and one or more final values\nare calculated on the :class:`OutputPort`(s)", "allowed_parameters": { "metadata": { - "type": "Union[Any, NoneType]", + "type": "Optional", "description": "Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values." }, "id": { @@ -78,19 +78,19 @@ }, "allowed_children": { "input_ports": { - "type": "InputPort", + "type": "List", "description": "Dictionary of the :class:`InputPort` objects in the Node" }, "functions": { - "type": "Function", + "type": "List", "description": "The :class:`Function`(s) for computation the node" }, "parameters": { - "type": "Parameter", + "type": "List", "description": "Dictionary of :class:`Parameter`(s) for the node" }, "output_ports": { - "type": "OutputPort", + "type": "List", "description": "The :class:`OutputPort`(s) containing evaluated quantities from the node" } } @@ -99,7 +99,7 @@ "definition": "The :class:`InputPort` is an attribute of a Node which allows external information to be input to the Node", "allowed_parameters": { "metadata": { - "type": "Union[Any, NoneType]", + "type": "Optional", "description": "Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values." }, "id": { @@ -107,11 +107,11 @@ "description": "The unique (for this Node) id of the input port," }, "shape": { - "type": "Union[Tuple[int, ...], NoneType]", + "type": "Optional", "description": "The shape of the input port. This uses the same syntax as numpy ndarray shapes\n(e.g., :code:`numpy.zeros(shape)` would produce an array with the correct shape" }, "type": { - "type": "Union[str, NoneType]", + "type": "Optional", "description": "The data type of the input received at a port." } } @@ -120,7 +120,7 @@ "definition": "A single value which is evaluated as a function of values on :class:`InputPort`(s) and other Functions", "allowed_parameters": { "metadata": { - "type": "Union[Any, NoneType]", + "type": "Optional", "description": "Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values." }, "id": { @@ -128,15 +128,15 @@ "description": "The unique (for this Node) id of the function, which will be used in other :class:`~Function`s and\nthe :class:`~OutputPort`s for its value" }, "function": { - "type": "Union[str, NoneType]", + "type": "Optional", "description": "Which of the in-build MDF functions (:code:`linear`, etc.). See supported functions:\nhttps://mdf.readthedocs.io/en/latest/api/MDF_function_specifications.html" }, "args": { - "type": "Union[Any, NoneType]", + "type": "Optional", "description": "Dictionary of values for each of the arguments for the Function, e.g. if the in-built function\nis linear(slope),the args here could be {\"slope\":3} or {\"slope\":\"input_port_0 + 2\"}" }, "value": { - "type": "Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType]", + "type": "Union", "description": "If the function is a value expression, this attribute will contain the expression and the function\nand args attributes will be None." } } @@ -145,7 +145,7 @@ "definition": "A parameter of the :class:`Node`, which can be: 1) a specific fixed :code:`value` (a constant (int/float) or an array) 2) a string expression for the :code:`value` referencing other named :class:`Parameter`(s). which may be stateful (i.e. can change value over multiple executions of the :class:`Node`); 3) be evaluated by an\ninbuilt :code:`function` with :code:`args`; 4) or change from a :code:`default_initial_value` with a :code:`time_derivative`.", "allowed_parameters": { "metadata": { - "type": "Union[Any, NoneType]", + "type": "Optional", "description": "Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values." }, "id": { @@ -153,29 +153,29 @@ "description": "" }, "value": { - "type": "Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType]", + "type": "Union", "description": "The next value of the parameter, in terms of the inputs, functions and PREVIOUS parameter values" }, "default_initial_value": { - "type": "Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType]", + "type": "Union", "description": "The initial value of the parameter, only used when parameter is stateful." }, "time_derivative": { - "type": "Union[str, NoneType]", + "type": "Optional", "description": "How the parameter changes with time, i.e. ds/dt. Units of time are seconds." }, "function": { - "type": "Union[str, NoneType]", + "type": "Optional", "description": "Which of the in-build MDF functions (linear etc.) this uses, See\nhttps://mdf.readthedocs.io/en/latest/api/MDF_function_specifications.html" }, "args": { - "type": "Union[Any, NoneType]", + "type": "Optional", "description": "Dictionary of values for each of the arguments for the function of the parameter,\ne.g. if the in-build function is :code:`linear(slope)`, the args here could be :code:`{\"slope\": 3}` or\n:code:`{\"slope\": \"input_port_0 + 2\"}`" } }, "allowed_children": { "conditions": { - "type": "ParameterCondition", + "type": "List", "description": "Parameter specific conditions" } } @@ -188,11 +188,11 @@ "description": "A unique identifier for the ParameterCondition" }, "test": { - "type": "Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType]", + "type": "Union", "description": "The boolean expression to evaluate" }, "value": { - "type": "Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType]", + "type": "Union", "description": "The new value of the Parameter if the test is true" } } @@ -201,7 +201,7 @@ "definition": "The :class:`OutputPort` is an attribute of a :class:`Node` which exports information to another :class:`Node` connected by an :class:`Edge`", "allowed_parameters": { "metadata": { - "type": "Union[Any, NoneType]", + "type": "Optional", "description": "Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values." }, "id": { @@ -209,15 +209,15 @@ "description": "Unique identifier for the output port." }, "value": { - "type": "Union[str, NoneType]", + "type": "Optional", "description": "The value of the :class:`OutputPort` in terms of the :class:`InputPort`, :class:`Function` values, and\n:class:`Parameter` values." }, "shape": { - "type": "Union[Tuple[int, ...], NoneType]", + "type": "Optional", "description": "The shape of the output port. This uses the same syntax as numpy ndarray shapes\n(e.g., :code:`numpy.zeros(shape)` would produce an array with the correct shape" }, "type": { - "type": "Union[str, NoneType]", + "type": "Optional", "description": "The data type of the output sent by a port." } } @@ -226,7 +226,7 @@ "definition": "An :class:`Edge` is an attribute of a :class:`Graph` that transmits computational results from a sender's :class:`OutputPort` to a receiver's :class:`InputPort`.", "allowed_parameters": { "metadata": { - "type": "Union[Any, NoneType]", + "type": "Optional", "description": "Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values." }, "id": { @@ -250,7 +250,7 @@ "description": "The id of the InputPort on the receiver :class:`~Node`" }, "parameters": { - "type": "Union[Any, NoneType]", + "type": "Optional", "description": "Dictionary of parameters for the edge." } } diff --git a/docs/MDF_specification.yaml b/docs/MDF_specification.yaml index aa520e8d..f6ff63ab 100644 --- a/docs/MDF_specification.yaml +++ b/docs/MDF_specification.yaml @@ -7,7 +7,7 @@ specification: :class:`Graph` objects and model attribute(s) allowed_parameters: metadata: - type: Union[Any, NoneType] + type: Optional description: Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. id: @@ -21,36 +21,36 @@ specification: description: Information on what application generated/saved this file onnx_opset_version: - type: Union[str, NoneType] + type: Optional description: The ONNX opset used for any ONNX functions in this model. allowed_children: graphs: - type: Graph + type: List description: The collection of graphs that make up the MDF model. Graph: definition: A directed graph consisting of :class:`~Node`s (with :class:`~Parameter`s and :class:`~Function`s evaluated internally) connected via :class:`~Edge`s. allowed_parameters: metadata: - type: Union[Any, NoneType] + type: Optional description: Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. id: type: str description: A unique identifier for this Graph parameters: - type: Union[Any, NoneType] + type: Optional description: Dictionary of global parameters for the Graph conditions: - type: Union[ConditionSet, NoneType] + type: Optional description: The ConditionSet stored as dictionary for scheduling of the Graph allowed_children: nodes: - type: Node + type: List description: One or more :class:`Node`(s) present in the graph edges: - type: Edge + type: List description: Zero or more :class:`Edge`(s) present in the graph Node: definition: 'A self contained unit of evaluation receiving input from other @@ -60,7 +60,7 @@ specification: are calculated on the :class:`OutputPort`(s)' allowed_parameters: metadata: - type: Union[Any, NoneType] + type: Optional description: Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. id: @@ -68,16 +68,16 @@ specification: description: A unique identifier for the node. allowed_children: input_ports: - type: InputPort + type: List description: Dictionary of the :class:`InputPort` objects in the Node functions: - type: Function + type: List description: The :class:`Function`(s) for computation the node parameters: - type: Parameter + type: List description: Dictionary of :class:`Parameter`(s) for the node output_ports: - type: OutputPort + type: List description: The :class:`OutputPort`(s) containing evaluated quantities from the node InputPort: @@ -85,28 +85,28 @@ specification: external information to be input to the Node allowed_parameters: metadata: - type: Union[Any, NoneType] + type: Optional description: Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. id: type: str description: The unique (for this Node) id of the input port, shape: - type: Union[Tuple[int, ...], NoneType] + type: Optional description: 'The shape of the input port. This uses the same syntax as numpy ndarray shapes (e.g., :code:`numpy.zeros(shape)` would produce an array with the correct shape' type: - type: Union[str, NoneType] + type: Optional description: The data type of the input received at a port. Function: definition: A single value which is evaluated as a function of values on :class:`InputPort`(s) and other Functions allowed_parameters: metadata: - type: Union[Any, NoneType] + type: Optional description: Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. id: @@ -116,21 +116,20 @@ specification: the :class:`~OutputPort`s for its value' function: - type: Union[str, NoneType] + type: Optional description: 'Which of the in-build MDF functions (:code:`linear`, etc.). See supported functions: https://mdf.readthedocs.io/en/latest/api/MDF_function_specifications.html' args: - type: Union[Any, NoneType] + type: Optional description: 'Dictionary of values for each of the arguments for the Function, e.g. if the in-built function is linear(slope),the args here could be {"slope":3} or {"slope":"input_port_0 + 2"}' value: - type: Union[EvaluableExpression, List, Dict, ndarray, int, float, - str, NoneType] + type: Union description: 'If the function is a value expression, this attribute will contain the expression and the function @@ -146,34 +145,32 @@ specification: with a :code:`time_derivative`.' allowed_parameters: metadata: - type: Union[Any, NoneType] + type: Optional description: Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. id: type: str description: '' value: - type: Union[EvaluableExpression, List, Dict, ndarray, int, float, - str, NoneType] + type: Union description: The next value of the parameter, in terms of the inputs, functions and PREVIOUS parameter values default_initial_value: - type: Union[EvaluableExpression, List, Dict, ndarray, int, float, - str, NoneType] + type: Union description: The initial value of the parameter, only used when parameter is stateful. time_derivative: - type: Union[str, NoneType] + type: Optional description: How the parameter changes with time, i.e. ds/dt. Units of time are seconds. function: - type: Union[str, NoneType] + type: Optional description: 'Which of the in-build MDF functions (linear etc.) this uses, See https://mdf.readthedocs.io/en/latest/api/MDF_function_specifications.html' args: - type: Union[Any, NoneType] + type: Optional description: 'Dictionary of values for each of the arguments for the function of the parameter, @@ -183,7 +180,7 @@ specification: :code:`{"slope": "input_port_0 + 2"}`' allowed_children: conditions: - type: ParameterCondition + type: List description: Parameter specific conditions ParameterCondition: definition: A condition to test on a Node's parameters, which if true, sets @@ -193,39 +190,37 @@ specification: type: str description: A unique identifier for the ParameterCondition test: - type: Union[EvaluableExpression, List, Dict, ndarray, int, float, - str, NoneType] + type: Union description: The boolean expression to evaluate value: - type: Union[EvaluableExpression, List, Dict, ndarray, int, float, - str, NoneType] + type: Union description: The new value of the Parameter if the test is true OutputPort: definition: The :class:`OutputPort` is an attribute of a :class:`Node` which exports information to another :class:`Node` connected by an :class:`Edge` allowed_parameters: metadata: - type: Union[Any, NoneType] + type: Optional description: Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. id: type: str description: Unique identifier for the output port. value: - type: Union[str, NoneType] + type: Optional description: 'The value of the :class:`OutputPort` in terms of the :class:`InputPort`, :class:`Function` values, and :class:`Parameter` values.' shape: - type: Union[Tuple[int, ...], NoneType] + type: Optional description: 'The shape of the output port. This uses the same syntax as numpy ndarray shapes (e.g., :code:`numpy.zeros(shape)` would produce an array with the correct shape' type: - type: Union[str, NoneType] + type: Optional description: The data type of the output sent by a port. Edge: definition: An :class:`Edge` is an attribute of a :class:`Graph` that transmits @@ -233,7 +228,7 @@ specification: :class:`InputPort`. allowed_parameters: metadata: - type: Union[Any, NoneType] + type: Optional description: Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. id: @@ -257,5 +252,5 @@ specification: type: str description: The id of the InputPort on the receiver :class:`~Node` parameters: - type: Union[Any, NoneType] + type: Optional description: Dictionary of parameters for the edge. diff --git a/docs/README.md b/docs/README.md index 0db2109c..e0e2b208 100644 --- a/docs/README.md +++ b/docs/README.md @@ -7,7 +7,7 @@ The top level construct in MDF is Model, which may contain multiple onnx_opset_version - Union[str, NoneType] + Optional The ONNX opset used for any ONNX functions in this model. @@ -46,7 +46,7 @@ The top level construct in MDF is Model, which may contain multiple Graph + List The collection of graphs that make up the MDF model. @@ -60,7 +60,7 @@ A directed graph consisting of Nodes (with Nodes (with Nodes (with Node + List One or more Node(s) present in the graph edges - Edge + List Zero or more Edge(s) present in the graph @@ -114,7 +114,7 @@ are calculated on the OutputPort(s) - + @@ -132,28 +132,28 @@ are calculated on the OutputPort(s)
metadataUnion[Any, NoneType]Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values.
- + - + - + - + @@ -167,7 +167,7 @@ The InputPort is an attribute of a Node which allows ex
input_portsInputPortList Dictionary of the InputPort objects in the Node
functionsFunctionList The Function(s) for computation the node
parametersParameterList Dictionary of Parameter(s) for the node
output_portsOutputPortList The OutputPort(s) containing evaluated quantities from the node
- + @@ -181,7 +181,7 @@ The InputPort is an attribute of a Node which allows ex - + @@ -189,7 +189,7 @@ The InputPort is an attribute of a Node which allows ex - + @@ -203,7 +203,7 @@ A single value which is evaluated as a function of values on OutputPorts for its value - + @@ -226,7 +226,7 @@ https://mdf.readthedocs.io/en/latest/api/MDFfunctionspec - + @@ -234,7 +234,7 @@ is linear(slope),the args here could be {"slope":3} or {"slope":"input_port_0 + - + @@ -250,7 +250,7 @@ inbuilt function with args; 4) or change from a default_initial
metadataUnion[Any, NoneType]Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values.
shapeUnion[Tuple[int, ...], NoneType]Optional The shape of the input port. This uses the same syntax as numpy ndarray shapes (e.g., numpy.zeros(shape) would produce an array with the correct shape
typeUnion[str, NoneType]Optional The data type of the input received at a port.
functionUnion[str, NoneType]Optional Which of the in-build MDF functions (linear, etc.). See supported functions: https://mdf.readthedocs.io/en/latest/api/MDFfunctionspecifications.html
argsUnion[Any, NoneType]Optional Dictionary of values for each of the arguments for the Function, e.g. if the in-built function is linear(slope),the args here could be {"slope":3} or {"slope":"input_port_0 + 2"}
valueUnion[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType]Union If the function is a value expression, this attribute will contain the expression and the function and args attributes will be None.
- + @@ -264,28 +264,28 @@ inbuilt function with args; 4) or change from a default_initial - + - + - + - + @@ -293,7 +293,7 @@ https://mdf.readthedocs.io/en/latest/api/MDFfunctionspec - + @@ -306,7 +306,7 @@ e.g. if the in-build function is linear(slope), the args here could be - + @@ -327,14 +327,14 @@ A condition to test on a Node's parameters, which if true, sets the value of thi - + - + @@ -348,7 +348,7 @@ The OutputPort is an attribute of a No
metadataUnion[Any, NoneType]Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values.
valueUnion[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType]Union The next value of the parameter, in terms of the inputs, functions and PREVIOUS parameter values
default_initial_valueUnion[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType]Union The initial value of the parameter, only used when parameter is stateful.
time_derivativeUnion[str, NoneType]Optional How the parameter changes with time, i.e. ds/dt. Units of time are seconds.
functionUnion[str, NoneType]Optional Which of the in-build MDF functions (linear etc.) this uses, See https://mdf.readthedocs.io/en/latest/api/MDFfunctionspecifications.html
argsUnion[Any, NoneType]Optional Dictionary of values for each of the arguments for the function of the parameter, e.g. if the in-build function is linear(slope), the args here could be {"slope": 3} or {"slope": "input_port_0 + 2"}
conditionsParameterConditionList Parameter specific conditions
testUnion[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType]Union The boolean expression to evaluate
valueUnion[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType]Union The new value of the Parameter if the test is true
- + @@ -362,7 +362,7 @@ The OutputPort is an attribute of a No - + @@ -370,7 +370,7 @@ The OutputPort is an attribute of a No - + @@ -378,7 +378,7 @@ The OutputPort is an attribute of a No - + @@ -392,7 +392,7 @@ An Edge is an attribute of a Graph that
metadataUnion[Any, NoneType]Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values.
valueUnion[str, NoneType]Optional The value of the OutputPort in terms of the InputPort, Function values, and Parameter values.
shapeUnion[Tuple[int, ...], NoneType]Optional The shape of the output port. This uses the same syntax as numpy ndarray shapes (e.g., numpy.zeros(shape) would produce an array with the correct shape
typeUnion[str, NoneType]Optional The data type of the output sent by a port.
- + @@ -435,7 +435,7 @@ An Edge is an attribute of a Graph that - + @@ -449,7 +449,7 @@ A set of descriptors which specifies conditional execution of Nodes to meet comp
metadataUnion[Any, NoneType]Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values.
parametersUnion[Any, NoneType]Optional Dictionary of parameters for the edge.
- + @@ -463,7 +463,7 @@ A set of descriptors which specifies conditional execution of Nodes to meet comp - + @@ -477,21 +477,21 @@ Specifies the non-default pattern of execution of Nodes
metadataUnion[Any, NoneType]Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values.
kwargsUnion[Any, NoneType]Optional The dictionary of keyword arguments needed to evaluate the Condition
- + - + - + diff --git a/docs/sphinx/requirements.txt b/docs/sphinx/requirements.txt index a5923835..759ed661 100644 --- a/docs/sphinx/requirements.txt +++ b/docs/sphinx/requirements.txt @@ -1,2 +1,2 @@ # Install all the dependencies --e .[optional] +--group=docs diff --git a/docs/sphinx/source/api/Introduction.md b/docs/sphinx/source/api/Introduction.md index d5381e69..f9192355 100644 --- a/docs/sphinx/source/api/Introduction.md +++ b/docs/sphinx/source/api/Introduction.md @@ -13,6 +13,13 @@ **Note: MDF is still in development! See the [open issues related to the specification](https://github.com/ModECI/MDF/issues?q=is%3Aissue+is%3Aopen+label%3Aspecification) or go [here](http://modeci.org/#contactPage) to get in contact regarding MDF.** *The MDF format was first proposed following a meeting organised at Princeton in July 2019 by Russ Poldrack of the Center for Reproducible Neuroscience (CRN) at Stanford and the [Brain Imaging Data Standard (BIDS)](https://bids.neuroimaging.io/) initiative. For more on the previous work in this area, see [here](https://github.com/OpenSourceBrain/PsyNeuLinkShowcase/tree/master/BIDS-MDF).* +## Paper introducing MDF + +The background to the ModECI project, the motivation for developing the Model Description Format, and the initial Python implementation of the language have been described in a NeuroView article in the Neuron journal: + +*Integrating model development across computational neuroscience, cognitive science, and machine learning* +Padraig Gleeson, Sharon Crook, David Turner, Katherine Mantel, Mayank Raunak, Ted Willke and Jonathan D. Cohen, April 25, 2023 DOI: [https://doi.org/10.1016/j.neuron.2023.03.037](https://doi.org/10.1016/j.neuron.2023.03.037) + ## Overview diff --git a/docs/sphinx/source/api/MDF_function_specifications.md b/docs/sphinx/source/api/MDF_function_specifications.md index e8d6d894..32564fa1 100644 --- a/docs/sphinx/source/api/MDF_function_specifications.md +++ b/docs/sphinx/source/api/MDF_function_specifications.md @@ -1685,7 +1685,7 @@ Python version: `onnx_ops.lppool(X, auto_pad, kernel_shape, p, pads, strides)` ## MatMul

-Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html +Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).

Python version: `onnx_ops.matmul(A, B)` @@ -1695,7 +1695,7 @@ Python version: `onnx_ops.matmul(A, B)` ## MatMulInteger

-Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. +Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.

@@ -1732,7 +1732,7 @@ Python version: `onnx_ops.max(data_0)` ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` - if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. Sliding windows that would start in the right padded region are ignored. + if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled: ``` @@ -2237,7 +2237,7 @@ Python version: `onnx_ops.qlinearconv(x, x_scale, x_zero_point, w, w_scale, w_ze ## QLinearMatMul

-Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. +Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). It consumes two quantized input tensors, their scales and zero points, scale and zero point of output, and computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point). For (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. diff --git a/docs/sphinx/source/api/Specification.rst b/docs/sphinx/source/api/Specification.rst index 18761b00..886b17b0 100644 --- a/docs/sphinx/source/api/Specification.rst +++ b/docs/sphinx/source/api/Specification.rst @@ -11,23 +11,23 @@ The top level construct in MDF is Model, which may contain multiple `Graph <#gra **Allowed parameters** -========================== ==================== ============================================================================================= -Allowed field Data Type Description -========================== ==================== ============================================================================================= -**metadata** Union[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. -**id** str A unique identifier for this Model -**format** str Information on the version of MDF used in this file -**generating_application** str Information on what application generated/saved this file -**onnx_opset_version** Union[str, NoneType] The ONNX opset used for any ONNX functions in this model. -========================== ==================== ============================================================================================= +========================== =========== ============================================================================================= +Allowed field Data Type Description +========================== =========== ============================================================================================= +**metadata** Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. +**id** str A unique identifier for this Model +**format** str Information on the version of MDF used in this file +**generating_application** str Information on what application generated/saved this file +**onnx_opset_version** Optional The ONNX opset used for any ONNX functions in this model. +========================== =========== ============================================================================================= **Allowed children** -=============== ================== ==================================================== -Allowed child Data Type Description -=============== ================== ==================================================== -**graphs** `Graph <#graph>`__ The collection of graphs that make up the MDF model. -=============== ================== ==================================================== +=============== ================ ==================================================== +Allowed child Data Type Description +=============== ================ ==================================================== +**graphs** `List <#list>`__ The collection of graphs that make up the MDF model. +=============== ================ ==================================================== ===== Graph @@ -36,22 +36,22 @@ A directed graph consisting of `Nodes <#node>`__ (with `Parameters <#parameter>` **Allowed parameters** -=============== ============================= ============================================================================================= -Allowed field Data Type Description -=============== ============================= ============================================================================================= -**metadata** Union[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. -**id** str A unique identifier for this Graph -**parameters** Union[Any, NoneType] Dictionary of global parameters for the Graph -**conditions** Union[ConditionSet, NoneType] The ConditionSet stored as dictionary for scheduling of the Graph -=============== ============================= ============================================================================================= +=============== =========== ============================================================================================= +Allowed field Data Type Description +=============== =========== ============================================================================================= +**metadata** Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. +**id** str A unique identifier for this Graph +**parameters** Optional Dictionary of global parameters for the Graph +**conditions** Optional The ConditionSet stored as dictionary for scheduling of the Graph +=============== =========== ============================================================================================= **Allowed children** =============== ================ ===================================================== Allowed child Data Type Description =============== ================ ===================================================== -**nodes** `Node <#node>`__ One or more `Node(s) <#node>`__ present in the graph -**edges** `Edge <#edge>`__ Zero or more `Edge(s) <#edge>`__ present in the graph +**nodes** `List <#list>`__ One or more `Node(s) <#node>`__ present in the graph +**edges** `List <#list>`__ Zero or more `Edge(s) <#edge>`__ present in the graph =============== ================ ===================================================== ==== @@ -62,23 +62,23 @@ are calculated on the `OutputPort(s) <#outputport>`__ **Allowed parameters** -=============== ==================== ============================================================================================= -Allowed field Data Type Description -=============== ==================== ============================================================================================= -**metadata** Union[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. -**id** str A unique identifier for the node. -=============== ==================== ============================================================================================= +=============== =========== ============================================================================================= +Allowed field Data Type Description +=============== =========== ============================================================================================= +**metadata** Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. +**id** str A unique identifier for the node. +=============== =========== ============================================================================================= **Allowed children** -================ ============================ ================================================================================= -Allowed child Data Type Description -================ ============================ ================================================================================= -**input_ports** `InputPort <#inputport>`__ Dictionary of the `InputPort <#inputport>`__ objects in the Node -**functions** `Function <#function>`__ The `Function(s) <#function>`__ for computation the node -**parameters** `Parameter <#parameter>`__ Dictionary of `Parameter(s) <#parameter>`__ for the node -**output_ports** `OutputPort <#outputport>`__ The `OutputPort(s) <#outputport>`__ containing evaluated quantities from the node -================ ============================ ================================================================================= +================ ================ ================================================================================= +Allowed child Data Type Description +================ ================ ================================================================================= +**input_ports** `List <#list>`__ Dictionary of the `InputPort <#inputport>`__ objects in the Node +**functions** `List <#list>`__ The `Function(s) <#function>`__ for computation the node +**parameters** `List <#list>`__ Dictionary of `Parameter(s) <#parameter>`__ for the node +**output_ports** `List <#list>`__ The `OutputPort(s) <#outputport>`__ containing evaluated quantities from the node +================ ================ ================================================================================= ========= InputPort @@ -87,15 +87,15 @@ The `InputPort <#inputport>`__ is an attribute of a Node which allows external i **Allowed parameters** -=============== ================================ ============================================================================================= -Allowed field Data Type Description -=============== ================================ ============================================================================================= -**metadata** Union[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. -**id** str The unique (for this Node) id of the input port, -**shape** Union[Tuple[int, ...], NoneType] The shape of the input port. This uses the same syntax as numpy ndarray shapes - (e.g., **numpy.zeros(shape)** would produce an array with the correct shape -**type** Union[str, NoneType] The data type of the input received at a port. -=============== ================================ ============================================================================================= +=============== =========== ============================================================================================= +Allowed field Data Type Description +=============== =========== ============================================================================================= +**metadata** Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. +**id** str The unique (for this Node) id of the input port, +**shape** Optional The shape of the input port. This uses the same syntax as numpy ndarray shapes + (e.g., **numpy.zeros(shape)** would produce an array with the correct shape +**type** Optional The data type of the input received at a port. +=============== =========== ============================================================================================= ======== Function @@ -104,19 +104,19 @@ A single value which is evaluated as a function of values on `InputPort(s) <#inp **Allowed parameters** -=============== ========================================================================== ======================================================================================================== -Allowed field Data Type Description -=============== ========================================================================== ======================================================================================================== -**metadata** Union[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. -**id** str The unique (for this Node) id of the function, which will be used in other `Functions <#function>`__ and - the `OutputPorts <#outputport>`__ for its value -**function** Union[str, NoneType] Which of the in-build MDF functions (**linear**, etc.). See supported functions: - https://mdf.readthedocs.io/en/latest/api/MDF_function_specifications.html -**args** Union[Any, NoneType] Dictionary of values for each of the arguments for the Function, e.g. if the in-built function - is linear(slope),the args here could be {"slope":3} or {"slope":"input_port_0 + 2"} -**value** Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType] If the function is a value expression, this attribute will contain the expression and the function - and args attributes will be None. -=============== ========================================================================== ======================================================================================================== +=============== =========== ======================================================================================================== +Allowed field Data Type Description +=============== =========== ======================================================================================================== +**metadata** Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. +**id** str The unique (for this Node) id of the function, which will be used in other `Functions <#function>`__ and + the `OutputPorts <#outputport>`__ for its value +**function** Optional Which of the in-build MDF functions (**linear**, etc.). See supported functions: + https://mdf.readthedocs.io/en/latest/api/MDF_function_specifications.html +**args** Optional Dictionary of values for each of the arguments for the Function, e.g. if the in-built function + is linear(slope),the args here could be {"slope":3} or {"slope":"input_port_0 + 2"} +**value** Union If the function is a value expression, this attribute will contain the expression and the function + and args attributes will be None. +=============== =========== ======================================================================================================== ========= Parameter @@ -126,28 +126,28 @@ inbuilt **function** with **args**; 4) or change from a **default_initial_value* **Allowed parameters** -========================= ========================================================================== ================================================================================================ -Allowed field Data Type Description -========================= ========================================================================== ================================================================================================ -**metadata** Union[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. +========================= =========== ================================================================================================ +Allowed field Data Type Description +========================= =========== ================================================================================================ +**metadata** Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. **id** str -**value** Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType] The next value of the parameter, in terms of the inputs, functions and PREVIOUS parameter values -**default_initial_value** Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType] The initial value of the parameter, only used when parameter is stateful. -**time_derivative** Union[str, NoneType] How the parameter changes with time, i.e. ds/dt. Units of time are seconds. -**function** Union[str, NoneType] Which of the in-build MDF functions (linear etc.) this uses, See - https://mdf.readthedocs.io/en/latest/api/MDF_function_specifications.html -**args** Union[Any, NoneType] Dictionary of values for each of the arguments for the function of the parameter, - e.g. if the in-build function is **linear(slope)**, the args here could be **{"slope": 3}** or - **{"slope": "input_port_0 + 2"}** -========================= ========================================================================== ================================================================================================ +**value** Union The next value of the parameter, in terms of the inputs, functions and PREVIOUS parameter values +**default_initial_value** Union The initial value of the parameter, only used when parameter is stateful. +**time_derivative** Optional How the parameter changes with time, i.e. ds/dt. Units of time are seconds. +**function** Optional Which of the in-build MDF functions (linear etc.) this uses, See + https://mdf.readthedocs.io/en/latest/api/MDF_function_specifications.html +**args** Optional Dictionary of values for each of the arguments for the function of the parameter, + e.g. if the in-build function is **linear(slope)**, the args here could be **{"slope": 3}** or + **{"slope": "input_port_0 + 2"}** +========================= =========== ================================================================================================ **Allowed children** -=============== ============================================ ============================= -Allowed child Data Type Description -=============== ============================================ ============================= -**conditions** `ParameterCondition <#parametercondition>`__ Parameter specific conditions -=============== ============================================ ============================= +=============== ================ ============================= +Allowed child Data Type Description +=============== ================ ============================= +**conditions** `List <#list>`__ Parameter specific conditions +=============== ================ ============================= ================== ParameterCondition @@ -156,13 +156,13 @@ A condition to test on a Node's parameters, which if true, sets the value of thi **Allowed parameters** -=============== ========================================================================== ================================================== -Allowed field Data Type Description -=============== ========================================================================== ================================================== -**id** str A unique identifier for the ParameterCondition -**test** Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType] The boolean expression to evaluate -**value** Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType] The new value of the Parameter if the test is true -=============== ========================================================================== ================================================== +=============== =========== ================================================== +Allowed field Data Type Description +=============== =========== ================================================== +**id** str A unique identifier for the ParameterCondition +**test** Union The boolean expression to evaluate +**value** Union The new value of the Parameter if the test is true +=============== =========== ================================================== ========== OutputPort @@ -171,17 +171,17 @@ The `OutputPort <#outputport>`__ is an attribute of a `Node <#node>`__ which exp **Allowed parameters** -=============== ================================ ============================================================================================================================== -Allowed field Data Type Description -=============== ================================ ============================================================================================================================== -**metadata** Union[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. -**id** str Unique identifier for the output port. -**value** Union[str, NoneType] The value of the `OutputPort <#outputport>`__ in terms of the `InputPort <#inputport>`__, `Function <#function>`__ values, and - `Parameter <#parameter>`__ values. -**shape** Union[Tuple[int, ...], NoneType] The shape of the output port. This uses the same syntax as numpy ndarray shapes - (e.g., **numpy.zeros(shape)** would produce an array with the correct shape -**type** Union[str, NoneType] The data type of the output sent by a port. -=============== ================================ ============================================================================================================================== +=============== =========== ============================================================================================================================== +Allowed field Data Type Description +=============== =========== ============================================================================================================================== +**metadata** Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. +**id** str Unique identifier for the output port. +**value** Optional The value of the `OutputPort <#outputport>`__ in terms of the `InputPort <#inputport>`__, `Function <#function>`__ values, and + `Parameter <#parameter>`__ values. +**shape** Optional The shape of the output port. This uses the same syntax as numpy ndarray shapes + (e.g., **numpy.zeros(shape)** would produce an array with the correct shape +**type** Optional The data type of the output sent by a port. +=============== =========== ============================================================================================================================== ==== Edge @@ -190,18 +190,18 @@ An `Edge <#edge>`__ is an attribute of a `Graph <#graph>`__ that transmits compu **Allowed parameters** -================= ==================== ============================================================================================================ -Allowed field Data Type Description -================= ==================== ============================================================================================================ -**metadata** Union[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. -**id** str A unique string identifier for this edge. -**sender** str The **id** of the `Node <#node>`__ which is the source of the edge. -**receiver** str The **id** of the `Node <#node>`__ which is the target of the edge. -**sender_port** str The id of the `OutputPort <#outputport>`__ on the sender `Node <#node>`__, whose value should be sent to the - **receiver_port** -**receiver_port** str The id of the InputPort on the receiver `Node <#node>`__ -**parameters** Union[Any, NoneType] Dictionary of parameters for the edge. -================= ==================== ============================================================================================================ +================= =========== ============================================================================================================ +Allowed field Data Type Description +================= =========== ============================================================================================================ +**metadata** Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. +**id** str A unique string identifier for this edge. +**sender** str The **id** of the `Node <#node>`__ which is the source of the edge. +**receiver** str The **id** of the `Node <#node>`__ which is the target of the edge. +**sender_port** str The id of the `OutputPort <#outputport>`__ on the sender `Node <#node>`__, whose value should be sent to the + **receiver_port** +**receiver_port** str The id of the InputPort on the receiver `Node <#node>`__ +**parameters** Optional Dictionary of parameters for the edge. +================= =========== ============================================================================================================ ========= Condition @@ -210,13 +210,13 @@ A set of descriptors which specifies conditional execution of Nodes to meet comp **Allowed parameters** -=============== ==================== ============================================================================================= -Allowed field Data Type Description -=============== ==================== ============================================================================================= -**metadata** Union[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. -**type** str The type of `Condition <#condition>`__ from the library -**kwargs** Union[Any, NoneType] The dictionary of keyword arguments needed to evaluate the `Condition <#condition>`__ -=============== ==================== ============================================================================================= +=============== =========== ============================================================================================= +Allowed field Data Type Description +=============== =========== ============================================================================================= +**metadata** Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. +**type** str The type of `Condition <#condition>`__ from the library +**kwargs** Optional The dictionary of keyword arguments needed to evaluate the `Condition <#condition>`__ +=============== =========== ============================================================================================= ============ ConditionSet @@ -225,10 +225,10 @@ Specifies the non-default pattern of execution of Nodes **Allowed parameters** -================= ========================== ============================================================================================= -Allowed field Data Type Description -================= ========================== ============================================================================================= -**metadata** Union[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. -**node_specific** Union[Condition, NoneType] A dictionary mapping nodes to any non-default run conditions -**termination** Union[Condition, NoneType] A dictionary mapping time scales of model execution to conditions indicating when they end -================= ========================== ============================================================================================= +================= =========== ============================================================================================= +Allowed field Data Type Description +================= =========== ============================================================================================= +**metadata** Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. +**node_specific** Optional A dictionary mapping nodes to any non-default run conditions +**termination** Optional A dictionary mapping time scales of model execution to conditions indicating when they end 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a/docs/sphinx/source/api/export_format/PyTorch/simple_pytorch_to_mdf.png and b/docs/sphinx/source/api/export_format/PyTorch/simple_pytorch_to_mdf.png differ diff --git a/examples/ACT-R/addition.json b/examples/ACT-R/addition.json index 8764998b..87b31836 100644 --- a/examples/ACT-R/addition.json +++ b/examples/ACT-R/addition.json @@ -1,7 +1,7 @@ { "addition": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "addition_graph": { "nodes": { diff --git a/examples/ACT-R/addition.yaml b/examples/ACT-R/addition.yaml index 7c1d1027..d3edb9ac 100644 --- a/examples/ACT-R/addition.yaml +++ b/examples/ACT-R/addition.yaml @@ -1,6 +1,6 @@ addition: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 graphs: addition_graph: nodes: diff --git a/examples/ACT-R/count.json b/examples/ACT-R/count.json index 5f99157e..f344afdf 100644 --- a/examples/ACT-R/count.json +++ b/examples/ACT-R/count.json @@ -1,7 +1,7 @@ { "count": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "count_graph": { "nodes": { diff --git a/examples/ACT-R/count.py b/examples/ACT-R/count.py index 50d05664..be6df249 100644 --- a/examples/ACT-R/count.py +++ b/examples/ACT-R/count.py @@ -1,4 +1,5 @@ """Create the MDF files for the count example and run using the scheduler.""" + import os from modeci_mdf.interfaces.actr import actr_to_mdf from modeci_mdf.execution_engine import EvaluableGraph diff --git a/examples/ACT-R/count.yaml b/examples/ACT-R/count.yaml index ca9d643e..2dfc1c17 100644 --- a/examples/ACT-R/count.yaml +++ b/examples/ACT-R/count.yaml @@ -1,6 +1,6 @@ count: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 graphs: count_graph: nodes: diff --git a/examples/MDF/ABCD.json b/examples/MDF/ABCD.json index fb926c29..9088b9cd 100644 --- a/examples/MDF/ABCD.json +++ b/examples/MDF/ABCD.json @@ -1,7 +1,7 @@ { "ABCD": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "abcd_example": { "nodes": { diff --git a/examples/MDF/ABCD.yaml b/examples/MDF/ABCD.yaml index 60505ced..751db848 100644 --- a/examples/MDF/ABCD.yaml +++ b/examples/MDF/ABCD.yaml @@ -1,6 +1,6 @@ ABCD: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 graphs: abcd_example: nodes: diff --git a/examples/MDF/Arrays.json b/examples/MDF/Arrays.json index 4689a23a..59550427 100644 --- a/examples/MDF/Arrays.json +++ b/examples/MDF/Arrays.json @@ -1,7 +1,7 @@ { "Arrays": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "array_example": { "nodes": { diff --git a/examples/MDF/Arrays.yaml b/examples/MDF/Arrays.yaml index 2cafe693..1baf853f 100644 --- a/examples/MDF/Arrays.yaml +++ b/examples/MDF/Arrays.yaml @@ -1,6 +1,6 @@ Arrays: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 graphs: array_example: nodes: diff --git a/examples/MDF/NewtonCoolingModel.json b/examples/MDF/NewtonCoolingModel.json index 1ace6d3f..f90876f0 100644 --- a/examples/MDF/NewtonCoolingModel.json +++ b/examples/MDF/NewtonCoolingModel.json @@ -1,7 +1,7 @@ { "NewtonCoolingModel": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "cooling_process": { "nodes": { diff --git a/examples/MDF/NewtonCoolingModel.yaml b/examples/MDF/NewtonCoolingModel.yaml index d11ded13..fac3caf1 100644 --- a/examples/MDF/NewtonCoolingModel.yaml +++ b/examples/MDF/NewtonCoolingModel.yaml @@ -1,6 +1,6 @@ NewtonCoolingModel: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 graphs: cooling_process: nodes: diff --git a/examples/MDF/ParametersFunctions.json b/examples/MDF/ParametersFunctions.json index af187b72..95e010b4 100644 --- a/examples/MDF/ParametersFunctions.json +++ b/examples/MDF/ParametersFunctions.json @@ -1,7 +1,7 @@ { "ParametersFunctions": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "params_funcs_example": { "nodes": { diff --git a/examples/MDF/ParametersFunctions.yaml b/examples/MDF/ParametersFunctions.yaml index 6d97faa5..7460bb1f 100644 --- a/examples/MDF/ParametersFunctions.yaml +++ b/examples/MDF/ParametersFunctions.yaml @@ -1,6 +1,6 @@ ParametersFunctions: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 graphs: params_funcs_example: nodes: diff --git a/examples/MDF/RNN/IAF_net.json b/examples/MDF/RNN/IAF_net.json index cf60851e..5a93fedb 100644 --- a/examples/MDF/RNN/IAF_net.json +++ b/examples/MDF/RNN/IAF_net.json @@ -1,7 +1,7 @@ { "IAF_net": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "iaf_example": { "nodes": { diff --git a/examples/MDF/RNN/IAF_net.yaml b/examples/MDF/RNN/IAF_net.yaml index a6556eb4..4efe41fc 100644 --- a/examples/MDF/RNN/IAF_net.yaml +++ b/examples/MDF/RNN/IAF_net.yaml @@ -1,6 +1,6 @@ IAF_net: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 graphs: iaf_example: nodes: diff --git a/examples/MDF/RNN/IAF_net2.json b/examples/MDF/RNN/IAF_net2.json index 3e29332b..f702d3d6 100644 --- a/examples/MDF/RNN/IAF_net2.json +++ b/examples/MDF/RNN/IAF_net2.json @@ -1,7 +1,7 @@ { "IAF_net2": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "iaf_example": { "nodes": { diff --git a/examples/MDF/RNN/IAF_net2.yaml b/examples/MDF/RNN/IAF_net2.yaml index 3b170ea3..deca79c3 100644 --- a/examples/MDF/RNN/IAF_net2.yaml +++ b/examples/MDF/RNN/IAF_net2.yaml @@ -1,6 +1,6 @@ IAF_net2: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 graphs: iaf_example: nodes: diff --git a/examples/MDF/RNN/IAFs.json b/examples/MDF/RNN/IAFs.json index efcc2a85..5b359bdb 100644 --- a/examples/MDF/RNN/IAFs.json +++ b/examples/MDF/RNN/IAFs.json @@ -1,7 +1,7 @@ { "IAFs": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "iaf_example": { "nodes": { diff --git a/examples/MDF/RNN/IAFs.yaml b/examples/MDF/RNN/IAFs.yaml index 7c479cc8..0e5492da 100644 --- a/examples/MDF/RNN/IAFs.yaml +++ b/examples/MDF/RNN/IAFs.yaml @@ -1,6 +1,6 @@ IAFs: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 graphs: iaf_example: nodes: diff --git a/examples/MDF/RNN/IaF.net.run.png b/examples/MDF/RNN/IaF.net.run.png index 68c81d47..83f01547 100644 Binary files a/examples/MDF/RNN/IaF.net.run.png and b/examples/MDF/RNN/IaF.net.run.png differ diff --git a/examples/MDF/RNN/IaF.net2.run.png b/examples/MDF/RNN/IaF.net2.run.png index 7c462c98..9f805f66 100644 Binary files a/examples/MDF/RNN/IaF.net2.run.png and b/examples/MDF/RNN/IaF.net2.run.png differ diff --git a/examples/MDF/RNN/IaF.run.png b/examples/MDF/RNN/IaF.run.png index 35cd5b21..2fa20289 100644 Binary files a/examples/MDF/RNN/IaF.run.png and b/examples/MDF/RNN/IaF.run.png differ diff --git a/examples/MDF/RNN/RNN.run.png b/examples/MDF/RNN/RNN.run.png index f793e1da..038d00fa 100644 Binary files a/examples/MDF/RNN/RNN.run.png and b/examples/MDF/RNN/RNN.run.png differ diff --git a/examples/MDF/RNN/RNNs.json b/examples/MDF/RNN/RNNs.json index 67b6697b..f68d5b30 100644 --- a/examples/MDF/RNN/RNNs.json +++ b/examples/MDF/RNN/RNNs.json @@ -1,7 +1,7 @@ { "RNNs": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "rnn_example": { "nodes": { diff --git a/examples/MDF/RNN/RNNs.yaml b/examples/MDF/RNN/RNNs.yaml index 3589bf9a..e1a0bfbe 100644 --- a/examples/MDF/RNN/RNNs.yaml +++ b/examples/MDF/RNN/RNNs.yaml @@ -1,6 +1,6 @@ RNNs: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 graphs: rnn_example: nodes: diff --git a/examples/MDF/RNN/generate_iaf.py b/examples/MDF/RNN/generate_iaf.py index 42b4ec8f..a333dffa 100644 --- a/examples/MDF/RNN/generate_iaf.py +++ b/examples/MDF/RNN/generate_iaf.py @@ -1,5 +1,5 @@ """ - Example of ModECI MDF - Testing integrate and fire neurons +Example of ModECI MDF - Testing integrate and fire neurons """ from modeci_mdf.mdf import * diff --git a/examples/MDF/RNN/generate_rnn.py b/examples/MDF/RNN/generate_rnn.py index 2ce00a50..05b92cb3 100644 --- a/examples/MDF/RNN/generate_rnn.py +++ b/examples/MDF/RNN/generate_rnn.py @@ -1,5 +1,5 @@ """ - Example of ModECI MDF - Testing RNNs +Example of ModECI MDF - Testing RNNs """ from modeci_mdf.mdf import * diff --git a/examples/MDF/RNN/iaf.net.png b/examples/MDF/RNN/iaf.net.png index c5186e82..30db8f94 100644 Binary files a/examples/MDF/RNN/iaf.net.png and b/examples/MDF/RNN/iaf.net.png differ diff --git a/examples/MDF/RNN/iaf.net2.png b/examples/MDF/RNN/iaf.net2.png index 1eea542d..22784bc0 100644 Binary files a/examples/MDF/RNN/iaf.net2.png and b/examples/MDF/RNN/iaf.net2.png differ diff --git a/examples/MDF/RNN/iaf.png b/examples/MDF/RNN/iaf.png index f54e65c5..acd54abb 100644 Binary files a/examples/MDF/RNN/iaf.png and b/examples/MDF/RNN/iaf.png differ diff --git a/examples/MDF/RNN/rnn.png b/examples/MDF/RNN/rnn.png index 1e3f8cda..ea726f10 100644 Binary files a/examples/MDF/RNN/rnn.png and b/examples/MDF/RNN/rnn.png differ diff --git a/examples/MDF/Simple.bson b/examples/MDF/Simple.bson index 8295e272..1af6cdd2 100644 Binary files a/examples/MDF/Simple.bson and b/examples/MDF/Simple.bson differ diff --git a/examples/MDF/Simple.json b/examples/MDF/Simple.json index a5f6c9c2..a94c7355 100644 --- a/examples/MDF/Simple.json +++ b/examples/MDF/Simple.json @@ -1,7 +1,7 @@ { "Simple": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "simple_example": { "nodes": { diff --git a/examples/MDF/Simple.yaml b/examples/MDF/Simple.yaml index 29055bf8..8c43c59e 100644 --- a/examples/MDF/Simple.yaml +++ b/examples/MDF/Simple.yaml @@ -1,6 +1,6 @@ Simple: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 graphs: simple_example: nodes: diff --git a/examples/MDF/States.json b/examples/MDF/States.json index 4d044714..cf22cb88 100644 --- a/examples/MDF/States.json +++ b/examples/MDF/States.json @@ -1,7 +1,7 @@ { "States": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "state_example": { "nodes": { diff --git a/examples/MDF/States.yaml b/examples/MDF/States.yaml index 9fe767e7..45addf23 100644 --- a/examples/MDF/States.yaml +++ b/examples/MDF/States.yaml @@ -1,6 +1,6 @@ States: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 graphs: state_example: nodes: diff --git a/examples/MDF/SwitchedRLC_Circuit.json b/examples/MDF/SwitchedRLC_Circuit.json index 41f1d5bd..bc14167e 100644 --- a/examples/MDF/SwitchedRLC_Circuit.json +++ b/examples/MDF/SwitchedRLC_Circuit.json @@ -1,7 +1,7 @@ { "SwitchedRLC_Circuit": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "metadata": { "preferred_duration": 2, "preferred_dt": 0.001 diff --git a/examples/MDF/SwitchedRLC_Circuit.yaml b/examples/MDF/SwitchedRLC_Circuit.yaml index 0f1ae016..d049071e 100644 --- a/examples/MDF/SwitchedRLC_Circuit.yaml +++ b/examples/MDF/SwitchedRLC_Circuit.yaml @@ -1,6 +1,6 @@ SwitchedRLC_Circuit: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 metadata: preferred_duration: 2 preferred_dt: 0.001 diff --git a/examples/MDF/abc_conditions.json b/examples/MDF/abc_conditions.json index bf4d43c0..a4abbce2 100644 --- a/examples/MDF/abc_conditions.json +++ b/examples/MDF/abc_conditions.json @@ -1,7 +1,7 @@ { "abc_conditions": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "abc_conditions_example": { "nodes": { diff --git a/examples/MDF/abc_conditions.py b/examples/MDF/abc_conditions.py index 4d52ba21..dab998dc 100644 --- a/examples/MDF/abc_conditions.py +++ b/examples/MDF/abc_conditions.py @@ -1,5 +1,5 @@ """ - Example of ModECI MDF - A simple 3 node graph with scheduling conditions +Example of ModECI MDF - A simple 3 node graph with scheduling conditions """ import os diff --git a/examples/MDF/abc_conditions.yaml b/examples/MDF/abc_conditions.yaml index 924e72f2..4be8f7e1 100644 --- a/examples/MDF/abc_conditions.yaml +++ b/examples/MDF/abc_conditions.yaml @@ -1,6 +1,6 @@ abc_conditions: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 graphs: abc_conditions_example: nodes: diff --git a/examples/MDF/abcd.py b/examples/MDF/abcd.py index 4ba690c8..a548f5d3 100644 --- a/examples/MDF/abcd.py +++ b/examples/MDF/abcd.py @@ -1,5 +1,5 @@ """ - Example of ModECI MDF - A simple 4 node graph +Example of ModECI MDF - A simple 4 node graph """ from modeci_mdf.mdf import * diff --git a/examples/MDF/abcd_torch.py b/examples/MDF/abcd_torch.py index ad85353b..a678bec0 100644 --- a/examples/MDF/abcd_torch.py +++ b/examples/MDF/abcd_torch.py @@ -38,6 +38,7 @@ def forward(self, input: torch.Tensor): B = MyLogistic(abcd.B_gain, abcd.B_bias, abcd.B_offset) + #### C class MyExp(nn.Module): def __init__(self, scale, rate, bias, offset): @@ -53,6 +54,7 @@ def forward(self, input: torch.Tensor): C = MyExp(abcd.C_scale, abcd.C_rate, abcd.C_bias, abcd.C_offset) + #### D class MySin(nn.Module): def __init__(self, scale): diff --git a/examples/MDF/arrays.py b/examples/MDF/arrays.py index 33113e8d..8a47ed49 100644 --- a/examples/MDF/arrays.py +++ b/examples/MDF/arrays.py @@ -1,5 +1,5 @@ """ - Example of ModECI MDF - Testing arrays as inputs +Example of ModECI MDF - Testing arrays as inputs """ from modeci_mdf.mdf import * diff --git a/examples/MDF/conditions/Composite_mdf_condition.json b/examples/MDF/conditions/Composite_mdf_condition.json index d6a6c5e6..ecea0f52 100644 --- a/examples/MDF/conditions/Composite_mdf_condition.json +++ b/examples/MDF/conditions/Composite_mdf_condition.json @@ -1,7 +1,7 @@ { "Composite_mdf_condition": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "Composite_mdf_condition_example": { "nodes": { diff --git a/examples/MDF/conditions/Composite_mdf_condition.yaml b/examples/MDF/conditions/Composite_mdf_condition.yaml index 70ca340b..7fb26f97 100644 --- a/examples/MDF/conditions/Composite_mdf_condition.yaml +++ b/examples/MDF/conditions/Composite_mdf_condition.yaml @@ -1,6 +1,6 @@ Composite_mdf_condition: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 graphs: Composite_mdf_condition_example: nodes: diff --git a/examples/MDF/conditions/composite_condition_example.py b/examples/MDF/conditions/composite_condition_example.py index a2884335..4d07b195 100644 --- a/examples/MDF/conditions/composite_condition_example.py +++ b/examples/MDF/conditions/composite_condition_example.py @@ -2,7 +2,6 @@ Example of ModECI MDF - A simple three Node graph satisfying the composite - 'All' condition """ - from modeci_mdf.mdf import * import graph_scheduler import os diff --git a/examples/MDF/conditions/everyNCalls.py b/examples/MDF/conditions/everyNCalls.py index f096516a..a52b42d5 100644 --- a/examples/MDF/conditions/everyNCalls.py +++ b/examples/MDF/conditions/everyNCalls.py @@ -2,6 +2,7 @@ Example of ModECI MDF- A simple 3 Node graph where the first node is satisfying the Static condition and the second and third node is satisfying the Node based conditions """ + import os import graph_scheduler from modeci_mdf.mdf import ( diff --git a/examples/MDF/conditions/everyncalls_condition.json b/examples/MDF/conditions/everyncalls_condition.json index 81aeb728..b06ab4b5 100644 --- a/examples/MDF/conditions/everyncalls_condition.json +++ b/examples/MDF/conditions/everyncalls_condition.json @@ -1,7 +1,7 @@ { "everyncalls_condition": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "everyncalls_example": { "nodes": { diff --git a/examples/MDF/conditions/everyncalls_condition.yaml b/examples/MDF/conditions/everyncalls_condition.yaml index e0bf2131..066532fd 100644 --- a/examples/MDF/conditions/everyncalls_condition.yaml +++ b/examples/MDF/conditions/everyncalls_condition.yaml @@ -1,6 +1,6 @@ everyncalls_condition: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 graphs: everyncalls_example: nodes: diff --git a/examples/MDF/conditions/images/composite_example.png b/examples/MDF/conditions/images/composite_example.png index 09aa37d1..d736a3ad 100644 Binary files a/examples/MDF/conditions/images/composite_example.png and b/examples/MDF/conditions/images/composite_example.png differ diff --git a/examples/MDF/conditions/images/everyncalls.png b/examples/MDF/conditions/images/everyncalls.png index 128b8c56..43bef00a 100644 Binary files a/examples/MDF/conditions/images/everyncalls.png and b/examples/MDF/conditions/images/everyncalls.png differ diff --git a/examples/MDF/conditions/images/threshold.png b/examples/MDF/conditions/images/threshold.png index 2f113afc..1e271be0 100644 Binary files a/examples/MDF/conditions/images/threshold.png and b/examples/MDF/conditions/images/threshold.png differ diff --git a/examples/MDF/conditions/images/timeinterval.png b/examples/MDF/conditions/images/timeinterval.png index eca6e71a..ca23cc77 100644 Binary files a/examples/MDF/conditions/images/timeinterval.png and b/examples/MDF/conditions/images/timeinterval.png differ diff --git a/examples/MDF/conditions/threshold_condition.json b/examples/MDF/conditions/threshold_condition.json index 6f65fef2..08dad14b 100644 --- a/examples/MDF/conditions/threshold_condition.json +++ b/examples/MDF/conditions/threshold_condition.json @@ -1,7 +1,7 @@ { "threshold_condition": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "threshold_example": { "nodes": { diff --git a/examples/MDF/conditions/threshold_condition.yaml b/examples/MDF/conditions/threshold_condition.yaml index a7736dfb..fd5cf809 100644 --- a/examples/MDF/conditions/threshold_condition.yaml +++ b/examples/MDF/conditions/threshold_condition.yaml @@ -1,6 +1,6 @@ threshold_condition: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 graphs: threshold_example: nodes: diff --git a/examples/MDF/conditions/timeInterval.py b/examples/MDF/conditions/timeInterval.py index 6219becb..fa3fa61b 100644 --- a/examples/MDF/conditions/timeInterval.py +++ b/examples/MDF/conditions/timeInterval.py @@ -1,6 +1,7 @@ """ - Example of ModECI MDF - A simple Graph satisfying a time based condition +Example of ModECI MDF - A simple Graph satisfying a time based condition """ + import sys import os import graph_scheduler diff --git a/examples/MDF/conditions/timeinterval_condition.json b/examples/MDF/conditions/timeinterval_condition.json index e9589ea2..95974e72 100644 --- a/examples/MDF/conditions/timeinterval_condition.json +++ b/examples/MDF/conditions/timeinterval_condition.json @@ -1,7 +1,7 @@ { "timeinterval_condition": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "timeinterval_example": { "nodes": { diff --git a/examples/MDF/conditions/timeinterval_condition.yaml b/examples/MDF/conditions/timeinterval_condition.yaml index 2507ab62..5d5caf30 100644 --- a/examples/MDF/conditions/timeinterval_condition.yaml +++ b/examples/MDF/conditions/timeinterval_condition.yaml @@ -1,6 +1,6 @@ timeinterval_condition: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 graphs: timeinterval_example: nodes: diff --git a/examples/MDF/images/abc_conditions.png b/examples/MDF/images/abc_conditions.png index 5883c83c..47058264 100644 Binary files a/examples/MDF/images/abc_conditions.png and b/examples/MDF/images/abc_conditions.png differ diff --git a/examples/MDF/images/abcd.png b/examples/MDF/images/abcd.png index 8f9fdba8..c8905c7e 100644 Binary files a/examples/MDF/images/abcd.png and b/examples/MDF/images/abcd.png differ diff --git a/examples/MDF/images/abcd_3.png b/examples/MDF/images/abcd_3.png index 3aea92db..717844f4 100644 Binary files a/examples/MDF/images/abcd_3.png and b/examples/MDF/images/abcd_3.png differ diff --git a/examples/MDF/images/arrays.png b/examples/MDF/images/arrays.png index ef2e05c0..2ad0f6aa 100644 Binary files a/examples/MDF/images/arrays.png and b/examples/MDF/images/arrays.png differ diff --git a/examples/MDF/images/newton.png b/examples/MDF/images/newton.png index c25aadbb..99b439f7 100644 Binary files a/examples/MDF/images/newton.png and b/examples/MDF/images/newton.png differ diff --git a/examples/MDF/images/newton_plot.png b/examples/MDF/images/newton_plot.png index dba7b1a3..cdb4c6cc 100644 Binary files a/examples/MDF/images/newton_plot.png and b/examples/MDF/images/newton_plot.png differ diff --git a/examples/MDF/images/params_funcs.png b/examples/MDF/images/params_funcs.png index 37b320de..fbb764ae 100644 Binary files a/examples/MDF/images/params_funcs.png and b/examples/MDF/images/params_funcs.png differ diff --git a/examples/MDF/images/simple.png b/examples/MDF/images/simple.png index cd7416e3..140e8477 100644 Binary files a/examples/MDF/images/simple.png and b/examples/MDF/images/simple.png differ diff --git a/examples/MDF/images/simple_3.png b/examples/MDF/images/simple_3.png index df12fb08..01c90210 100644 Binary files a/examples/MDF/images/simple_3.png and b/examples/MDF/images/simple_3.png differ diff --git a/examples/MDF/images/states.png b/examples/MDF/images/states.png index d3a28e40..6c2c45a9 100644 Binary files a/examples/MDF/images/states.png and b/examples/MDF/images/states.png differ diff --git a/examples/MDF/images/switched_rlc_circuit.png b/examples/MDF/images/switched_rlc_circuit.png index f9d5b94e..4382427c 100644 Binary files a/examples/MDF/images/switched_rlc_circuit.png and b/examples/MDF/images/switched_rlc_circuit.png differ diff --git a/examples/MDF/images/switched_rlc_plot.png b/examples/MDF/images/switched_rlc_plot.png index e9b6fcd4..6627147d 100644 Binary files a/examples/MDF/images/switched_rlc_plot.png and b/examples/MDF/images/switched_rlc_plot.png differ diff --git a/examples/MDF/params_funcs.py b/examples/MDF/params_funcs.py index f1c3306e..fc544b68 100644 --- a/examples/MDF/params_funcs.py +++ b/examples/MDF/params_funcs.py @@ -1,5 +1,5 @@ """ - Example of ModECI MDF - Showing usage of parameters and functions +Example of ModECI MDF - Showing usage of parameters and functions """ from modeci_mdf.mdf import * diff --git a/examples/MDF/scaling.py b/examples/MDF/scaling.py index 7c84fbd1..934e59f1 100644 --- a/examples/MDF/scaling.py +++ b/examples/MDF/scaling.py @@ -1,6 +1,6 @@ """ - Example of ModECI MDF - Testing arrays as inputs - this allows graphs of - various scales/sizes to be generated for testing purposes +Example of ModECI MDF - Testing arrays as inputs - this allows graphs of +various scales/sizes to be generated for testing purposes """ from modeci_mdf.mdf import * diff --git a/examples/MDF/simple.py b/examples/MDF/simple.py index 85264f29..8ca66c5b 100644 --- a/examples/MDF/simple.py +++ b/examples/MDF/simple.py @@ -1,5 +1,5 @@ """ - Example of ModECI MDF - A simple 2 node graph. +Example of ModECI MDF - A simple 2 node graph. """ import os diff --git a/examples/MDF/states.py b/examples/MDF/states.py index edca885b..b293f764 100644 --- a/examples/MDF/states.py +++ b/examples/MDF/states.py @@ -1,5 +1,5 @@ """ - Example of ModECI MDF - Testing state variables +Example of ModECI MDF - Testing state variables """ from modeci_mdf.mdf import * diff --git a/examples/NeuroML/ABCD.1.mdf.png b/examples/NeuroML/ABCD.1.mdf.png index 3dfb0906..e63f701e 100644 Binary files a/examples/NeuroML/ABCD.1.mdf.png and b/examples/NeuroML/ABCD.1.mdf.png differ diff --git a/examples/NeuroML/ABCD.json b/examples/NeuroML/ABCD.json index 4ae8044f..fa8fbba1 100644 --- a/examples/NeuroML/ABCD.json +++ b/examples/NeuroML/ABCD.json @@ -1,6 +1,6 @@ { "ABCD": { - "version": "NeuroMLlite v0.6.0", + "version": "NeuroMLlite v0.6.1", "notes": "Example of a simplified network", "parameters": { "A_initial": 0.1, diff --git a/examples/NeuroML/ABCD.mdf.png b/examples/NeuroML/ABCD.mdf.png index c21ed050..c263e3f9 100644 Binary files a/examples/NeuroML/ABCD.mdf.png and b/examples/NeuroML/ABCD.mdf.png differ diff --git a/examples/NeuroML/ABCD.net.nml b/examples/NeuroML/ABCD.net.nml index 996a1dc6..041a98c1 100644 --- a/examples/NeuroML/ABCD.net.nml +++ b/examples/NeuroML/ABCD.net.nml @@ -1,5 +1,5 @@ - Generated by NeuroMLlite v0.6.0 + Generated by NeuroMLlite v0.6.1 Generated network: ABCD Generation seed: 1234 NeuroMLlite parameters: diff --git a/examples/NeuroML/FN.gv.png b/examples/NeuroML/FN.gv.png index a50c9690..bb3fb0ab 100644 Binary files a/examples/NeuroML/FN.gv.png and b/examples/NeuroML/FN.gv.png differ diff --git a/examples/NeuroML/FN.json b/examples/NeuroML/FN.json index 38f564e1..c34637f8 100644 --- a/examples/NeuroML/FN.json +++ b/examples/NeuroML/FN.json @@ -1,6 +1,6 @@ { "FN": { - "version": "NeuroMLlite v0.6.0", + "version": "NeuroMLlite v0.6.1", "notes": "FitzHugh Nagumo cell model - originally specified in NeuroML/LEMS", "parameters": { "initial_w": 0.0, diff --git a/examples/NeuroML/FN.net.nml b/examples/NeuroML/FN.net.nml index 8087b3bf..aa08ece2 100644 --- a/examples/NeuroML/FN.net.nml +++ b/examples/NeuroML/FN.net.nml @@ -1,5 +1,5 @@ - Generated by NeuroMLlite v0.6.0 + Generated by NeuroMLlite v0.6.1 Generated network: FN Generation seed: 1234 NeuroMLlite parameters: diff --git a/examples/NeuroML/FNmulti.png b/examples/NeuroML/FNmulti.png index 02cc5867..7ad48198 100644 Binary files a/examples/NeuroML/FNmulti.png and b/examples/NeuroML/FNmulti.png differ diff --git a/examples/NeuroML/Izh.png b/examples/NeuroML/Izh.png index 02f8239c..0ee1a7b9 100644 Binary files a/examples/NeuroML/Izh.png and b/examples/NeuroML/Izh.png differ diff --git a/examples/NeuroML/Izh_run.png b/examples/NeuroML/Izh_run.png index 656f13f0..51949a3e 100644 Binary files a/examples/NeuroML/Izh_run.png and b/examples/NeuroML/Izh_run.png differ diff --git a/examples/NeuroML/IzhikevichTest.gv.png b/examples/NeuroML/IzhikevichTest.gv.png index dd51b554..120eef86 100644 Binary files a/examples/NeuroML/IzhikevichTest.gv.png and b/examples/NeuroML/IzhikevichTest.gv.png differ diff --git a/examples/NeuroML/IzhikevichTest.net.nml b/examples/NeuroML/IzhikevichTest.net.nml index 1b9da89e..fcc8f5b7 100644 --- a/examples/NeuroML/IzhikevichTest.net.nml +++ b/examples/NeuroML/IzhikevichTest.net.nml @@ -1,5 +1,5 @@ - Generated by NeuroMLlite v0.6.0 + Generated by NeuroMLlite v0.6.1 Generated network: IzhikevichTest Generation seed: 1234 NeuroMLlite parameters: diff --git a/examples/NeuroML/IzhikevichTest.nmllite.yaml b/examples/NeuroML/IzhikevichTest.nmllite.yaml index 62888fbb..55800357 100644 --- a/examples/NeuroML/IzhikevichTest.nmllite.yaml +++ b/examples/NeuroML/IzhikevichTest.nmllite.yaml @@ -1,5 +1,5 @@ IzhikevichTest: - version: NeuroMLlite v0.6.0 + version: NeuroMLlite v0.6.1 notes: Example Izhikevich parameters: N: 1 diff --git a/examples/NeuroML/LEMS_SimABCD.png b/examples/NeuroML/LEMS_SimABCD.png index 98b10485..d34c68c1 100644 Binary files a/examples/NeuroML/LEMS_SimABCD.png and b/examples/NeuroML/LEMS_SimABCD.png differ diff --git a/examples/NeuroML/LEMS_SimABCD.xml b/examples/NeuroML/LEMS_SimABCD.xml index ca361699..3e2ac574 100644 --- a/examples/NeuroML/LEMS_SimABCD.xml +++ b/examples/NeuroML/LEMS_SimABCD.xml @@ -2,7 +2,7 @@ diff --git a/examples/NeuroML/LEMS_SimFN.png b/examples/NeuroML/LEMS_SimFN.png index d282d58d..0cf55dea 100644 Binary files a/examples/NeuroML/LEMS_SimFN.png and b/examples/NeuroML/LEMS_SimFN.png differ diff --git a/examples/NeuroML/LEMS_SimFN.xml b/examples/NeuroML/LEMS_SimFN.xml index 206a9299..4c8fc16a 100644 --- a/examples/NeuroML/LEMS_SimFN.xml +++ b/examples/NeuroML/LEMS_SimFN.xml @@ -2,7 +2,7 @@ diff --git a/examples/NeuroML/LEMS_SimIzhikevichTest.png b/examples/NeuroML/LEMS_SimIzhikevichTest.png index ea457421..e85f0ea7 100644 Binary files a/examples/NeuroML/LEMS_SimIzhikevichTest.png and b/examples/NeuroML/LEMS_SimIzhikevichTest.png differ diff --git a/examples/NeuroML/LEMS_SimIzhikevichTest.xml b/examples/NeuroML/LEMS_SimIzhikevichTest.xml index 54490d7d..884e3b55 100644 --- a/examples/NeuroML/LEMS_SimIzhikevichTest.xml +++ b/examples/NeuroML/LEMS_SimIzhikevichTest.xml @@ -2,7 +2,7 @@ diff --git a/examples/NeuroML/MDFFNrun.multi.png b/examples/NeuroML/MDFFNrun.multi.png index 89b829e0..795174ec 100644 Binary files a/examples/NeuroML/MDFFNrun.multi.png and b/examples/NeuroML/MDFFNrun.multi.png differ diff --git a/examples/NeuroML/MDFFNrun.png b/examples/NeuroML/MDFFNrun.png index 577b0fc5..faf75a54 100644 Binary files a/examples/NeuroML/MDFFNrun.png and b/examples/NeuroML/MDFFNrun.png differ diff --git a/examples/NeuroML/PyNN/HH.mdf.png b/examples/NeuroML/PyNN/HH.mdf.png index 512925d7..7cb45a8b 100644 Binary files a/examples/NeuroML/PyNN/HH.mdf.png and b/examples/NeuroML/PyNN/HH.mdf.png differ diff --git a/examples/NeuroML/PyNN/HH.net.nml b/examples/NeuroML/PyNN/HH.net.nml index da98d695..5303a517 100644 --- a/examples/NeuroML/PyNN/HH.net.nml +++ b/examples/NeuroML/PyNN/HH.net.nml @@ -1,5 +1,5 @@ - Generated by NeuroMLlite v0.6.0 + Generated by NeuroMLlite v0.6.1 Generated network: HH Generation seed: 1234 NeuroMLlite parameters: diff --git a/examples/NeuroML/PyNN/InputWeights.mdf.png b/examples/NeuroML/PyNN/InputWeights.mdf.png index d6496618..a34f50a1 100644 Binary files a/examples/NeuroML/PyNN/InputWeights.mdf.png and b/examples/NeuroML/PyNN/InputWeights.mdf.png differ diff --git a/examples/NeuroML/PyNN/InputWeights.net.nml b/examples/NeuroML/PyNN/InputWeights.net.nml index 437728db..cfd553e4 100644 --- a/examples/NeuroML/PyNN/InputWeights.net.nml +++ b/examples/NeuroML/PyNN/InputWeights.net.nml @@ -1,5 +1,5 @@ - Generated by NeuroMLlite v0.6.0 + Generated by NeuroMLlite v0.6.1 Generated network: InputWeights Generation seed: 1234 NeuroMLlite parameters: diff --git a/examples/NeuroML/PyNN/Net1.mdf.png b/examples/NeuroML/PyNN/Net1.mdf.png index dd3fd4b4..8d21027b 100644 Binary files a/examples/NeuroML/PyNN/Net1.mdf.png and b/examples/NeuroML/PyNN/Net1.mdf.png differ diff --git a/examples/NeuroML/PyNN/Net1.net.nml b/examples/NeuroML/PyNN/Net1.net.nml index 87c1eb1c..25a38900 100644 --- a/examples/NeuroML/PyNN/Net1.net.nml +++ b/examples/NeuroML/PyNN/Net1.net.nml @@ -1,5 +1,5 @@ - Generated by NeuroMLlite v0.6.0 + Generated by NeuroMLlite v0.6.1 Generated network: Net1 Generation seed: 1234 NeuroMLlite parameters: diff --git a/examples/NeuroML/PyNN/OneCell.mdf.png b/examples/NeuroML/PyNN/OneCell.mdf.png index f4ee41af..d67c5aff 100644 Binary files a/examples/NeuroML/PyNN/OneCell.mdf.png and b/examples/NeuroML/PyNN/OneCell.mdf.png differ diff --git a/examples/NeuroML/PyNN/OneCell.net.nml b/examples/NeuroML/PyNN/OneCell.net.nml index b2361b0a..26f4b686 100644 --- a/examples/NeuroML/PyNN/OneCell.net.nml +++ b/examples/NeuroML/PyNN/OneCell.net.nml @@ -1,5 +1,5 @@ - Generated by NeuroMLlite v0.6.0 + Generated by NeuroMLlite v0.6.1 Generated network: OneCell Generation seed: 1234 NeuroMLlite parameters: diff --git a/examples/NeuroML/PyNN/SimpleNet.mdf.png b/examples/NeuroML/PyNN/SimpleNet.mdf.png index a595f6b7..52d272aa 100644 Binary files a/examples/NeuroML/PyNN/SimpleNet.mdf.png and b/examples/NeuroML/PyNN/SimpleNet.mdf.png differ diff --git a/examples/NeuroML/PyNN/SimpleNet.net.nml b/examples/NeuroML/PyNN/SimpleNet.net.nml index 1860cfbb..89376dc5 100644 --- a/examples/NeuroML/PyNN/SimpleNet.net.nml +++ b/examples/NeuroML/PyNN/SimpleNet.net.nml @@ -1,5 +1,5 @@ - Generated by NeuroMLlite v0.6.0 + Generated by NeuroMLlite v0.6.1 Generated network: SimpleNet Generation seed: 1234 NeuroMLlite parameters: diff --git a/examples/NeuroML/SimABCD.json b/examples/NeuroML/SimABCD.json index c111c11f..0eefe726 100644 --- a/examples/NeuroML/SimABCD.json +++ b/examples/NeuroML/SimABCD.json @@ -1,6 +1,6 @@ { "SimABCD": { - "version": "NeuroMLlite v0.6.0", + "version": "NeuroMLlite v0.6.1", "network": "ABCD.json", "duration": 5000.0, "dt": 100.0, diff --git a/examples/NeuroML/SimFN.json b/examples/NeuroML/SimFN.json index 4b90990b..494efc98 100644 --- a/examples/NeuroML/SimFN.json +++ b/examples/NeuroML/SimFN.json @@ -1,6 +1,6 @@ { "SimFN": { - "version": "NeuroMLlite v0.6.0", + "version": "NeuroMLlite v0.6.1", "network": "FN.json", "duration": 100.0, "dt": 0.05, diff --git a/examples/NeuroML/SimIzhikevichTest.yaml b/examples/NeuroML/SimIzhikevichTest.yaml index 2e418d84..f98bfd7a 100644 --- a/examples/NeuroML/SimIzhikevichTest.yaml +++ b/examples/NeuroML/SimIzhikevichTest.yaml @@ -1,5 +1,5 @@ SimIzhikevichTest: - version: NeuroMLlite v0.6.0 + version: NeuroMLlite v0.6.1 network: IzhikevichTest.nmllite.yaml duration: 700.0 dt: 0.025 diff --git a/examples/ONNX/ab.json b/examples/ONNX/ab.json index 10cce222..d7d8097c 100644 --- a/examples/ONNX/ab.json +++ b/examples/ONNX/ab.json @@ -1,7 +1,7 @@ { "ONNX Model": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "main_graph": { "nodes": { diff --git a/examples/ONNX/ab.png b/examples/ONNX/ab.png index 3edee23c..2f65a46f 100644 Binary files a/examples/ONNX/ab.png and b/examples/ONNX/ab.png differ diff --git a/examples/ONNX/ab.yaml b/examples/ONNX/ab.yaml index 25eb84bd..96efadbd 100644 --- a/examples/ONNX/ab.yaml +++ b/examples/ONNX/ab.yaml @@ -1,6 +1,6 @@ ONNX Model: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 graphs: main_graph: nodes: diff --git a/examples/ONNX/abc.json b/examples/ONNX/abc.json index 3c0dce3f..9213fb9f 100644 --- a/examples/ONNX/abc.json +++ b/examples/ONNX/abc.json @@ -1,7 +1,7 @@ { "ONNX Model": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "main_graph": { "nodes": { @@ -33,7 +33,7 @@ } } }, - "Loop_3": { + "Loop_6": { "input_ports": { "B_loop_count": { "shape": [ @@ -55,7 +55,7 @@ "graph_sub_graph": { "id": "sub_graph", "nodes": { - "Div_5": { + "Div_8": { "input_ports": { "x.11": { "shape": [ @@ -69,7 +69,7 @@ "B": { "value": 10.0 }, - "Div_5": { + "Div_8": { "function": "onnx::Div", "args": { "A": "x.11", @@ -79,16 +79,16 @@ }, "output_ports": { "x": { - "value": "Div_5" + "value": "Div_8" } } }, - "Identity_6": { + "Identity_9": { "parameters": { "input": { "value": true }, - "Identity_6": { + "Identity_9": { "function": "onnx::Identity", "args": { "input": "input" @@ -97,7 +97,7 @@ }, "output_ports": { "_11": { - "value": "Identity_6" + "value": "Identity_9" } } } @@ -108,7 +108,7 @@ "cond": { "value": true }, - "Loop_3": { + "Loop_6": { "function": "onnx::Loop", "args": { "M": "B_loop_count", @@ -119,7 +119,7 @@ }, "output_ports": { "x.3": { - "value": "Loop_3" + "value": "Loop_6" } } }, @@ -152,14 +152,14 @@ } }, "edges": { - "/A/Add._A_Add_output_0_Loop_3._A_Add_output_0": { + "/A/Add._A_Add_output_0_Loop_6._A_Add_output_0": { "sender": "/A/Add", - "receiver": "Loop_3", + "receiver": "Loop_6", "sender_port": "_A_Add_output_0", "receiver_port": "_A_Add_output_0" }, - "Loop_3.x.3_/C/Mul.x.3": { - "sender": "Loop_3", + "Loop_6.x.3_/C/Mul.x.3": { + "sender": "Loop_6", "receiver": "/C/Mul", "sender_port": "x.3", "receiver_port": "x.3" diff --git a/examples/ONNX/abc.yaml b/examples/ONNX/abc.yaml index f96b8fd9..52481493 100644 --- a/examples/ONNX/abc.yaml +++ b/examples/ONNX/abc.yaml @@ -1,6 +1,6 @@ ONNX Model: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 graphs: main_graph: nodes: @@ -22,7 +22,7 @@ ONNX Model: output_ports: _A_Add_output_0: value: /A/Add - Loop_3: + Loop_6: input_ports: B_loop_count: shape: @@ -39,7 +39,7 @@ ONNX Model: graph_sub_graph: id: sub_graph nodes: - Div_5: + Div_8: input_ports: x.11: shape: @@ -49,28 +49,28 @@ ONNX Model: parameters: B: value: 10.0 - Div_5: + Div_8: function: onnx::Div args: A: x.11 B: B output_ports: x: - value: Div_5 - Identity_6: + value: Div_8 + Identity_9: parameters: input: value: true - Identity_6: + Identity_9: function: onnx::Identity args: input: input output_ports: _11: - value: Identity_6 + value: Identity_9 cond: value: true - Loop_3: + Loop_6: function: onnx::Loop args: M: B_loop_count @@ -78,7 +78,7 @@ ONNX Model: v_initial: _A_Add_output_0 output_ports: x.3: - value: Loop_3 + value: Loop_6 /C/Mul: input_ports: x.3: @@ -97,13 +97,13 @@ ONNX Model: _13: value: /C/Mul edges: - /A/Add._A_Add_output_0_Loop_3._A_Add_output_0: + /A/Add._A_Add_output_0_Loop_6._A_Add_output_0: sender: /A/Add - receiver: Loop_3 + receiver: Loop_6 sender_port: _A_Add_output_0 receiver_port: _A_Add_output_0 - Loop_3.x.3_/C/Mul.x.3: - sender: Loop_3 + Loop_6.x.3_/C/Mul.x.3: + sender: Loop_6 receiver: /C/Mul sender_port: x.3 receiver_port: x.3 diff --git a/examples/ONNX/abc_basic-mdf.json b/examples/ONNX/abc_basic-mdf.json index 197f56b4..03225073 100644 --- a/examples/ONNX/abc_basic-mdf.json +++ b/examples/ONNX/abc_basic-mdf.json @@ -1,7 +1,7 @@ { "ONNX Model": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "main_graph": { "nodes": { diff --git a/examples/ONNX/abc_basic-mdf.yaml b/examples/ONNX/abc_basic-mdf.yaml index f3d1660a..f997af0c 100644 --- a/examples/ONNX/abc_basic-mdf.yaml +++ b/examples/ONNX/abc_basic-mdf.yaml @@ -1,6 +1,6 @@ ONNX Model: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 graphs: main_graph: nodes: diff --git a/examples/ONNX/abc_basic.py b/examples/ONNX/abc_basic.py index 9e811582..303c0141 100644 --- a/examples/ONNX/abc_basic.py +++ b/examples/ONNX/abc_basic.py @@ -4,6 +4,7 @@ - Exports in to ONNX using a combination of tracing and scripting - Converts it to MDF """ + import torch import onnx diff --git a/examples/ONNX/abcd.json b/examples/ONNX/abcd.json index d5ac3113..2ded22d9 100644 --- a/examples/ONNX/abcd.json +++ b/examples/ONNX/abcd.json @@ -1,7 +1,7 @@ { "ONNX Model": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "main_graph": { "nodes": { diff --git a/examples/ONNX/abcd.yaml b/examples/ONNX/abcd.yaml index fbbd8a30..bfa60c3c 100644 --- a/examples/ONNX/abcd.yaml +++ b/examples/ONNX/abcd.yaml @@ -1,6 +1,6 @@ ONNX Model: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 graphs: main_graph: nodes: diff --git a/examples/ONNX/simple_ab.py b/examples/ONNX/simple_ab.py index 3418dccd..0b369e38 100644 --- a/examples/ONNX/simple_ab.py +++ b/examples/ONNX/simple_ab.py @@ -4,6 +4,7 @@ - Exports in to ONNX using a combination of tracing and scripting - Converts it to MDF """ + import torch import onnx import sys diff --git a/examples/ONNX/simple_abc.py b/examples/ONNX/simple_abc.py index 4ce3cd40..62403b85 100644 --- a/examples/ONNX/simple_abc.py +++ b/examples/ONNX/simple_abc.py @@ -4,6 +4,7 @@ - Exports in to ONNX using a combination of tracing and scripting - Converts it to MDF """ + import torch import onnx import os diff --git a/examples/ONNX/simple_abcd.py b/examples/ONNX/simple_abcd.py index e584f707..5489c9ee 100644 --- a/examples/ONNX/simple_abcd.py +++ b/examples/ONNX/simple_abcd.py @@ -4,6 +4,7 @@ - Exports in to ONNX using a combination of tracing and scripting - Converts it to MDF """ + import torch import onnx diff --git a/examples/PyTorch/MDF_PyTorch/ABCD.onnx b/examples/PyTorch/MDF_PyTorch/ABCD.onnx index b05f48a6..c772c3c3 100644 Binary files a/examples/PyTorch/MDF_PyTorch/ABCD.onnx and b/examples/PyTorch/MDF_PyTorch/ABCD.onnx differ diff --git a/examples/PyTorch/MDF_PyTorch/ABCD_pytorch.py b/examples/PyTorch/MDF_PyTorch/ABCD_pytorch.py index 2cfa3a5b..56095be2 100644 --- a/examples/PyTorch/MDF_PyTorch/ABCD_pytorch.py +++ b/examples/PyTorch/MDF_PyTorch/ABCD_pytorch.py @@ -1,5 +1,5 @@ """ -This script has been generated by modeci_mdf v0.4.12. +This script has been generated by modeci_mdf v0.4.13. It is an export of a MDF model (mdf.s - MDF stateful, i.e. full MDF allowing stateful parameters) to PyTorch """ diff --git a/examples/PyTorch/MDF_PyTorch/Arrays.onnx b/examples/PyTorch/MDF_PyTorch/Arrays.onnx index 88ce0174..fb8b1035 100644 Binary files a/examples/PyTorch/MDF_PyTorch/Arrays.onnx and b/examples/PyTorch/MDF_PyTorch/Arrays.onnx differ diff --git a/examples/PyTorch/MDF_PyTorch/Arrays_pytorch.py b/examples/PyTorch/MDF_PyTorch/Arrays_pytorch.py index 65c08a97..362f7f68 100644 --- a/examples/PyTorch/MDF_PyTorch/Arrays_pytorch.py +++ b/examples/PyTorch/MDF_PyTorch/Arrays_pytorch.py @@ -1,5 +1,5 @@ """ -This script has been generated by modeci_mdf v0.4.12. +This script has been generated by modeci_mdf v0.4.13. It is an export of a MDF model (mdf.s - MDF stateful, i.e. full MDF allowing stateful parameters) to PyTorch """ diff --git a/examples/PyTorch/MDF_PyTorch/Simple.onnx b/examples/PyTorch/MDF_PyTorch/Simple.onnx index 6e9c440a..9ca615f2 100644 Binary files a/examples/PyTorch/MDF_PyTorch/Simple.onnx and b/examples/PyTorch/MDF_PyTorch/Simple.onnx differ diff --git a/examples/PyTorch/MDF_PyTorch/Simple_pytorch.py b/examples/PyTorch/MDF_PyTorch/Simple_pytorch.py index 8591755c..d98b074e 100644 --- a/examples/PyTorch/MDF_PyTorch/Simple_pytorch.py +++ b/examples/PyTorch/MDF_PyTorch/Simple_pytorch.py @@ -1,5 +1,5 @@ """ -This script has been generated by modeci_mdf v0.4.12. +This script has been generated by modeci_mdf v0.4.13. It is an export of a MDF model (mdf.s - MDF stateful, i.e. full MDF allowing stateful parameters) to PyTorch """ diff --git a/examples/PyTorch/MDF_PyTorch/translated_ABCD_pytorch.py b/examples/PyTorch/MDF_PyTorch/translated_ABCD_pytorch.py index b89172ad..4fdc49c9 100644 --- a/examples/PyTorch/MDF_PyTorch/translated_ABCD_pytorch.py +++ b/examples/PyTorch/MDF_PyTorch/translated_ABCD_pytorch.py @@ -1,4 +1,5 @@ "Converted examples from MDF models version(mdf.0) to PyTorch/ONNX" + import torch import torch.nn as nn import onnx diff --git a/examples/PyTorch/MDF_PyTorch/translated_Arrays_pytorch.py b/examples/PyTorch/MDF_PyTorch/translated_Arrays_pytorch.py index d9007f2d..c099b3a7 100644 --- a/examples/PyTorch/MDF_PyTorch/translated_Arrays_pytorch.py +++ b/examples/PyTorch/MDF_PyTorch/translated_Arrays_pytorch.py @@ -1,4 +1,5 @@ "Converted examples from MDF models version(mdf.0) to PyTorch/ONNX" + import torch import torch.nn as nn import onnx diff --git a/examples/PyTorch/MDF_PyTorch/translated_Simple_pytorch.py b/examples/PyTorch/MDF_PyTorch/translated_Simple_pytorch.py index 1e1c4156..23169ade 100644 --- a/examples/PyTorch/MDF_PyTorch/translated_Simple_pytorch.py +++ b/examples/PyTorch/MDF_PyTorch/translated_Simple_pytorch.py @@ -1,4 +1,5 @@ "Converted examples from MDF models version(mdf.0) to PyTorch/ONNX" + import torch import torch.nn as nn import onnx diff --git a/examples/PyTorch/PyTorch_MDF/benchmark_script/squeezenet1_1_benchmark.json b/examples/PyTorch/PyTorch_MDF/benchmark_script/squeezenet1_1_benchmark.json index ac988b55..4558ea97 100644 --- a/examples/PyTorch/PyTorch_MDF/benchmark_script/squeezenet1_1_benchmark.json +++ b/examples/PyTorch/PyTorch_MDF/benchmark_script/squeezenet1_1_benchmark.json @@ -2,11 +2,11 @@ { "model_name": "squeezenet1_1", "model_type": "SqueezeNet", - "pytorch_time": 0.0694, + "pytorch_time": 0.0839, "pytorch_predictions": 10, - "mdf_time": 3.6003, + "mdf_time": 4.4275, "mdf_predictions": 10, "node density": 66, - "mdf : pytorch ratio": "51.86" + "mdf : pytorch ratio": "52.79" } ] diff --git a/examples/PyTorch/PyTorch_MDF/convolution.py b/examples/PyTorch/PyTorch_MDF/convolution.py index 35471adf..92a5a6bb 100644 --- a/examples/PyTorch/PyTorch_MDF/convolution.py +++ b/examples/PyTorch/PyTorch_MDF/convolution.py @@ -4,6 +4,7 @@ from torch import nn from modeci_mdf.interfaces.pytorch import pytorch_to_mdf + # Simple CNN class CNN(nn.Module): def __init__(self, in_channels=1, num_classes=10): diff --git a/examples/PyTorch/PyTorch_MDF/simple_Convolution.py b/examples/PyTorch/PyTorch_MDF/simple_Convolution.py index 21ca155d..b51bfa8f 100644 --- a/examples/PyTorch/PyTorch_MDF/simple_Convolution.py +++ b/examples/PyTorch/PyTorch_MDF/simple_Convolution.py @@ -4,6 +4,7 @@ from torch import nn # All neural network modules from modeci_mdf.interfaces.pytorch import pytorch_to_mdf + # Simple CNN class CNN(nn.Module): def __init__(self, in_channels=1, num_classes=10): diff --git a/examples/PyTorch/inception.json b/examples/PyTorch/inception.json index d112ec0e..6eef335f 100644 --- a/examples/PyTorch/inception.json +++ b/examples/PyTorch/inception.json @@ -1,7 +1,7 @@ { "InceptionBlocks": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "InceptionBlocksGraph": { "nodes": { diff --git a/examples/PyTorch/inception.png b/examples/PyTorch/inception.png index 7b70f702..f1e3a55b 100644 Binary files a/examples/PyTorch/inception.png and b/examples/PyTorch/inception.png differ diff --git a/examples/PyTorch/mlp_pure_mdf.json b/examples/PyTorch/mlp_pure_mdf.json index 7b11885a..179bfbb3 100644 --- a/examples/PyTorch/mlp_pure_mdf.json +++ b/examples/PyTorch/mlp_pure_mdf.json @@ -1,7 +1,7 @@ { "mlp_pure_mdf": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "mlp_pure_mdf": { "nodes": { diff --git a/examples/PyTorch/mlp_pure_mdf.png b/examples/PyTorch/mlp_pure_mdf.png index 3e3c386a..4947f30d 100644 Binary files a/examples/PyTorch/mlp_pure_mdf.png and b/examples/PyTorch/mlp_pure_mdf.png differ diff --git a/examples/PyTorch/mlp_pure_mdf.py b/examples/PyTorch/mlp_pure_mdf.py index bb8df955..a216bbeb 100644 --- a/examples/PyTorch/mlp_pure_mdf.py +++ b/examples/PyTorch/mlp_pure_mdf.py @@ -6,6 +6,7 @@ import time import os + # Note: the weights for this model were precomputed and saved in the file weights.h5 def get_weight_info(): diff --git a/examples/PyTorch/mlp_pure_mdf.yaml b/examples/PyTorch/mlp_pure_mdf.yaml index 287d2c81..b4fce58c 100644 --- a/examples/PyTorch/mlp_pure_mdf.yaml +++ b/examples/PyTorch/mlp_pure_mdf.yaml @@ -1,6 +1,6 @@ mlp_pure_mdf: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.12 + generating_application: Python modeci-mdf v0.4.13 graphs: mlp_pure_mdf: nodes: diff --git a/examples/PyTorch/mod_torch_builtins.py b/examples/PyTorch/mod_torch_builtins.py index 32665f5e..c4932f25 100644 --- a/examples/PyTorch/mod_torch_builtins.py +++ b/examples/PyTorch/mod_torch_builtins.py @@ -2,6 +2,7 @@ Wrap commonly-used torch builtins in nn.Module subclass for easier automatic construction of script """ + import torch import torch.nn as nn import torch.nn.functional as F diff --git a/examples/PyTorch/simple_pytorch_to_mdf.1.png b/examples/PyTorch/simple_pytorch_to_mdf.1.png index 2b487df2..85a88cea 100644 Binary files a/examples/PyTorch/simple_pytorch_to_mdf.1.png and b/examples/PyTorch/simple_pytorch_to_mdf.1.png differ diff --git a/examples/PyTorch/simple_pytorch_to_mdf.json b/examples/PyTorch/simple_pytorch_to_mdf.json index f14fdca5..4c54f15b 100644 --- a/examples/PyTorch/simple_pytorch_to_mdf.json +++ b/examples/PyTorch/simple_pytorch_to_mdf.json @@ -1,7 +1,7 @@ { "SimpleNet": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.12", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "SimpleNetGraph": { "nodes": { diff --git a/examples/PyTorch/simple_pytorch_to_mdf.png b/examples/PyTorch/simple_pytorch_to_mdf.png index 4439cf61..d18239be 100644 Binary files a/examples/PyTorch/simple_pytorch_to_mdf.png and b/examples/PyTorch/simple_pytorch_to_mdf.png differ diff --git a/examples/TensorFlow/Keras/IRIS/README.md b/examples/TensorFlow/Keras/IRIS/README.md index 6672244d..508a5d62 100644 --- a/examples/TensorFlow/Keras/IRIS/README.md +++ b/examples/TensorFlow/Keras/IRIS/README.md @@ -2,7 +2,7 @@ **For more details on Keras and the current state of the Keras->MDF mapping see the [MNIST example](../MNIST).** -This model uses the [IRIS dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set) in the [trained Keras model](keras_model.py) and the MDF equivalent. +This model uses the [IRIS dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set) in the [trained Keras model](iris_keras_model.py) and the MDF equivalent. ### Summarize Model diff --git a/examples/TensorFlow/Keras/IRIS/iris_keras_model.py b/examples/TensorFlow/Keras/IRIS/iris_keras_model.py new file mode 100644 index 00000000..c58e226c --- /dev/null +++ b/examples/TensorFlow/Keras/IRIS/iris_keras_model.py @@ -0,0 +1,97 @@ +import sys +import tensorflow as tf + +# tf.__version__ + +from keras.layers import Dense +from keras.utils import plot_model +from keras.models import Sequential + +# from keras_visualizer import visualizer +from keras import layers + +import matplotlib.pyplot as plt +import numpy as np + +import matplotlib.pyplot as plt +import numpy as np + +from sklearn.datasets import load_iris +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler + +# Controlling randomness, to facilitate testing/reproducibility +# based on https://keras.io/examples/keras_recipes/reproducibility_recipes/ +from keras.utils import set_random_seed + + +def run(): + + set_random_seed(1234) + tf.config.experimental.enable_op_determinism() + + print("Loading data") + iris = load_iris() + + X, y = iris.data, iris.target + + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42 + ) + + # standardize the data + scaler = StandardScaler() + X_train = scaler.fit_transform(X_train) + X_test = scaler.fit_transform(X_test) + + # Build Model + print("Building the Model") + model = tf.keras.models.Sequential() + model.add( + tf.keras.layers.Dense( + 32, activation=tf.nn.relu, input_dim=4, name="first_layer" + ) + ) # hidden layers + model.add(tf.keras.layers.Dense(64, activation=tf.nn.relu, name="second_layer")) + model.add(tf.keras.layers.Dense(64, activation=tf.nn.relu, name="third_layer")) + model.add( + tf.keras.layers.Dense(3, activation=tf.nn.softmax, name="fourth_layer") + ) # output layer + + # Compile Model + print("Compiling the model") + model.compile( + optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"] + ) + + # train the model + print("Training the Model") + model.fit(X_train, y_train, epochs=3) + + # check if the model actually generalize the data. + print("Accuracy of our model is:") + val_loss, val_acc = model.evaluate(X_test, y_test) + print(val_loss, val_acc) + + # Print summary + model.summary() + + # plot the model + print("Plotting the model") + plot_model( + model, to_file="model_on_iris_plot.png", show_shapes=True, show_layer_names=True + ) + + # Saving model in h5 for Ploting Nuetron visual + model.save("keras_model_on_iris.keras") + + # predict example for index 0 + print("Predict value at index 0:") + predictions = model.predict([X_test]) + print("The predicted number at index 0 is", np.argmax(predictions[0])) + + return model + + +if __name__ == "__main__": + run() diff --git a/examples/TensorFlow/Keras/IRIS/keras_model.py b/examples/TensorFlow/Keras/IRIS/keras_model.py deleted file mode 100644 index dcdc89e7..00000000 --- a/examples/TensorFlow/Keras/IRIS/keras_model.py +++ /dev/null @@ -1,91 +0,0 @@ -import sys -import tensorflow as tf - -# tf.__version__ - -from keras.layers import Dense -from keras.utils import plot_model -from keras.models import Sequential - -# from keras_visualizer import visualizer -from keras import layers - -import matplotlib.pyplot as plt -import numpy as np - -import matplotlib.pyplot as plt -import numpy as np - -from sklearn.datasets import load_iris -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler - -# Controlling randomness, to facilitate testing/reproducibility -# based on https://keras.io/examples/keras_recipes/reproducibility_recipes/ -from keras.utils import set_random_seed - -set_random_seed(1234) -tf.config.experimental.enable_op_determinism() - -print("Loading data") -iris = load_iris() - -X, y = iris.data, iris.target - -X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.2, random_state=42 -) - -# standardize the data -scaler = StandardScaler() -X_train = scaler.fit_transform(X_train) -X_test = scaler.fit_transform(X_test) - - -# Build Model -print("Building the Model") -model = tf.keras.models.Sequential() -model.add( - tf.keras.layers.Dense(32, activation=tf.nn.relu, input_dim=4, name="first_layer") -) # hidden layers -model.add(tf.keras.layers.Dense(64, activation=tf.nn.relu, name="second_layer")) -model.add(tf.keras.layers.Dense(64, activation=tf.nn.relu, name="third_layer")) -model.add( - tf.keras.layers.Dense(3, activation=tf.nn.softmax, name="fourth_layer") -) # output layer - -# Compile Model -print("Compiling the model") -model.compile( - optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"] -) - -# train the model -print("Training the Model") -model.fit(X_train, y_train, epochs=3) - - -# check if the model actually generalize the data. -print("Accuracy of our model is:") -val_loss, val_acc = model.evaluate(X_test, y_test) -print(val_loss, val_acc) - - -# Print summary -model.summary() - -# plot the model -print("Plotting the model") -plot_model( - model, to_file="model_on_iris_plot.png", show_shapes=True, show_layer_names=True -) - - -# Saving model in h5 for Ploting Nuetron visual -model.save("keras_model_on_iris.keras") - - -# predict example for index 0 -print("Predict value at index 0:") -predictions = model.predict([X_test]) -print("The predicted number at index 0 is", np.argmax(predictions[0])) diff --git a/examples/TensorFlow/Keras/IRIS/keras_to_MDF.1.png b/examples/TensorFlow/Keras/IRIS/keras_to_MDF.1.png index 210339df..46d37065 100644 Binary files a/examples/TensorFlow/Keras/IRIS/keras_to_MDF.1.png and b/examples/TensorFlow/Keras/IRIS/keras_to_MDF.1.png differ diff --git a/examples/TensorFlow/Keras/IRIS/keras_to_MDF.json b/examples/TensorFlow/Keras/IRIS/keras_to_MDF.json index bd7e964f..c20f3d8e 100644 --- a/examples/TensorFlow/Keras/IRIS/keras_to_MDF.json +++ b/examples/TensorFlow/Keras/IRIS/keras_to_MDF.json @@ -1,7 +1,7 @@ { "Sequential": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.11", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "Sequential_graph": { "nodes": { @@ -236,7 +236,7 @@ 0.016803866252303123, 0.05345309525728226, 0.2977215647697449, - 0.2879030406475067, + 0.2879030704498291, 0.266394704580307 ], [ @@ -275,7 +275,7 @@ ], [ 0.1601700782775879, - 0.3706554174423218, + 0.3706553876399994, 0.01457951869815588, -0.3413405418395996, -0.3211863338947296, @@ -305,7 +305,7 @@ 0.33330732583999634, -0.052887436002492905, 0.1207023337483406, - -0.03572190925478935 + -0.035721905529499054 ], [ -0.17111125588417053, @@ -332,8 +332,8 @@ 0.33152857422828674, 0.1661553978919983, -0.2442578226327896, - -0.0304008349776268, - -0.11865990608930588, + -0.0304008387029171, + -0.11865989863872528, 0.16890159249305725, -0.24666498601436615, 0.2965058386325836, @@ -345,25 +345,25 @@ }, "bias": { "value": [ - 0.011731109581887722, + 0.011731108650565147, -0.010842712596058846, -0.010995130054652691, - -0.0003828618791885674, - -0.011633925139904022, + -0.00038286257768049836, + -0.011633927002549171, -0.01093180663883686, 0.010172368958592415, - 0.008381562307476997, - -0.00609193928539753, + 0.008381561376154423, + -0.006091940216720104, -0.010678180493414402, -0.01128037367016077, - 0.011846471577882767, + 0.011846472509205341, -0.009920035488903522, -0.0021539812441915274, - -0.0112261101603508, - 0.0021782119292765856, + -0.011226111091673374, + 0.0021782121621072292, -0.0032380232587456703, - 0.010699188336730003, - 0.01058390736579895, + 0.010699187405407429, + 0.0105839092284441, 0.011204266920685768, -0.004481090698391199, 0.005531076807528734, @@ -371,12 +371,12 @@ 0.011576894670724869, -0.01145396288484335, -0.008559968322515488, - 0.011603811755776405, - -0.0057199266739189625, + 0.01160381082445383, + -0.005719926208257675, 0.006414022762328386, - 0.0028886247891932726, + 0.00288862525485456, 0.010425901040434837, - -0.0024574401322752237 + -0.00245743989944458 ] }, "linear": { @@ -419,7 +419,7 @@ -0.06301043182611465, -0.04849974066019058, 0.10950469225645065, - -0.04075626656413078, + -0.04075627028942108, 0.20764896273612976, 0.14600086212158203, -0.2555709183216095, @@ -463,7 +463,7 @@ -0.16370528936386108, 0.18346945941448212, 0.20399557054042816, - -0.1144314855337143 + -0.1144314780831337 ], [ 0.13290926814079285, @@ -480,7 +480,7 @@ 0.10864748805761337, 0.23852503299713135, -0.21956175565719604, - -0.0004136184579692781, + -0.00041361869079992175, 0.12997660040855408, -0.17875151336193085, 0.24640774726867676, @@ -497,7 +497,7 @@ 0.08143285661935806, -0.10681308805942535, 0.009092679247260094, - 2.6435242034494877e-05, + 2.6435183826833963e-05, 0.1955804079771042, -0.1414516270160675, 0.17286065220832825, @@ -511,7 +511,7 @@ -0.0404694527387619, 0.10921937227249146, 0.21699602901935577, - 0.05308510735630989, + 0.05308510363101959, -0.055551476776599884, 0.203565776348114, -0.17767353355884552, @@ -536,7 +536,7 @@ 0.18963824212551117, 0.042993493378162384, -0.1593853086233139, - -0.11226361989974976, + -0.11226362735033035, 0.04111243411898613, -0.19495347142219543, -0.19569510221481323, @@ -551,7 +551,7 @@ 0.23431581258773804, 0.12049762159585953, -0.09232243150472641, - 0.05769813060760498, + 0.057698123157024384, -0.05231788381934166, -0.0146090779453516, 0.18564572930335999, @@ -561,7 +561,7 @@ 0.047633469104766846, -0.00831644982099533, -0.22667764127254486, - 0.00032215070677921176, + 0.00032215029932558537, 0.050024885684251785, 0.14504235982894897, -0.07484065741300583, @@ -575,7 +575,7 @@ -0.24052751064300537, -0.05799839273095131, 0.22917531430721283, - 0.124468132853508, + 0.1244681179523468, 0.22250178456306458, 0.13756760954856873, -0.22552308440208435, @@ -584,7 +584,7 @@ -0.23633337020874023, 0.14024178683757782, 0.16781704127788544, - 0.012206395156681538, + 0.012206394225358963, 0.22221656143665314, -0.07576917111873627, -0.02778514288365841, @@ -669,7 +669,7 @@ -0.021549489349126816, 0.1409248560667038, -0.11154264956712723, - 0.11172010004520416, + 0.11172010749578476, -0.05457177758216858, -0.13010060787200928, 0.07673892378807068, @@ -757,7 +757,7 @@ -0.2263766974210739, -0.1252206563949585, -0.16222184896469116, - -0.001130665186792612, + -0.0011306654196232557, 0.1609678715467453, 0.13945303857326508, 0.0225210003554821, @@ -805,7 +805,7 @@ -0.15389883518218994, 0.13958740234375, 0.017156342044472694, - 0.18807651102542877, + 0.18807652592658997, 0.026984425261616707, -0.022655759006738663, 0.0502847395837307, @@ -828,7 +828,7 @@ -0.17486676573753357, 0.007183203473687172, 0.16075164079666138, - 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-0.03140052407979965, + -0.03140052780508995, -0.15922200679779053, 0.12175433337688446, -0.20692025125026703, @@ -960,12 +960,12 @@ -0.10872390121221542, -0.16201797127723694, -0.129658505320549, - -0.020679274573922157, + -0.020679276436567307, 0.2186688780784607, -0.14016084372997284, 0.24376656115055084, -0.1874377727508545, - 0.01651960238814354, + 0.01651959866285324, 0.16384416818618774, -0.04198498651385307, 0.15475696325302124, @@ -974,7 +974,7 @@ 0.07524938136339188, -0.14980654418468475, 0.13355985283851624, - -0.0486624501645565, + -0.048662446439266205, -0.12872380018234253, 0.1897653192281723, 0.2548260986804962, @@ -1014,7 +1014,7 @@ -0.2009771168231964, -0.21945905685424805, -0.20482096076011658, - 0.0025802019517868757, + 0.002580201718956232, -0.10828737169504166, 0.19581030309200287, -0.050881750881671906, @@ -1023,13 +1023,13 @@ -0.011875331401824951, -0.2075451910495758, 0.1385238617658615, - 0.025365278124809265, + 0.025365279987454414, -0.2054339349269867, 0.06799770891666412, 0.13210774958133698, -0.07756450027227402, 0.1758442521095276, - -0.04724507033824921, + -0.047245074063539505, -0.12524265050888062, -0.15718138217926025, -0.16030073165893555, @@ -1050,7 +1050,7 @@ 0.09964962303638458, -0.0836825743317604, -0.138927161693573, - -0.09556829184293747, + -0.09556829929351807, 0.23673546314239502, 0.17377527058124542, 0.13483427464962006, @@ -1064,7 +1064,7 @@ -0.010334965772926807, -0.058261431753635406, -0.09404803812503815, - 0.01930932141840458, + 0.01930931955575943, -0.19266560673713684, -0.14185568690299988, -0.09697407484054565, @@ -1217,7 +1217,7 @@ -0.00204138970002532, -0.22597719728946686, 0.19069965183734894, - -0.05490046367049217, + -0.05490046739578247, -0.24261462688446045, -0.24642819166183472, 0.11519394814968109, @@ -1226,10 +1226,10 @@ 0.11925724893808365, -0.05027318745851517, 0.05888703092932701, - -0.052025701850652695, + -0.05202570557594299, 0.16135990619659424, -0.033539071679115295, - 0.010132801719009876, + 0.0101328045129776, 0.20125405490398407, 0.12675076723098755, -0.007958590984344482, @@ -1261,7 +1261,7 @@ 0.07843469083309174, 0.21286951005458832, 0.14340810477733612, - 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0.06704913079738617, + 0.06704912334680557, 0.12026380002498627, 0.2220211774110794, 0.13534514605998993 @@ -1555,7 +1555,7 @@ 0.08844269812107086, 0.1299598217010498, -0.17222510278224945, - -0.026671409606933594, + -0.026671407744288445, 0.22058743238449097, -0.12420913577079773, 0.116779625415802, @@ -1574,7 +1574,7 @@ 0.17547841370105743, 0.16924791038036346, -0.21415819227695465, - -0.05218646302819252, + -0.05218645930290222, 0.03734031692147255, 0.2209835648536682, 0.03793710470199585, @@ -1592,7 +1592,7 @@ -0.1786336898803711, -0.02096426486968994, -0.24047434329986572, - 0.06013716384768486, + 0.06013716012239456, -0.099703848361969, -0.12498237192630768, -0.23418104648590088, @@ -1609,7 +1609,7 @@ -0.0594521202147007, -0.11767581850290298, 0.18231721222400665, - 0.0028405403718352318, + 0.002840540837496519, -0.1367609053850174, -0.024994853883981705, -0.16793715953826904, @@ -1675,7 +1675,7 @@ 0.20086601376533508, -0.14382304251194, -0.028950970619916916, - -0.1902094930410385, + -0.19020947813987732, -0.10203535854816437, 0.12300492078065872, 0.12942668795585632, @@ -1703,11 +1703,11 @@ 0.10389889031648636, 0.10318142920732498, -0.12258977442979813, - 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0.14420101046562195 + 0.14420099556446075 ], [ -0.23970769345760345, @@ -7193,7 +7193,7 @@ ], [ -0.30223095417022705, - 0.01886676251888275, + 0.018866760656237602, 0.17003139853477478 ], [ @@ -7202,13 +7202,13 @@ -0.1497655063867569 ], [ - 0.06048506870865822, + 0.06048506125807762, 0.24500487744808197, -0.13554513454437256 ], [ - 0.053448501974344254, - -0.14035899937152863, + 0.053448498249053955, + -0.14035898447036743, 0.0694827288389206 ], [ @@ -7217,7 +7217,7 @@ -0.07105574756860733 ], [ - -0.12041544169187546, + -0.12041543424129486, -0.3046673834323883, 0.20069821178913116 ], @@ -7251,7 +7251,7 @@ "bias": { "value": [ 0.003933753818273544, - 0.005671352613717318, + 0.005671353079378605, -0.005814753472805023 ] }, diff --git a/examples/TensorFlow/Keras/IRIS/keras_to_MDF.png b/examples/TensorFlow/Keras/IRIS/keras_to_MDF.png index 16fefd28..983eee9b 100644 Binary files a/examples/TensorFlow/Keras/IRIS/keras_to_MDF.png and b/examples/TensorFlow/Keras/IRIS/keras_to_MDF.png differ diff --git a/examples/TensorFlow/Keras/IRIS/keras_to_MDF.py b/examples/TensorFlow/Keras/IRIS/keras_to_MDF.py index 8bcf420b..4ea4611d 100644 --- a/examples/TensorFlow/Keras/IRIS/keras_to_MDF.py +++ b/examples/TensorFlow/Keras/IRIS/keras_to_MDF.py @@ -11,7 +11,9 @@ from modelspec.utils import _val_info from modeci_mdf.execution_engine import EvaluableGraph -exec(open("./keras_model.py").read()) +from iris_keras_model import run + +model = run() # load the keras model # model = tf.keras.models.load_model("keras_model_on_iris.keras") diff --git a/examples/TensorFlow/Keras/IRIS/keras_to_MDF.yaml b/examples/TensorFlow/Keras/IRIS/keras_to_MDF.yaml index 03158435..401b1e64 100644 --- a/examples/TensorFlow/Keras/IRIS/keras_to_MDF.yaml +++ b/examples/TensorFlow/Keras/IRIS/keras_to_MDF.yaml @@ -1,6 +1,6 @@ Sequential: format: ModECI MDF v0.4 - 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0.005671353079378605 - -0.005814753472805023 linear: value: (Fourth_layer_in @ wgts) + bias diff --git a/examples/TensorFlow/Keras/IRIS/model_on_iris_plot.png b/examples/TensorFlow/Keras/IRIS/model_on_iris_plot.png index 9b0cde3d..5bfe195f 100644 Binary files a/examples/TensorFlow/Keras/IRIS/model_on_iris_plot.png and b/examples/TensorFlow/Keras/IRIS/model_on_iris_plot.png differ diff --git a/examples/TensorFlow/Keras/MNIST/README.md b/examples/TensorFlow/Keras/MNIST/README.md index c60aa68c..c68499be 100644 --- a/examples/TensorFlow/Keras/MNIST/README.md +++ b/examples/TensorFlow/Keras/MNIST/README.md @@ -42,7 +42,7 @@ The MNIST dataset has already been preprocessed and cleaned, which saves time an This model uses the Keras library to recognize handwritten digits from the MNIST dataset. The model is built using a Sequential model, with three dense layers and uses the Adam optimizer, sparse categorical crossentropy loss, and accuracy as evaluation metrics. -The model ([keras_model.py](keras_model.py)) is trained for 3 epochs and the final network is saved as [kr_N_model.h5](kr_N_model.h5). The model is then evaluated on the test data, where its accuracy is reported. The model predicts the number for the first test data index, and the actual number is shown using a plot. +The model ([keras_model.py](mnist_keras_model.py)) is trained for 3 epochs and the final network is saved as [kr_N_model.h5](kr_N_model.h5). The model is then evaluated on the test data, where its accuracy is reported. The model predicts the number for the first test data index, and the actual number is shown using a plot. ### Prerequisites diff --git a/examples/TensorFlow/Keras/MNIST/keras_model.py b/examples/TensorFlow/Keras/MNIST/keras_model.py deleted file mode 100644 index d035d29b..00000000 --- a/examples/TensorFlow/Keras/MNIST/keras_model.py +++ /dev/null @@ -1,86 +0,0 @@ -import sys -import tensorflow as tf - -# tf.__version__ - -from keras.layers import Dense -from keras.utils import plot_model -from keras.models import Sequential - -# from keras_visualizer import visualizer -from keras import layers - -import matplotlib.pyplot as plt -import numpy as np - -import matplotlib.pyplot as plt -import numpy as np - -# Controlling randomness, to facilitate testing/reproducibility -# based on https://keras.io/examples/keras_recipes/reproducibility_recipes/ -from keras.utils import set_random_seed - -set_random_seed(1234) -tf.config.experimental.enable_op_determinism() - -print("Loading data") -mnist = tf.keras.datasets.mnist # 28*28 images of handwritten digits (0-9) - -(x_train, y_train), (x_test, y_test) = mnist.load_data() - -# data Normalizations ( scalling numbers between 0 and 1) -print("Normalizing data") -x_train = tf.keras.utils.normalize(x_train, axis=1) -x_test = tf.keras.utils.normalize(x_test, axis=1) - -# Build Model -print("Building the Model") -model = tf.keras.models.Sequential() -model.add(tf.keras.layers.Flatten()) # input layer - -model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu)) # hidden layers -model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu)) -model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax)) # output layer - -# Compile Model -print("Compiling the model") -model.compile( - optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"] -) - -# train the model -print("Training the Model") -model.fit(x_train, y_train, epochs=3) - -# loadthe model above -# new_model = tf.keras.models.load_model("num_reader.model") - - -# check if the model actually generalize the data. -print("Accuracy of our model is:") -val_loss, val_acc = model.evaluate(x_test, y_test) -print(val_loss, val_acc) - -# Print summary -model.summary() - -# plot the model -print("Plotting the model") -plot_model(model, to_file="model_plot.png", show_shapes=True, show_layer_names=True) - - -# Saving model in h5 for Ploting Nuetron visual -model.save("kr_N_model.keras") - - -# predict example for index 0 -print("Predict value at index 0:") -predictions = model.predict([x_test]) -print("The predicted number at index 0 is", np.argmax(predictions[0])) - -# print the actual value at that index -print("The actually number at index zero is (see the image below):") -plt.imshow(x_test[0]) - -if not "-nogui" in sys.argv: - plt.show() diff --git a/examples/TensorFlow/Keras/MNIST/keras_to_MDF.1.png b/examples/TensorFlow/Keras/MNIST/keras_to_MDF.1.png index 8c6cbabb..b2d74c6a 100644 Binary files a/examples/TensorFlow/Keras/MNIST/keras_to_MDF.1.png and b/examples/TensorFlow/Keras/MNIST/keras_to_MDF.1.png differ diff --git a/examples/TensorFlow/Keras/MNIST/keras_to_MDF.json b/examples/TensorFlow/Keras/MNIST/keras_to_MDF.json index df2d2477..f236a0d3 100644 --- a/examples/TensorFlow/Keras/MNIST/keras_to_MDF.json +++ b/examples/TensorFlow/Keras/MNIST/keras_to_MDF.json @@ -1,7 +1,7 @@ { "Sequential": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.11", + "generating_application": "Python modeci-mdf v0.4.13", "graphs": { "Sequential_graph": { "nodes": { @@ -18444,523 +18444,523 @@ 0.013358339667320251 ], [ - -0.0020416092593222857, - -0.05779494345188141, + -0.0029968719463795424, + -0.055856991559267044, -0.02310975268483162, - 0.062086157500743866, + 0.0701138898730278, -0.013045690953731537, - 0.03852328285574913, - -0.09638043493032455, - 0.07738926261663437, - 0.09516232460737228, - -0.03334152698516846, - -0.013168858364224434, - 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a/examples/TensorFlow/Keras/MNIST/keras_to_MDF.py +++ b/examples/TensorFlow/Keras/MNIST/keras_to_MDF.py @@ -7,7 +7,9 @@ from modelspec.utils import _val_info from modeci_mdf.execution_engine import EvaluableGraph -exec(open("./keras_model.py").read()) +from mnist_keras_model import run + +model = run() # load the keras model # model = tf.keras.models.load_model("kr_N_model.keras") diff --git a/examples/TensorFlow/Keras/MNIST/keras_to_MDF.yaml b/examples/TensorFlow/Keras/MNIST/keras_to_MDF.yaml index 19b82bde..ae8af52c 100644 --- a/examples/TensorFlow/Keras/MNIST/keras_to_MDF.yaml +++ b/examples/TensorFlow/Keras/MNIST/keras_to_MDF.yaml @@ -1,6 +1,6 @@ Sequential: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.11 + generating_application: Python modeci-mdf v0.4.13 graphs: Sequential_graph: nodes: @@ -17244,517 +17244,517 @@ Sequential: - 0.023861080408096313 - 0.05859772115945816 - 0.013358339667320251 - - - -0.0020416092593222857 - - -0.05779494345188141 + - - 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linear: value: (Dense_2_in @ wgts) + bias output_ports: diff --git a/examples/TensorFlow/Keras/MNIST/mnist_keras_model.py b/examples/TensorFlow/Keras/MNIST/mnist_keras_model.py new file mode 100644 index 00000000..b561a77a --- /dev/null +++ b/examples/TensorFlow/Keras/MNIST/mnist_keras_model.py @@ -0,0 +1,92 @@ +import sys +import tensorflow as tf + +# tf.__version__ + +from keras.layers import Dense +from keras.utils import plot_model +from keras.models import Sequential + +# from keras_visualizer import visualizer +from keras import layers + +import matplotlib.pyplot as plt +import numpy as np + +import matplotlib.pyplot as plt +import numpy as np + +# Controlling randomness, to facilitate testing/reproducibility +# based on https://keras.io/examples/keras_recipes/reproducibility_recipes/ +from keras.utils import set_random_seed + + +def run(): + + set_random_seed(1234) + tf.config.experimental.enable_op_determinism() + + print("Loading data") + mnist = tf.keras.datasets.mnist # 28*28 images of handwritten digits (0-9) + + (x_train, y_train), (x_test, y_test) = mnist.load_data() + + # data Normalizations ( scalling numbers between 0 and 1) + print("Normalizing data") + x_train = tf.keras.utils.normalize(x_train, axis=1) + x_test = tf.keras.utils.normalize(x_test, axis=1) + + # Build Model + print("Building the Model") + model = tf.keras.models.Sequential() + model.add(tf.keras.layers.Flatten()) # input layer + + model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu)) # hidden layers + model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu)) + model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax)) # output layer + + # Compile Model + print("Compiling the model") + model.compile( + optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"] + ) + + # train the model + print("Training the Model") + model.fit(x_train, y_train, epochs=3) + + # load the model above + # new_model = tf.keras.models.load_model("num_reader.model") + + # check if the model actually generalize the data. + print("Accuracy of our model is:") + val_loss, val_acc = model.evaluate(x_test, y_test) + print(val_loss, val_acc) + + # Print summary + model.summary() + + # plot the model + print("Plotting the model") + plot_model(model, to_file="model_plot.png", show_shapes=True, show_layer_names=True) + + # Saving model in h5 for Ploting Nuetron visual + model.save("kr_N_model.keras") + + # predict example for index 0 + print("Predict value at index 0:") + predictions = model.predict([x_test]) + print("The predicted number at index 0 is", np.argmax(predictions[0])) + + # print the actual value at that index + print("The actually number at index zero is (see the image below):") + plt.imshow(x_test[0]) + + if not "-nogui" in sys.argv: + plt.show() + + return model + + +if __name__ == "__main__": + run() diff --git a/examples/TensorFlow/Keras/MNIST/model_plot.png b/examples/TensorFlow/Keras/MNIST/model_plot.png index f4142fab..445bbb4c 100644 Binary files a/examples/TensorFlow/Keras/MNIST/model_plot.png and b/examples/TensorFlow/Keras/MNIST/model_plot.png differ diff --git a/examples/TensorFlow/Keras/regenerate.sh b/examples/TensorFlow/Keras/regenerate.sh index c6d1cff3..df027603 100755 --- a/examples/TensorFlow/Keras/regenerate.sh +++ b/examples/TensorFlow/Keras/regenerate.sh @@ -5,12 +5,12 @@ set -ex cd MNIST -python keras_model.py -nogui +python mnist_keras_model.py -nogui python keras_to_MDF.py -nogui cd ../IRIS -python keras_model.py +python iris_keras_model.py python keras_to_MDF.py -nogui diff --git a/pyproject.toml b/pyproject.toml index 5d169c21..25402296 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,17 +4,210 @@ requires = [ "wheel", "toml", ] - build-backend = "setuptools.build_meta" +[project] +name = "modeci-mdf" +dynamic = ["version"] +description = "ModECI (Model Exchange and Convergence Initiative) Model Description Format" +readme = "README.md" +requires-python = ">=3.9" +license = {text = "LGPLv3"} +authors = [ + {name = "ModECI contributors", email = "p.gleeson@gmail.com"}, +] +maintainers = [ + {name = "ModECI contributors", email = "p.gleeson@gmail.com"}, +] +keywords = ["scientific computing", "modeling", "neuroscience"] +classifiers = [ + "Intended Audience :: Science/Research", + "License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)", + "Natural Language :: English", + "Operating System :: OS Independent", + "Programming Language :: Python :: 3.8", + "Topic :: Scientific/Engineering", + "Intended Audience :: Science/Research", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Topic :: Scientific/Engineering", + "Topic :: Software Development", + "Typing :: Typed", +] +dependencies = [ + "graph_scheduler>=1.1.1", + "numpy", + "matplotlib", + "graphviz", + "h5py", + "onnxruntime", + "onnx", + "skl2onnx", + "attrs>=21.1.0", + "cattrs", + "modelspec<0.4,>=0.3.0", + "glom", +] + +[project.urls] +Homepage = "https://www.modeci.org" +Repository = "https://github.com/ModECI/MDF" +Documentation = "https://mdf.readthedocs.io" + +[project.optional-dependencies] +psyneulink = [ + "psyneulink>=0.15.3", +] + +neuroml = [ + "pyNeuroML>=1.0.10", + "neuromllite>=0.5.6", + "PyNN>=0.10.1", +] + +neuroml_simulators = [ + "neuron", +] + +tensorflow = [ + "tensorflow>=2.16.0", + "keras_visualizer", + "pydot", +] + +test = [ + "pytest", + "pytest-benchmark", + "pytest-mock", + "setuptools", +] + +optional = [ + "torchviz", + "netron", + "torch>=1.11.0", + "torchvision", + "h5py", +] + +all_except_psyneulink = [ + "modeci-mdf[tensorflow]", + "modeci-mdf[optional]", + "modeci-mdf[neuroml]", +] + +all = [ + "modeci-mdf[all_except_psyneulink]", + "modeci-mdf[psyneulink]", + "modeci-mdf[neuroml_simulators]", +] + +# Development dependency groups (PEP 735) +[dependency-groups] +dev = [ + "pre-commit", + "modeci-mdf[test]", +] + +docs = [ + "Sphinx", + "recommonmark>=0.5.0", + "nbsphinx", + "sphinx_copybutton", + "sphinx-rtd-theme", + "myst_parser", + "sphinx_markdown_tables", + "sphinx-autoapi", + "pytorch-sphinx-theme==0.0.19", + "sphinxcontrib-versioning", + "Jinja2<3.1", +] + +[tool.setuptools] +package-dir = {"" = "src"} +include-package-data = true + +[tool.setuptools.packages.find] +where = ["src"] +exclude = ["tests", "examples"] + +[tool.setuptools.package-data] +"*" = ["syn_definitions.xml"] + +[tool.setuptools.dynamic] +version = {attr = "modeci_mdf.__version__"} + #[tool.setuptools_scm] #write_to = "src/modeci_mdf/version.py" [tool.pytest.ini_options] - markers = [ - "coremdf: marks tests which are core to the MDF specification", - "actr: marks tests which require ACT-R to be installed.", - "pytorch: marks test which require PyTorch to be installed.')", - "psyneulink: marks tests which require PsyNueLink to be installed.", - "neuroml: marks tests which require NeuroML to be installed." - ] +junit_family = "xunit2" +addopts = "--benchmark-disable -Wd --strict-markers" +xfail_strict = true +testpaths = ["tests"] +required_plugins = ["pytest-benchmark"] +markers = [ + "coremdf: marks tests which are core to the MDF specification", + "actr: marks tests which require ACT-R to be installed.", + "pytorch: marks test which require PyTorch to be installed.')", + "psyneulink: marks tests which require PsyNueLink to be installed.", + "neuroml: marks tests which require NeuroML to be installed.", + "tensorflow: marks tests which require TensorFlow to be installed.", +] + +[tool.isort] +profile = "black" +multi_line_output = 3 + +[tool.flake8] +max-line-length = 80 +max-complexity = 13 +select = ["C", "E", "F", "W", "B", "B9", "T"] +ignore = ["E203", "E231", "E501", "E722", "W503", "B950"] +per-file-ignores = [ + "tests/*:T", + "examples/*:T", + "notebooks/*:T", + "docs/*:T", + "scripts/*:T", + "setup_helpers.py:C901", +] + +[tool.mypy] +warn_unused_configs = true +python_version = "3.6" +files = ["src"] +disallow_any_generics = true +disallow_subclassing_any = true +disallow_untyped_calls = true +disallow_untyped_defs = true +disallow_incomplete_defs = true +check_untyped_defs = true +disallow_untyped_decorators = true +no_implicit_optional = true +warn_redundant_casts = true +warn_unused_ignores = true +warn_return_any = true +no_implicit_reexport = true +strict_equality = true + +[tool.check-manifest] +ignore = [ + ".all-contributorsrc", + ".pre-commit-config.yaml", + ".readthedocs.yml", + "examples/**", + "notebooks/**", + "docs/**", + "scripts/**", + "dev-requirements.txt", + "CONTRIBUTING.md", + "*.html", + "src/modeci_mdf/version.py", + "tests/.pytest_cache/**", +] diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index 2f489c2e..00000000 --- a/setup.cfg +++ /dev/null @@ -1,182 +0,0 @@ -[metadata] -name = modeci-mdf -version = attr: modeci_mdf.__version__ -description = ModECI (Model Exchange and Convergence Initiative) Model Description Format -long_description = file: README.md -long_description_content_type = text/markdown -url = https://www.modeci.org -author = ModECI contributors -author_email = p.gleeson@gmail.com -maintainer = ModECI contributors -maintainer_email = p.gleeson@gmail.com -license = LGPLv3 -license_file = LICENSE -classifiers = - Intended Audience :: Science/Research - License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3) - Natural Language :: English - Operating System :: OS Independent - Programming Language :: Python :: 3.8 - Topic :: Scientific/Engineering - Intended Audience :: Science/Research - Programming Language :: Python - Programming Language :: Python :: 3 - Programming Language :: Python :: 3 :: Only - Programming Language :: Python :: 3.8 - Programming Language :: Python :: 3.9 - Programming Language :: Python :: 3.10 - Programming Language :: Python :: 3.11 - Topic :: Scientific/Engineering - Topic :: Software Development - Typing :: Typed - -[options] -packages = find: -install_requires = - typing_compat;python_version<'3.8' - dataclasses;python_version<'3.7' - graph_scheduler>=1.1.1 - numpy - matplotlib - graphviz - h5py - onnxruntime - onnx<=1.17.0 # TEMP test pin onnx.. - skl2onnx - attrs>=21.1.0 - cattrs - modelspec<0.4,>=0.3.0 - glom - -python_requires = >=3.7 -include_package_data = True -package_dir = - =src - -[options.extras_require] -psyneulink = - psyneulink>=0.15.3 - -neuroml = - pyNeuroML>=1.0.10 - neuromllite>=0.5.6 - PyNN>=0.10.1 - -neuroml_simulators = - neuron - - -tensorflow = - tensorflow>=2.11.0 - keras_visualizer - pydot - -test = - pytest - pytest-benchmark - pytest-mock - typing_extensions;python_version<'3.8' - setuptools - -optional = - Sphinx - recommonmark>=0.5.0 - nbsphinx - sphinx_copybutton - sphinx-rtd-theme - myst_parser - sphinx_markdown_tables - sphinx-autoapi - pytorch-sphinx-theme==0.0.19 - sphinxcontrib-versioning - Jinja2<3.1 - torchviz - netron - torch>=1.11.0 - torchvision<=0.20 # it's actually psyneulink limiting our version of torch, but the latest torchvision fails with earlier torch... - h5py - -all_except_psyneulink = - modeci-mdf[tensorflow] - modeci-mdf[optional] - modeci-mdf[neuroml] - - -dev = - pre-commit - modeci-mdf[test] - -all = - modeci-mdf[all_except_psyneulink] - modeci-mdf[psyneulink] - modeci-mdf[neuroml_simulators] - modeci-mdf[dev] - -[options.packages.find] -where = src -exclude = - tests - examples - - -[options.package_data] -* = syn_definitions.xml - -[tool:pytest] -junit_family = xunit2 -addopts = --benchmark-disable -Wd --strict-markers -xfail_strict = True -testpaths = - tests -required_plugins = - pytest-benchmark - -[tool:isort] -profile = black -multi_line_output = 3 - -[flake8] -max-line-length = 80 -max-complexity = 13 -select = C, E, F, W, B, B9, T -ignore = E203, E231, E501, E722, W503, B950 -per-file-ignores = - tests/*: T - examples/*: T - notebooks/*: T - docs/*: T - scripts/*: T - setup_helpers.py: C901 - -[mypy] -warn_unused_configs = True -python_version = 3.6 -files = src -disallow_any_generics = True -disallow_subclassing_any = True -disallow_untyped_calls = True -disallow_untyped_defs = True -disallow_incomplete_defs = True -check_untyped_defs = True -disallow_untyped_decorators = True -no_implicit_optional = True -warn_redundant_casts = True -warn_unused_ignores = True -warn_return_any = True -no_implicit_reexport = True -strict_equality = True - -[check-manifest] -ignore = - .all-contributorsrc - .pre-commit-config.yaml - .readthedocs.yml - examples/** - notebooks/** - docs/** - scripts/** - dev-requirements.txt - CONTRIBUTING.md - *.html - src/modeci_mdf/version.py - tests/.pytest_cache/** diff --git a/src/modeci_mdf/__init__.py b/src/modeci_mdf/__init__.py index 9d4be402..45510701 100644 --- a/src/modeci_mdf/__init__.py +++ b/src/modeci_mdf/__init__.py @@ -13,4 +13,4 @@ MODECI_MDF_VERSION = "0.4" # Version of the Python module. -__version__ = "0.4.12" +__version__ = "0.4.13" diff --git a/src/modeci_mdf/interfaces/graphviz/exporter.py b/src/modeci_mdf/interfaces/graphviz/exporter.py index 9418c770..e34c32ac 100644 --- a/src/modeci_mdf/interfaces/graphviz/exporter.py +++ b/src/modeci_mdf/interfaces/graphviz/exporter.py @@ -283,7 +283,7 @@ def mdf_to_graphviz( if node.input_ports and len(node.input_ports) > 0: for ip in node.input_ports: additional = "" - if ip.shape is not None: + if ip.shape is not None and ip.shape != (): additional += "shape: %s, " % str(ip.shape) """if ip.reduce is not None: if ip.reduce != "overwrite": # since this is the default... @@ -487,9 +487,9 @@ def mdf_to_graphviz( format_output(op.id), match_in_expr(op.value, node), "(shape: %s)" % str(op.shape) - if op.shape is not None + if op.shape is not None and op.shape != () else "" - if level >= LEVEL_2 and op.shape is not None + if level >= LEVEL_2 and op.shape is not None and op.shape != () else "", ) diff --git a/src/modeci_mdf/interfaces/onnx/exporter.py b/src/modeci_mdf/interfaces/onnx/exporter.py index c7409a77..7f6f8bc8 100644 --- a/src/modeci_mdf/interfaces/onnx/exporter.py +++ b/src/modeci_mdf/interfaces/onnx/exporter.py @@ -49,7 +49,7 @@ def mdf_to_onnx(mdf_model): # There is still now programmatic way to determine the max supported ir_version from onnxruntime # Here is the issue: https://github.com/microsoft/onnxruntime/issues/14932 # We will have to continue this dumb hack for the time being. - make_model_kwargs = {} + make_model_kwargs = {"ir_version": 10} try: from packaging.version import Version, InvalidVersion diff --git a/src/modeci_mdf/interfaces/pytorch/importer.py b/src/modeci_mdf/interfaces/pytorch/importer.py index ec8a7981..c1766393 100644 --- a/src/modeci_mdf/interfaces/pytorch/importer.py +++ b/src/modeci_mdf/interfaces/pytorch/importer.py @@ -361,9 +361,9 @@ def torchnode_to_mdfnode( out_dtype = str(out_type.getElementType()) try: - shape = tuple(out_type.sizes()) if out_type.sizes() else None + shape = tuple(out_type.sizes()) if out_type.sizes() else () except RuntimeError: - shape = None + shape = () mdf_node.output_ports.append( OutputPort( @@ -387,9 +387,9 @@ def torchnode_to_mdfnode( inp_dtype = str(inp_type.getElementType()) try: - shape = tuple(inp_type.sizes()) if inp_type.sizes() else None + shape = tuple(inp_type.sizes()) if inp_type.sizes() else () except RuntimeError: - shape = None + shape = () mdf_node.input_ports.append( InputPort(id=ip_name, shape=shape, type=inp_dtype) diff --git a/src/modeci_mdf/mdf.py b/src/modeci_mdf/mdf.py index f3be7b75..327240be 100644 --- a/src/modeci_mdf/mdf.py +++ b/src/modeci_mdf/mdf.py @@ -108,7 +108,7 @@ class InputPort(MdfBase): id: str = field(validator=instance_of(str)) shape: Optional[Tuple[int, ...]] = field( validator=optional(instance_of(tuple)), - default=None, + default=(), converter=lambda x: make_tuple(x) if type(x) is str else x, ) type: Optional[str] = field(validator=optional(instance_of(str)), default=None) @@ -133,7 +133,7 @@ class OutputPort(MdfBase): value: Optional[str] = field(validator=optional(instance_of(str)), default=None) shape: Optional[Tuple[int, ...]] = field( validator=optional(instance_of(tuple)), - default=None, + default=(), converter=lambda x: make_tuple(x) if type(x) is str else x, ) type: Optional[str] = field(validator=optional(instance_of(str)), default=None) diff --git a/test_all.sh b/test_all.sh index 4d09a4d1..41816de5 100755 --- a/test_all.sh +++ b/test_all.sh @@ -1,7 +1,7 @@ #!/bin/bash set -ex -pip install .[all] +pip install .[all] --group "dev" # Note this: # 1) runs examples to regenerate yaml/json... diff --git a/tests/interfaces/pytorch/test_import.py b/tests/interfaces/pytorch/test_import.py index b3f899cf..ef54f7e3 100644 --- a/tests/interfaces/pytorch/test_import.py +++ b/tests/interfaces/pytorch/test_import.py @@ -119,7 +119,7 @@ def list_model_fns(module): "maxvit_t": "MaxViT is failing because we are trying to call ast.parse on a string that is not valid python." " Need to handle string arguments requried by einops.", "resnet101": "Resnet101 is failing to match currently.", - "vit_": "ViT models are failing because PyTorch cant convert to ONNX the unflatten op.", + # "vit_": "ViT models are failing because PyTorch cant convert to ONNX the unflatten op.", } pytest_params = [] diff --git a/tests/test_examples.py b/tests/test_examples.py index 1e30aa43..1dce7371 100644 --- a/tests/test_examples.py +++ b/tests/test_examples.py @@ -24,11 +24,14 @@ example_pnl_scripts = { Path(f) for f in glob.glob("examples/PsyNeuLink/**/*.py", recursive=True) } +example_tf_scripts = { + Path(f) for f in glob.glob("examples/TensorFlow/**/*.py", recursive=True) +} example_exclusion_strings = [ ".reconstructed.py", "generate_json_and_scripts.py", "pytorch_ddm.py", - "keras_model.py", + "iris_keras_model.py", ] # Filter any excluded example scripts @@ -98,8 +101,9 @@ def test_example(script, example_tmp_dir, additional_args): dir_path = os.path.dirname(os.path.realpath(full_script_path)) os.chdir(dir_path) - # If this is one of the example/MDF scripts, we need to append example/MDF to sys.path - if Path(script) in example_mdf_scripts: + # If this is one of the example/MDF scripts, we need to append example/MDF to sys.path. + # Do the same for tensorflow examples since they also have local imports. + if Path(script) in example_mdf_scripts or Path(script) in example_tf_scripts: sys.path.append(dir_path) print(f"Running script {full_script_path} in working dir {os.getcwd()}")

metadataUnion[Any, NoneType]Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values.
node_specificUnion[Condition, NoneType]Optional A dictionary mapping nodes to any non-default run conditions
terminationUnion[Condition, NoneType]Optional A dictionary mapping time scales of model execution to conditions indicating when they end