From 8c3d8a2e61651811b88c075ab8938f6f152d5902 Mon Sep 17 00:00:00 2001 From: "Geisel Maren (CR/AME3)" Date: Wed, 4 Sep 2024 08:50:20 +0200 Subject: [PATCH 1/8] Add wrapper module for Modulus framework --- AUTHORS.rst | 1 + NOTICE | 1 + README.rst | 14 +- .../wrapper/data/heat-eq-inverse-data.npy | Bin 0 -> 2743520 bytes examples/wrapper/fdm_heat_equation.py | 150 ++ examples/wrapper/heat-equation-wrapper.ipynb | 1050 ++++++++++++++ ...rse-heat-equation-D-function-wrapper.ipynb | 793 ++++++++++ setup.cfg | 2 +- src/torchphysics/__init__.py | 1 + src/torchphysics/utils/callbacks.py | 15 +- src/torchphysics/wrapper/TPModulusWrapper.rst | 109 ++ src/torchphysics/wrapper/__init__.py | 7 + src/torchphysics/wrapper/geometry.py | 1280 +++++++++++++++++ src/torchphysics/wrapper/helper.py | 653 +++++++++ src/torchphysics/wrapper/model.py | 383 +++++ src/torchphysics/wrapper/nodes.py | 228 +++ src/torchphysics/wrapper/solver.py | 539 +++++++ .../tests/test_ParallelogramCylinder.py | 77 + .../wrapper/tests/test_TPGeometryWrapper.py | 269 ++++ src/torchphysics/wrapper/tests/test_model.py | 42 + .../wrapper/tests/test_wrapper.py | 147 ++ src/torchphysics/wrapper/wrapper.py | 361 +++++ 22 files changed, 6110 insertions(+), 12 deletions(-) create mode 100644 examples/wrapper/data/heat-eq-inverse-data.npy create mode 100644 examples/wrapper/fdm_heat_equation.py create mode 100644 examples/wrapper/heat-equation-wrapper.ipynb create mode 100644 examples/wrapper/inverse-heat-equation-D-function-wrapper.ipynb create mode 100644 src/torchphysics/wrapper/TPModulusWrapper.rst create mode 100644 src/torchphysics/wrapper/__init__.py create mode 100644 src/torchphysics/wrapper/geometry.py create mode 100644 src/torchphysics/wrapper/helper.py create mode 100644 src/torchphysics/wrapper/model.py create mode 100644 src/torchphysics/wrapper/nodes.py create mode 100644 src/torchphysics/wrapper/solver.py create mode 100644 src/torchphysics/wrapper/tests/test_ParallelogramCylinder.py create mode 100644 src/torchphysics/wrapper/tests/test_TPGeometryWrapper.py create mode 100644 src/torchphysics/wrapper/tests/test_model.py create mode 100644 src/torchphysics/wrapper/tests/test_wrapper.py create mode 100644 src/torchphysics/wrapper/wrapper.py diff --git a/AUTHORS.rst b/AUTHORS.rst index af934369..9c8005c9 100644 --- a/AUTHORS.rst +++ b/AUTHORS.rst @@ -5,3 +5,4 @@ Contributors * Nick Heilenkötter, nheilenkoetter * Tom Freudenberg, TomF98 * Daniel Kreuter, dkreuter +* Maren Geisel, mgei01 diff --git a/NOTICE b/NOTICE index 5b8ccbae..c06d9871 100644 --- a/NOTICE +++ b/NOTICE @@ -21,6 +21,7 @@ # Please keep the list sorted. Robert Bosch GmbH + Maren Geisel Felix Hildebrand Uwe Iben Daniel Christopher Kreuter diff --git a/README.rst b/README.rst index d5640d1f..27d102eb 100644 --- a/README.rst +++ b/README.rst @@ -26,7 +26,7 @@ TorchPhysics is build upon the machine learning library PyTorch_. Features ======== -The Goal of this library is to create a basic framework that can be used in many +The goal of this library is to create a basic framework that can be used in many different applications and with different deep learning methods. To this end, TorchPhysics aims at a: @@ -61,6 +61,14 @@ Some built-in features are: .. _Shapely: https://github.com/shapely/shapely .. _`PyTorch Lightning`: https://www.pytorchlightning.ai/ +Additional module: + +- TorchPhysics comes with a wrapper module for `NVIDIA Modulus`_. +This module serves as a bridge between the two frameworks and allows users to train TorchPhysics models with the Modulus training framework with minimal changes to their existing code. The additional installation of Modulus is required and documented in the `Wrapper Readme`_. + +.. _`NVIDIA Modulus`: https://developer.nvidia.com/modulus +.. _`Wrapper Readme`: https://github.com/boschresearch/torchphysics/blob/main/src/wrapper/TPModulusWrapper.rst + Getting Started =============== @@ -78,7 +86,7 @@ to have a look at the following sections: Installation ============ -TorchPhysics reqiueres the follwing dependencies to be installed: +TorchPhysics requires the following dependencies to be installed: - Python >= 3.8 - PyTorch_ >= 2.0.0 @@ -99,7 +107,7 @@ Or by git clone https://github.com/boschresearch/torchphysics cd path_to_torchphysics_folder - pip install .[all] + pip install -e . if you want to modify the code. diff --git a/examples/wrapper/data/heat-eq-inverse-data.npy b/examples/wrapper/data/heat-eq-inverse-data.npy new file mode 100644 index 0000000000000000000000000000000000000000..f75fe905f504f69eeef3f9269706e5460757bab1 GIT binary patch literal 2743520 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A normalization can improve convergence for large or small domains." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "model = tp.models.Sequential(\n", + " tp.models.NormalizationLayer(A_x*A_t*A_D),\n", + " tp.models.FCN(input_space=X*T*D, output_space=U, hidden=(50,50,50))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we define a condition which aims to minimze the mean squared error of the residual of the poisson equation. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def heat_residual(u, x, t, D):\n", + " return D*tp.utils.laplacian(u, x) - tp.utils.grad(u, t)\n", + "\n", + "pde_condition = tp.conditions.PINNCondition(module=model,\n", + " sampler=inner_sampler,\n", + " residual_fn=heat_residual,\n", + " name='pde_condition')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additionally, we add a boundary condition at the boundary of the domain:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def boundary_v_residual(u):\n", + " return u\n", + "\n", + "boundary_v_condition = tp.conditions.PINNCondition(module=model,\n", + " sampler=boundary_v_sampler,\n", + " residual_fn=boundary_v_residual,\n", + " name='boundary_condition')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The initial condition can be defined via a data function. Again, we minimize the mean squared error over the sampled points." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def f(x):\n", + " return torch.sin(math.pi/w*x[:, :1])*torch.sin(math.pi/h*x[:,1:])\n", + "\n", + "def initial_v_residual(u, f):\n", + " return u-f\n", + "\n", + "initial_v_condition = tp.conditions.PINNCondition(module=model,\n", + " sampler=initial_v_sampler,\n", + " residual_fn=initial_v_residual,\n", + " data_functions={'f': f},\n", + " name='initial_condition')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For comparison, we compute the solution via a finite difference scheme." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('..')\n", + "\n", + "from fdm_heat_equation import FDM, transform_to_points\n", + "\n", + "fdm_domain, fdm_time_domains, fdm_solution = FDM([0, w, 0, h], 2*[2e-1], [0,5], [0.1,0.2,0.4,0.6,0.8, 1.0], f)\n", + "fdm_inp, fdm_out = transform_to_points(fdm_domain, fdm_time_domains, fdm_solution, [0.1,0.2,0.4,0.6,0.8, 1.0], True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comparsion to measured or computed data can be performed via a DataCondition using data supplied via a PointsDataLoader." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "val_condition = tp.conditions.DataCondition(module=model,\n", + " dataloader=tp.utils.PointsDataLoader((fdm_inp, fdm_out), batch_size=80000),\n", + " norm='inf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Training in TorchPhysics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we optimize the conditions using a pytorch-lightning.LightningModule Solver and running the training. In the Solver, the training and validation conditions, as well as all optimizer options can be specified.\n", + "\n", + "The process of the training can be monitored with Tensorboard (e.g. tensorboard --logdir=lightning_logs --port=0). The losses are plotted under the tab \"SCALARS\" and the plots of the PlotterCallback can be found in the tab \"IMAGES\"." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", + "/home/gea3si/.conda/envs/mod_tp/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py:258: Found unsupported keys in the lr scheduler dict: {'gamma'}. HINT: remove them from the output of `configure_optimizers`.\n", + "\n", + " | Name | Type | Params | Mode \n", + "--------------------------------------------------------\n", + "0 | train_conditions | ModuleList | 5.4 K | train\n", + "1 | val_conditions | ModuleList | 5.4 K | train\n", + "--------------------------------------------------------\n", + "5.4 K Trainable params\n", + "0 Non-trainable params\n", + "5.4 K Total params\n", + "0.022 Total estimated model params size (MB)\n", + "20 Modules in train mode\n", + "0 Modules in eval mode\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "394bfad9438e419795450a4418d75731", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: | | 0/? [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "anim_sampler = tp.samplers.AnimationSampler(A_x, A_t, 100, n_points=400, data_for_other_variables={'D': 1.0})\n", + "anim = tp.utils.animate(model, lambda u: u[:, 0], anim_sampler, ani_speed=10)\n", + "anim[1].save('heat-eq.gif')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Training with the wrapper in Modulus" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we want to do the training in Modulus with the usage of the TPModulusWrapper.\n", + "\n", + "Remember that an additional Modulus installation is required!\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use a Modulus Fourier net architecture with the ModulusArchitectureWrapper." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/gea3si/.conda/envs/mod_tp/lib/python3.10/site-packages/modulus/sym/hydra/utils.py:150: UserWarning: \n", + "The version_base parameter is not specified.\n", + "Please specify a compatability version level, or None.\n", + "Will assume defaults for version 1.1\n", + " hydra.initialize(\n" + ] + } + ], + "source": [ + "model = tp.models.Sequential(\n", + " tp.models.NormalizationLayer(A_x*A_t*A_D), \n", + " tp.wrapper.ModulusArchitectureWrapper(input_space=X*T*D, output_space=U, arch_name='fourier', frequencies = ['axis',[0,1,2]])\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "New initialization of the same conditions as before.\n", + "\n", + "Since Modulus by default saves the resulting values of the condition objectives and validation in VTK-files, the training procedure will slow down, when the amount of validation data increase or if the frequency of validation (val_check_interval) or the plotter callback frequency (check_interval) is too high." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "pde_condition = tp.conditions.PINNCondition(module=model,\n", + " sampler=inner_sampler,\n", + " residual_fn=heat_residual,\n", + " name='pde_condition')\n", + "boundary_v_condition = tp.conditions.PINNCondition(module=model,\n", + " sampler=boundary_v_sampler,\n", + " residual_fn=boundary_v_residual,\n", + " name='boundary_condition')\n", + "initial_v_condition = tp.conditions.PINNCondition(module=model,\n", + " sampler=initial_v_sampler,\n", + " residual_fn=initial_v_residual,\n", + " data_functions={'f': f},\n", + " name='initial_condition')\n", + "val_condition = tp.conditions.DataCondition(module=model,\n", + " dataloader=tp.utils.PointsDataLoader((fdm_inp, fdm_out), batch_size=80000),\n", + " norm='inf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The learning rate monitor callback could be omitted here, as Modulus uses it by default.\n", + "\n", + "The process of the training can be monitored again with tensorboard, but in this example with --logdir=outputs_heat, as this is the name of the folder containing the Modulus output.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/gea3si/.conda/envs/mod_tp/lib/python3.10/site-packages/modulus/sym/hydra/utils.py:150: UserWarning: \n", + "The version_base parameter is not specified.\n", + "Please specify a compatability version level, or None.\n", + "Will assume defaults for version 1.1\n", + " hydra.initialize(\n", + "Setting JobRuntime:name=app\n", + "/home/gea3si/NewModTpWrapper/torchphysics/src/torchphysics/wrapper/solver.py:168: UserWarning: Modulus only supports RandomUniformSampler or Halton sequence. Using RandomUniformSampler instead.\n", + " warnings.warn(\"Modulus only supports RandomUniformSampler or Halton sequence. Using RandomUniformSampler instead.\")\n", + "/home/gea3si/.conda/envs/mod_tp/lib/python3.10/site-packages/modulus/sym/trainer.py:453: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n", + " self.scaler = GradScaler(enabled=enable_scaler)\n", + "attempting to restore from: /home/gea3si/NewModTpWrapper/torchphysics/examples/wrapper/outputs_heat\n", + "optimizer checkpoint not found\n", + "model model0.0.pth not found\n", + "[step: 0] saved constraint results to outputs_heat\n", + "[step: 0] record constraint batch time: 2.487e-01s\n", + "[step: 0] saved validator results to outputs_heat\n", + "[step: 0] record validators time: 4.094e+01s\n", + "[step: 0] saved inferencer results to outputs_heat\n", + "[step: 0] record inferencers time: 1.546e+00s\n", + "[step: 0] saved checkpoint to /home/gea3si/NewModTpWrapper/torchphysics/examples/wrapper/outputs_heat\n", + "[step: 0] loss: 3.529e+04\n", + "Attempting cuda graph building, this may take a bit...\n", + "[step: 40] saved validator results to outputs_heat\n", + "[step: 40] record validators time: 3.914e+01s\n", + "[step: 50] saved inferencer results to outputs_heat\n", + "[step: 50] record inferencers time: 1.449e+00s\n", + "[step: 80] saved validator results to outputs_heat\n", + "[step: 80] record validators time: 4.045e+01s\n", + "[step: 100] saved inferencer results to outputs_heat\n", + "[step: 100] record inferencers time: 1.440e+00s\n", + "[step: 100] loss: 8.275e+02, time/iteration: 1.250e+03 ms\n", + "[step: 120] saved validator results to outputs_heat\n", + "[step: 120] record validators time: 4.159e+01s\n", + "[step: 150] saved inferencer results to outputs_heat\n", + "[step: 150] record inferencers time: 1.436e+00s\n", + "[step: 160] saved validator results to outputs_heat\n", + "[step: 160] record validators time: 4.328e+01s\n", + "[step: 200] saved constraint results to outputs_heat\n", + "[step: 200] record constraint batch time: 2.162e-01s\n", + "[step: 200] saved validator results to outputs_heat\n", + "[step: 200] record validators time: 4.192e+01s\n", + "[step: 200] saved inferencer results to outputs_heat\n", + "[step: 200] record inferencers time: 1.478e+00s\n", + "[step: 200] loss: 1.945e+02, time/iteration: 1.620e+03 ms\n", + "[step: 200] reached maximum training steps, finished training!\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "decay_rate = 0.95\n", + "decay_steps = 15000\n", + "optim_setting = tp.solver.OptimizerSetting(torch.optim.Adam, lr=0.001,\n", + " scheduler_class=torch.optim.lr_scheduler.ExponentialLR,\n", + " scheduler_args={\"gamma\": decay_rate ** (1.0 / decay_steps)})\n", + "\n", + "solver = tp.solver.Solver([pde_condition,\n", + " boundary_v_condition,\n", + " initial_v_condition], \n", + " [val_condition],\n", + " optimizer_setting=optim_setting)\n", + "\n", + "run_name ='Heat_equation_paramD_Modulus'\n", + "\n", + "plot_sampler = tp.samplers.PlotSampler(plot_domain=A_x, n_points=1000,device='cpu', data_for_other_variables={'D':1.0, 't': 5})\n", + "callbacks = [ tp.utils.PlotterCallback(model=model,plot_function=lambda u: u[:,0],point_sampler=plot_sampler, \n", + " plot_type='contour_surface',log_name=run_name, check_interval=50), \n", + " \n", + " pl.callbacks.LearningRateMonitor(logging_interval='step')\n", + " ]\n", + "\n", + "\n", + "\n", + "trainer = pl.Trainer(devices=1, accelerator=\"gpu\",\n", + " max_steps = 200, \n", + " benchmark=True, \n", + " val_check_interval=40,\n", + " enable_checkpointing=False,\n", + " callbacks = callbacks\n", + " )\n", + "\n", + "\n", + "tp.wrapper.TPModulusWrapper(trainer,solver,confdir_name='conf_heat',outputdir_name='outputs_heat').train()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "anim_sampler = tp.samplers.AnimationSampler(A_x, A_t, 100, n_points=400, data_for_other_variables={'D': 1.0})\n", + "anim = tp.utils.animate(model, lambda u: u[:, 0], anim_sampler, ani_speed=10)\n", + "anim[1].save('heat-eq-wrapper.gif')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are addtional options for the training with the wrapper, e.g. use of aggregating function like LR Annealing or using spatial loss weighting, e.g. with the signed distance function. \n", + "For further information use the help function" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on class TPModulusWrapper in module torchphysics.wrapper.wrapper:\n", + "\n", + "class TPModulusWrapper(builtins.object)\n", + " | TPModulusWrapper(trainer, solver, outputdir_name='outputs', confdir_name='conf', keep_output=True, **kwargs)\n", + " | \n", + " | Training of a TorchPhysics trainer/solver with the Modulus wrapper.\n", + " | The wrapper is a bridge between TorchPhysics and Modulus. It uses\n", + " | the Modulus configuration and the Modulus solver to train the \n", + " | TorchPhysics solver/trainer/models.\n", + " | Loss weighting algorithms can be selected by choosing an\n", + " | aggregation function. The aggregation function can be selected by\n", + " | the parameter \"aggregator\" and additional arguments can be set by \n", + " | the parameter \"aggregator_args\".\n", + " | A learning rate scheduler can be selected by the parameter \n", + " | \"scheduler\" and additional arguments can be set by the parameter \n", + " | \"scheduler_args\".\n", + " | Pointwise weighting of the loss can be set by the parameter \n", + " | \"lambda_weighting\". The pointwise weighting can be a list of \n", + " | numbers or sympy expressions or the string 'sdf'. \n", + " | Notes \n", + " | ----- \n", + " | The following conventions are important for the usage of the \n", + " | wrapper:\n", + " | Possible spatial variables: x, y, z or x (multidimensional)\n", + " | Time variable: t\n", + " | Geometries: TP geometries (domains) should be defined in the \n", + " | space of x, y, z or x (multidimensional).\n", + " | A general product of domains as in TorchPhysics \n", + " | can not be implemented in Modulus, because the \n", + " | domain defines a spatial geometry that must have a\n", + " | sdf implementation which is not available in \n", + " | general for an arbitrary product domain.\n", + " | Cross products of domains and domain operations are\n", + " | generally allowed, but too complicated \n", + " | constructions should be avoided, e.g. a cross \n", + " | product of 3 translated intervals is not allowed or\n", + " | only isosceles triangle with axis of symmetry \n", + " | parallel to y-axis in cross product with an interval \n", + " | are supported.\n", + " | Shapely polygons in 2D are supported, but currently\n", + " | 3D geometries (TrimeshPolyhedron) defined in stl-\n", + " | files are only supported in Modulus in the container \n", + " | installation.\n", + " | Translation of primitive domains is supported, but \n", + " | not translation of domains resulting from domain \n", + " | operations like union, intersection, difference.\n", + " | \n", + " | Parameters\n", + " | ---------- \n", + " | trainer : pytorch_lightning.Trainer\n", + " | The Pytorch Lightning Trainer instance. \n", + " | Supported parameters of trainer instance:\n", + " | Modulus always uses GPU device if available. Trainer \n", + " | settings concerning GPU devices or cuda handling, e.g. \n", + " | 'accelerator' or 'devices', are not supported by this\n", + " | wrapper.\n", + " | Modulus automatically logs the training process with \n", + " | tensorboard. The tensorboard logs are saved in the output \n", + " | directory.\n", + " | All TorchPhysics callbacks are supported by the wrapper.\n", + " | The following Trainer parameters are supported by this \n", + " | wrapper:\n", + " | 'max_steps' : int, optional\n", + " | The maximum number of training steps. If not \n", + " | specified, the default value of Pytorch Lightning \n", + " | Trainer is used.\n", + " | 'val_check_interval' : int optional\n", + " | How often to check the validation set. Default is \n", + " | 1.0, meaning once per training epoch.\n", + " | 'log_every_n_steps' : int, optional\n", + " | How often to log within steps. Modulus/wrapper \n", + " | default is 50. \n", + " | Checkpoints, progress bar and model summary are \n", + " | automatically used by Modulus.\n", + " | \n", + " | solver: torchphysics.solvers.Solver\n", + " | The TorchPhysics solver instance.\n", + " | All parameters of the TorchPhysics solver are supported by the \n", + " | wrapper.\n", + " | outputdir_name : str, optional\n", + " | The name of the Modulus output directory, where the trained \n", + " | models, the optimization configuration, tensorboard files, etc. \n", + " | are saved. Default is 'outputs'.\n", + " | If the directory contains the results of a previous run and the\n", + " | configuration of a second call is mainly changed, there will be \n", + " | a conflict loading existing models or configuration leading to \n", + " | an error.\n", + " | If the directory contains the results of a previous run and the\n", + " | configuration of a second call is mainly unchanged, the new run \n", + " | will continue the previous run with the already trained Modulus \n", + " | models.\n", + " | If not desired or in error case, it is recommended to remove \n", + " | the content of the output directory before starting a new run.\n", + " | confdir_name : str, optional \n", + " | The name of a Modulus configuration directory, where initially \n", + " | a hydra configuration file is saved. It is overwritten on each \n", + " | call. Default is 'conf'. \n", + " | keep_output : bool, optional. Default is True.\n", + " | If True, the output directory is not deleted after the training\n", + " | process. Otherwise, it is deleted after the training process. \n", + " | **kwargs : optional\n", + " | Additional keyword arguments:\n", + " | \"lambda_weighting\": list[Union[int, float, sp.Basic]]=None \n", + " | The spatial pointwise weighting of the constraint. It \n", + " | is a list of numbers or sympy expressions or the string\n", + " | 'sdf'. \n", + " | If the list has more than one element, the length of \n", + " | the list and the order has to match the number of \n", + " | TorchPhysics conditions in the call \n", + " | tp.solver.Solver([condition_1,condition_2,...]).\n", + " | If the list has only one element, the same weighting is\n", + " | applied to all conditions.\n", + " | If it is a sympy expression, it has to be a function of \n", + " | the spatial coordinates x, y, z.\n", + " | If the TorchPhysics conditions contain weight \n", + " | definitions with the keyword \"weight\", these are \n", + " | additionally applied.\n", + " | For example,\n", + " | 'lambda_weighting=[\"sdf\"]' would apply a pointwise \n", + " | weighting of the loss by the signed distance function, \n", + " | but only for interior sampling, not boundary sampling.\n", + " | 'lambda_weighting=[100.0, 2.0] would apply a pointwise \n", + " | weighting of the loss by 100 to the first TorchPhysics \n", + " | condition and 2 to the second TorchPhysics condition.\n", + " | 'lambda_weighting=[2.0*sympy.Symbol('x')]' would apply \n", + " | a pointwise weighting to the loss of `2.0 * x`.\n", + " | \"aggregator\" : str = None\n", + " | The aggregation function for the loss. It is a string \n", + " | with the name of the aggregation function. Default is \n", + " | 'Sum'.\n", + " | Possible values are 'Sum', 'GradNorm', 'ResNorm, \n", + " | 'Homoscedastic','LRAnnealing','SoftAdapt','Relobralo'.\n", + " | \"aggregator_args\" : dict = None\n", + " | Additional arguments for the aggregation function. It \n", + " | is a dictionary with the argument names as keys and the\n", + " | argument values as values. Default is None.\n", + " | Possible arguments with its default values are, \n", + " | depending on the aggregator:\n", + " | GradNorm: \n", + " | alpha = 1.0\n", + " | ResNorm:\n", + " | alpha = 1.0\n", + " | LRAnnealing:\n", + " | update_freq = 1\n", + " | alpha = 0.01\n", + " | ref_key = None # Change to Union[None, str] when \n", + " | supported by hydra\n", + " | eps = 1e-8\n", + " | SoftAdapt:\n", + " | eps = 1e-8\n", + " | Relobralo:\n", + " | alpha = 0.95\n", + " | beta = 0.99\n", + " | tau = 1.0\n", + " | eps = 1e-8\n", + " | \"scheduler\" : str = None\n", + " | The learning rate scheduler. It is a string with the \n", + " | name of the scheduler. Default is constant learning \n", + " | rate. \n", + " | Possible values are 'ExponentialLR', \n", + " | 'CosineAnnealingLR' or 'CosineAnnealingWarmRestarts'. \n", + " | \"scheduler_args\" : dict = None\n", + " | Additional arguments for the scheduler. It is a \n", + " | dictionary with the argument names as keys and the \n", + " | argument values as values. Default is None.\n", + " | Possible arguments with its default values are, \n", + " | depending on the scheduler:\n", + " | ExponentialLR:\n", + " | gamma = 0.99998718\n", + " | TFExponentialLR:\n", + " | decay_rate = 0.95\n", + " | decay_steps = 1000\n", + " | CosineAnnealingLR:\n", + " | T_max = 1000\n", + " | eta_min = 0.0\n", + " | last_epoch= -1\n", + " | CosineAnnealingWarmRestarts:\n", + " | T_0 = 1000\n", + " | T_mult = 1\n", + " | eta_min = 0.0\n", + " | last_epoch = -1\n", + " | \n", + " | Methods defined here:\n", + " | \n", + " | __init__(self, trainer, solver, outputdir_name='outputs', confdir_name='conf', keep_output=True, **kwargs)\n", + " | Initialize self. See help(type(self)) for accurate signature.\n", + " | \n", + " | train(self, resume_from_ckpt=False)\n", + " | Call the training process of the Modulus solver. The training \n", + " | process is started with the Modulus configuration and the \n", + " | Modulus solver.\n", + " | The TorchPhysics models are trained and the function \n", + " | additionally returns trained parameters.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | resume_from_ckpt: bool, optional. Default is False.\n", + " | If True, the training is resumed from the Modulus checkpoint files.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Data descriptors defined here:\n", + " | \n", + " | __dict__\n", + " | dictionary for instance variables (if defined)\n", + " | \n", + " | __weakref__\n", + " | list of weak references to the object (if defined)\n", + "\n" + ] + } + ], + "source": [ + "help(tp.wrapper.TPModulusWrapper)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Training with the use of LR Annealing and the sdf function for spatial weighting." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/gea3si/.conda/envs/mod_tp/lib/python3.10/site-packages/modulus/sym/hydra/utils.py:150: UserWarning: \n", + "The version_base parameter is not specified.\n", + "Please specify a compatability version level, or None.\n", + "Will assume defaults for version 1.1\n", + " hydra.initialize(\n", + "Setting JobRuntime:name=app\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/gea3si/NewModTpWrapper/torchphysics/src/torchphysics/wrapper/solver.py:168: UserWarning: Modulus only supports RandomUniformSampler or Halton sequence. Using RandomUniformSampler instead.\n", + " warnings.warn(\"Modulus only supports RandomUniformSampler or Halton sequence. Using RandomUniformSampler instead.\")\n", + "/home/gea3si/.conda/envs/mod_tp/lib/python3.10/site-packages/modulus/sym/trainer.py:453: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n", + " self.scaler = GradScaler(enabled=enable_scaler)\n", + "attempting to restore from: /home/gea3si/NewModTpWrapper/torchphysics/examples/wrapper/outputs_heat\n", + "optimizer checkpoint not found\n", + "model model0.0.pth not found\n", + "[step: 0] saved constraint results to outputs_heat\n", + "[step: 0] record constraint batch time: 1.811e-01s\n", + "[step: 0] saved validator results to outputs_heat\n", + "[step: 0] record validators time: 4.099e+01s\n", + "[step: 0] saved inferencer results to outputs_heat\n", + "[step: 0] record inferencers time: 1.543e+00s\n", + "[step: 0] saved checkpoint to /home/gea3si/NewModTpWrapper/torchphysics/examples/wrapper/outputs_heat\n", + "[step: 0] loss: 5.744e+02\n", + "Attempting cuda graph building, this may take a bit...\n", + "[step: 40] saved validator results to outputs_heat\n", + "[step: 40] record validators time: 4.181e+01s\n", + "[step: 50] saved inferencer results to outputs_heat\n", + "[step: 50] record inferencers time: 1.395e+00s\n", + "[step: 80] saved validator results to outputs_heat\n", + "[step: 80] record validators time: 4.790e+01s\n", + "[step: 100] saved inferencer results to outputs_heat\n", + "[step: 100] record inferencers time: 1.517e+00s\n", + "[step: 100] loss: 2.471e+03, time/iteration: 1.583e+03 ms\n", + "[step: 120] saved validator results to outputs_heat\n", + "[step: 120] record validators time: 5.824e+01s\n", + "[step: 150] saved inferencer results to outputs_heat\n", + "[step: 150] record inferencers time: 1.448e+00s\n", + "[step: 160] saved validator results to outputs_heat\n", + "[step: 160] record validators time: 5.856e+01s\n", + "[step: 200] saved constraint results to outputs_heat\n", + "[step: 200] record constraint batch time: 2.374e-01s\n", + "[step: 200] saved validator results to outputs_heat\n", + "[step: 200] record validators time: 4.454e+01s\n", + "[step: 200] saved inferencer results to outputs_heat\n", + "[step: 200] record inferencers time: 1.528e+00s\n", + "[step: 200] loss: 7.796e+02, time/iteration: 2.192e+03 ms\n", + "[step: 200] reached maximum training steps, finished training!\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tp.wrapper.TPModulusWrapper(trainer,solver,lambda_weighting=[\"sdf\"],aggregator='LRAnnealing',confdir_name='conf_heat',outputdir_name='outputs_heat').train()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/wrapper/inverse-heat-equation-D-function-wrapper.ipynb b/examples/wrapper/inverse-heat-equation-D-function-wrapper.ipynb new file mode 100644 index 00000000..013e456d --- /dev/null +++ b/examples/wrapper/inverse-heat-equation-D-function-wrapper.ipynb @@ -0,0 +1,793 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Inverse heat equation with space dependent diffusion $D$\n", + "========================================================\n", + "In this example we want to solve an inverse problem where the parameter we are looking for is also a function.\n", + "We consider, $\\Omega = [0, 10] \\times [0, 10]$ and:\n", + "\\begin{align*}\n", + " \\text{div}(D(x)\\nabla u(x, t)) &= \\partial_t u(x, t) \\text{ in } \\Omega \\times [0, 5]\\\\\n", + " u(x, 0) &= 100\\sin(\\tfrac{\\pi}{10}x_1)\\sin(\\tfrac{\\pi}{10}x_2) \\text{ in } \\Omega \\\\\n", + " u(x, t) &= 0 \\text{ on } \\partial \\Omega \\times [0, 5]\n", + "\\end{align*} \n", + "The diffusion will be given by: $D(x)=5e^{-\\tfrac{1}{20}((x_1-3)^2 + (x_2-3)^2)}$. \n", + "\n", + "With an external program, we computed a FEM solution of this equation. The data can be found under the data folder.\n", + "This forward solution will be used as input data for the inverse problem.\n", + "\n", + "Since $D$ is a function too, we will use two neural networks. One for the temperature $u$ and for $D$. They will be connected over the PDE.\n", + "\n", + "This is the same example as in the folder examples/pinn, but in the second part of the notebook, the inverse problem is solved in Modulus with the wrapper module of TorchPhysics." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import torchphysics as tp\n", + "import pytorch_lightning as pl\n", + "import torch" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The spaces and domains are defined like always:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "X = tp.spaces.R2('x')\n", + "T = tp.spaces.R1('t')\n", + "\n", + "D = tp.spaces.R1('D')\n", + "U = tp.spaces.R1('u')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "w, h = 10, 10\n", + "t_0, t_end = 0, 5\n", + "\n", + "domain_x = tp.domains.Parallelogram(X, [0, 0], [w, 0], [0, h])\n", + "domain_t = tp.domains.Interval(T, t_0, t_end)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we now have two networks, like previously mentioned.\n", + "In the PDE both networks have to be evaluated, therefore we use the parallel evaluation of ``tp.models.Parallel``.\n", + "This objects will evaluate all models in parallel and pass the output to the corresponding condition." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "model_u = tp.models.Sequential(\n", + " tp.models.NormalizationLayer(domain_x*domain_t),\n", + " tp.models.FCN(input_space=X*T, output_space=U, hidden=(80,80,50,50)))\n", + "model_D = tp.models.Sequential(\n", + " tp.models.NormalizationLayer(domain_x),\n", + " tp.models.FCN(input_space=X, output_space=D, hidden=(80,80,50,50))) # D only has X as an Input\n", + "parallel_model = tp.models.Parallel(model_u, model_D)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we load the data of the forward problem. There are around, 90000 data points, we take only some random ones of them. This is chosen with ``num_of_data ``. \n", + "The data is in the form: [time, x-coordinates, temperature].\n", + "\n", + "After we have picked some points, we transform them to a ``Points``-object, so we can use the data in training." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "solution_data = np.load('data/heat-eq-inverse-data.npy')\n", + "num_of_data = 30000\n", + "solution_data = solution_data[np.random.choice(len(solution_data), num_of_data, replace=False), :].astype(np.float32)\n", + "solution_data = torch.tensor(solution_data)\n", + "inp_data = tp.spaces.Points.from_coordinates({'x': solution_data[:, 1:3], 't': solution_data[:, :1]})\n", + "out_data = tp.spaces.Points.from_coordinates({'u': solution_data[:, 3:]})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data will be put in a ``PointsDataLoader``, that will pass the data to the condition. \n", + "As a condition, we will here use a ``DataCondition`` that just compares the output of the model with the expected values.\n", + "\n", + "In this condition, we only train the model of temperature, since we only have data of the temperature. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "data_loader = tp.utils.PointsDataLoader((inp_data, out_data), batch_size=num_of_data)\n", + "data_condition = tp.conditions.DataCondition(module=model_u,\n", + " dataloader=data_loader,\n", + " norm=2, \n", + " use_full_dataset=True) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the differential equation we have to create some points inside the domain and check there the PDE.\n", + "Here, both models will be used, since $D$ and $u$ have to fulfill this condition together. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "inner_sampler = tp.samplers.RandomUniformSampler(domain_x*domain_t, n_points=15000)\n", + "\n", + "def heat_residual(u, x, t, D):\n", + " return tp.utils.div(D*tp.utils.grad(u, x), x) - tp.utils.grad(u, t)\n", + "\n", + "pde_condition = tp.conditions.PINNCondition(module=parallel_model, # use parallel evaluation\n", + " sampler=inner_sampler,\n", + " residual_fn=heat_residual,\n", + " name='pde_condition', \n", + " weight=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is all we have to do. In total, the definition is not much different from the inverse problem with a constant $D$. Now we can start the training:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Training with TorchPhysics" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", + "\n", + " | Name | Type | Params | Mode \n", + "--------------------------------------------------------\n", + "0 | train_conditions | ModuleList | 26.8 K | train\n", + "1 | val_conditions | ModuleList | 0 | train\n", + "--------------------------------------------------------\n", + "26.8 K Trainable params\n", + "0 Non-trainable params\n", + "26.8 K Total params\n", + "0.107 Total estimated model params size (MB)\n", + "30 Modules in train mode\n", + "0 Modules in eval mode\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8911d9de5d25409ab344ea15b782b5a6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: | | 0/? [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_sampler = tp.samplers.PlotSampler(plot_domain=domain_x, n_points=760, device='cuda')\n", + "fig = tp.utils.plot(model_D, lambda D : D, plot_sampler, plot_type='contour_surface')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here the exact function:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def exact(x):\n", + " return 5*torch.exp(-1/20.0 * ((x[:, :1] - 3)**2 + (x[:, 1:] - 3)**2))\n", + "\n", + "fig = tp.utils.plot(model_D, exact, plot_sampler, plot_type='contour_surface')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And the absolute error:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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U19rUwY8AqSbhoRJeDci5IPYgV1ZSB/Gy5+MXF/xaS7R0AS26IWS8/hlU18/cB06MWKBr7QjCKkJj7Z7x417RESI0QQkRv24ZY2tIOUSILrYCpFxnpbBX+dXFZh7jScA4YeKoHZwY8YnMwuFt598qQuOxioQZJyLrq7pmsu11jxeKNaQSIsTkYmkjQMISH9UiNsKA997Gg0BxLhxHdePESIDQFo4wrSIVixOhUVlEEhbH443LG4d3PCBAIVIOEVIt4qOehYcuqs+gXsSKEySO6sWJEUMG0Al9n3YOv1YRI/eMbF9Css8kToQnROhvUgKlqMYPJUi1nCJE96LeD/2YkSDEhxMb/hF9hvUiUhyOyuPEiA9oYeHXKqLbJzD3jKyfqRChYSuxBhkfom0NqTYR0g99AetHgDjhUV7oz9sJE4cFUajXzZShs+RVjeDESMjoWkU8fQRWEWv3TEKyL0ghopoTb5uOEKmYNSQIEaKDrQApt/jQrW1h835ISkE/Std+qgXXAvlfVLsoca4aR3XixIgmOfdMEZFVRBeRVUR7VV4TQWHbLyEZx9QiYhsfUnZriN94kDAFSDnEh+m8ypHBIzpGNV5Ua0WUOBzVhRMjAeMRFhZWERqhVSSoOBHdfoD3GssLVm2RjFURIVJuS4iuCDEJXA1SfNRJMQIP7HuqJnFSzaLEWUcc1YcTIxaUwyqivRCebpyInzokKiECyf6ghIhRpkyQ1hCZIDC1gnRKW6mPZ3Ks8Ug1lkivVlFCPqtKfz7jnA64mJE8TowESJBWERorq0hCsx0L2092vZUdg3cc2/iQ0KwhYYqQsNaksTnGeKZaBEq1ihKHozpwYsSQJNp99Q/NKhJEnEiC2ScLWGXbqiwiFREi5RYh5RAgJsdx8Kmke8eJEoeDhxMjPhAKC4FVJNBUXhv3TFhCZABAq+Q4gbllqlWElJugMlgclcOJEoeDxomRKkArldfGPWPr0jEVIrLXKiGiZQ0xyZThXZRtRIipFcTm7pq+EPm1ljiBYkelYyYqLUpcMKujOnBiRBPWPROUVcR3TRFdIaLTJzc5LyZChCU0IVIpEWKbiaMDuRgFmUGjc5FxgqU6+BBOkPglhpr7PnfB31V4NKiJVB4nRsqMrnvGg0hIVJMQ8WsR0XLLBOGSCVqE2DBRMo8wRImMsC9Cpp9dDPJqteW+2JRz5dtKWklqVZDQ51OzZToc1YUTIxb4sYrQ+HbP6KAbZ2IrRMjzVur1sKRvxawhpiKk0ndY5RYlYaF7gdNdr4eMF8T/RzY32XernkVJLWBxQ+eoepwYKSNld8/o1hLxK0REsH1DtYbUmgjRvdCIRImsf60LGB2CFCUsJmsVhSlMyi1Kqt064kRIPePEiCFDIiuHoVVECz/umXILEVXhNKUQ0bGGVEqEVMMS8ybHGE9CxVaUBBXwWw5rSSXjSaoFJ0TqHSdGfGAS/2FsFdGxQCQ02sj6BCVE+gG0SfoaCxE/1pByihC2XS1cMMoxx0oIHr+WEr8WlrCtJeUSJNVmHalzERKFdykNU0SJhjVIo7qJQ0UQVhGpe4ZGR3ToWFGCFCI09IJ5g8y4KVgIkT6UXih42z4E/yLYzxlTNAY9js0F1bZfvTFR8FcOuqC+mLL7g3b1iL5bftkTwpjVTJ0LkQqwevVqHH300YjFYpg0aRJOP/10vP7669I+9913HxoaGjx/7e2lxT///Oc/43Of+xy6u7vR0dGBo48+Gjt37tSem7OMaMK6Z4K2ingIwz1Dtw9LiIjGBEJ2y4hECA9Tt44N9Fi1YC0pFzqfxQQE87/QsZSEHR9UbVaGWsIJkTB46qmnsHTpUhx99NEYHR3Fd77zHSxYsACvvvoqOjrEN8RdXV0e0dLQ4A04/5//+R+ceOKJ+Jd/+RdcffXV6Orqwp/+9CeuaBHhxIhPbK0ixu6ZoIQISxBCJAF4yrD4dsvUqgiRjV8JURJmkGeYBJlJxPsMyvl5uAXpzHFCxJS+Pu93uq2tDW1tbSXtNm3a5Hl93333YdKkSdi+fTs+8YlPCMdvaGjAlClThPu/+93v4pRTTsFNN91U2HbggQfqTh+Ac9NYobKK6BQ4CxzdmBHRCrwJyXgqIUKTZF57hEga5kJE1yVj644RtTf50yEME3uX4k+3XRB/YRCkiyfMeeoQpACqZ3fNOBMiHQH8AZg+fTq6u7sLf6tXr9Y6fG9vzq8+caL8NzYwMIADDjgA06dPx2mnnYY//elPhX2ZTAa//vWv8bGPfQwLFy7EpEmTcOyxx+KRRx7RmgPBWUZ8YLsybyhWEQi20+39ChGdNWYIgQSp+rGG2FpCgr5g2V5Ia+1O2rRmhyns52hrNamktShIt009ZtiMMyESILt27UJXV/G7xbOKsGQyGVx88cU44YQTcNhhhwnbHXTQQdiwYQOOOOII9Pb24nvf+x6OP/54/OlPf8J+++2H9957DwMDA7jxxhtx3XXXYc2aNdi0aRM+//nP48knn8QnP/lJrffgxIghA+iU7re2ipQzTkQkRGTpujIhMgggQ7327ZYJQ4SUC9MLRK2JDhvCECr052wjTGrVhUVTT4LECRE/dHV1ecSIDkuXLsUrr7yCZ555RtruuOOOw3HHHVd4ffzxx+Pggw/GXXfdhWuvvRaZTO7kf9ppp+GSSy4BABx55JF49tlnsX79eidGwsbEKmJV4AyoHSFCIxUiNrEhQYoQk4tWWCf5GPSrjZqMWS6CXsGYnECzsC/n7cdqUuuipNYFiRMhlWDZsmV47LHH8PTTT2O//fYz6tvS0oI5c+bgzTffBADsvffeaG5uxiGHHOJpd/DBByuFDo0TI5rkLCIj0jYiqwjBuqZIQrC/3EIkwbTjlXYv4NcaErQlRPcCFeSJnb5TMbnYVvMJWjU3v2KFiDU/4sDGakL/r0yOXQ1iplYFSTV/z+uTbDaLb37zm3j44YexdetWzJw503iMsbEx/PGPf8Qpp5wCAGhtbcXRRx9dkiL8xhtv4IADDtAe14kRC2ytIkpUoiQoISIak32tK0SM3DI2LplyuGOCCpI0oR5PxrL3ZCJUbMUBi01mjkxgVKtbrdYEST1+9y2Ioriulw3y++MSli5dio0bN+LRRx9FLBZDT08PAKC7uxuRSO5adt5552HatGmFINhrrrkG//iP/4h/+Id/QCKRwM0334x33nkHX/3qVwvjXnrppTjjjDPwiU98Ap/61KewadMm/Nd//Re2bt2qPTcnRgIiEKsIb1tCsV+3lggNaWcjRLTcMqSkoF9rSFAiRHYhUp3Ag7r4kOXNw3DTEIK8GIWR8kz/LmyFCWD3HfAjSmqFWhMkjnJz5513AgBOOukkz/Z7770XX/nKVwAAO3fuRGNjMdF2z549uOCCC9DT04MJEybgqKOOwrPPPutxy/zzP/8z1q9fj9WrV+Nb3/oWDjroIPziF7/AiSeeqD03J0YMUVlFVEGrSveMSeZMELVEeGNZCRETt4yNNSQMM7jsxO33QsQKUl0XTbVcTHTm4UewsJ+PjWvEpm+QNUxskc3X7/fOCRKHmGxWfR5irRnf//738f3vf1/Zb8mSJViyZInt1JwYCQJegTOtSqsE2zgREaZxIqZCJFC3jI01hO0jO/nyLjpBixBTk3O9XCxE78PmQk9bjnSL5hBsXDoTURlBoppfEOm/1S5InIvGUYoTI5rkLCIj1Gu5VaTQztY9w9sWRsBq4EJkAMWvlV8hopviK6pyaiJEVBcAPyfQicjlPvchV+48DPxcwMoRf2Ny4SeftU0wrElAaTVYSXhUuox8pY/vGI84MWKBquy7VU2RoONEyi5EWCUlc8sEJUJ4bURiw9YaUk6rR6UuALrHLXeGi22MCVD7osQJgnFBF7wrnpsyHNREKo8TIwFhZBUhmLhneNvotmUVIn7iQ0yFiOkFIihriEqE2IqOGIAmy76Vhvd5+QkmBcIXJrUsSvwIEr+umrDEkHPROPg4MWJIiloRTtcqEqh7hpCgnleVEOlH8WJLXwDCFiE8qkGEkGONGfbTGdOUMIKA/QoU8nkSN5YONsKky2D8ahIlzkLiGB84MRIAKqtIgSDdM8WDFymrEOGJEBoTIRI0piIkCAHi94JRjguO6TFsxYufbJcJyK3faWox0flOmRYoMwlyrYbiZ0HjhJCjfDgxogltEcm9trCKEIJ0zxAqKkRkgaq8kznvAsLeuZpcCMKIBwm6/kiMeqyVn53oPZpecG2sJ6aunLBEiamVxMQCo4utKKi2rBrnonGIqeqz4tjYGK666ir8x3/8B3p6ejB16lR85StfwRVXXIGGhrAKR6nhFTjjpfIG5p4xKWxG76+IEOlH7s4W4FtE6BNSDHaCxI+lQnRCDEp8iNqNavanCfrkHZRFKgiRYpKOayJMTEVJGK6bsARJt0W/ahMkDg8d8BfAWtVXcDOq+q2sWbMGd955J+6//34ceuihePHFF3H++eeju7sb3/rWtyo9vfK5Z3jteO4Zer+JENEuZiarH8Iics3YCBJdyi1AbO5WO1GsUFtuTMWNbQYLjc5FuQv6MTW6wkRXlITlulF9N2zEiq2YdILEUf1UtRh59tlncdppp+HUU08FAMyYMQMPPvggtm3bJuwzPDyM4eFivlNfX+5Hn06nkU6zwZf6tKYzAIBMuh1tyD1P50+gqVQEkfydb3Y095jpi6JwIc/kB6Ev+qQQXi+1bRC5SuEDKFYMH0TR0EDiQulSHoP5570oXuMGUVzvYIB5TlR4L4B2AEmgkKlMREhBT4lSd0vjRSKR3JvMPe4p2e+FFLcidDJtWIGigoyV5WxjtwPFWh8ZZjt7seZdIOk2MmtHp+dVJDLqeawN2pnXpsXIAJS4M/n/10hkLP8YRe5LqUMcpd81Fvr/IPtOmVhUyIVddWwZxMqh/z2PRBrzj+W0CtuupBz2WKVEIt0A+nyd5wH47u+woyGrUx+2Qtxwww24++678cQTT+BjH/sY/vCHP2DBggVYu3YtzjnnHG6fq666CldffXXJ9o0bNyIaNVi0zuFwOBzjjmQyibPPPhu9vb3o6gongLevrw/d3d3ovQzo8uGm6RsGutcg1LmWi6q2jFx++eXo6+vDrFmz0NTUhLGxMVx//fVCIQIAK1aswPLlywuv+/r6MH36dCxYsMDXP+vY9KtYtfmvuHr+DAy3NGIgf8eVSuXMCJ6aIn150UNbQshz+uayF6XtBjjbejX388YRHZO++RS6ZUTWkNK7uUjkQ2zY0I8lS2JIpRokfVjrA/talqUjczPw9omqnZqOw9KpbuKhBZFIGhs2bMaSJfORSlXKTWOKnztEEytKPyKRMWzY8GcsWXIwUileHRZdC4KutUJnPBPrnK2VxMwysmHDLCxZ8hpSKU5gvBKb6r9BxS2FGbyae1+RSAYbNuzC/Pnz0dJi/xsj1vSyEEWpAdKEWi1ZxKGqxcjPfvYzPPDAA9i4cSMOPfRQ7NixAxdffDGmTp2KxYsXc/u0tbWhra1Uara0tPj6gg7nfSXDLY34oCVnYk0mo4VPMNWce5Lp7Sh+QYh7hXa7kMcENXiWakfIcNqR8xYvc8Y2RqREiNAr7tJl3enaIY1UH+I/z72xVKohL0b6UXyz9I+7H15/+gC8J6oulMaQyOCd5ExTe4MqBa/+fqVSLeZiRLG0Ef9AFn1KYOdpIk7YC5/sojsBxOWVSjUJxEicei67WOyVf1TFdbDfM1Eb1fEIEzSOyYN1FapJpTJIpXpRnrRb9vdqS/lWqvZ7rvfT12FPVYuRSy+9FJdffjnOPPNMAMDhhx+Od955B6tXrxaKkbAZ4NwVK4NWdbbxxESC2qbKnOEdw1iI8DqKqqmKTry0CGH70NvoE5wqqJWHrgixESA64sPyhGUjLMI6jrFg8SNOdGMySOqzTADoZOLoZL8EHeRaqcX3dKm3QNZ6ei+OqhYjyWQSjY2Nnm1NTU3IZMzvJoJGWlNEN3tGV7DoZs7opO8SAhUi/eAHp8lO3raCREeEyISGbeEzA/HBEwLlEiEmqOakFCu8z0QlUHSrp+oKAFUabdCipJoESa0UJQvDReOESL1R1WJk0aJFuP7667H//vvj0EMPxcsvv4y1a9diyZIlZZ8LzyICCGqKFDuVkuAOXtqetAtSiJD+gQkR0clbttDdRKadriDxI0JCEiBBCAwb178pbOq2Luz707KkmFhPdESAjijRaaMrSpwgqX6cEKlHqlqM3Hbbbbjyyitx4YUX4r333sPUqVPxta99DStXrqzovKSr8vpxzxASnG28C4rKikKPE6gQkZ2wefs+ZJ6bChIaXVeMjQDRsH6YChAiNkiQWhTekJtyIBM8JkLFlzhRiRKVRSVIUeLXShJGUTM/mAiSWnfV1PLcOXTB301Nq7pJrVDVYiQWi2HdunVYt25dpafCRcsqYuqeSVDbTANWAxUiutYQuh1tPZKd8E0FCRCeCAnA+hG2dSPO2ZYIaGw/QsVInNCfs0x4kKJwIjGgEy+iEiVBWEmCcA/VCn6sL64EvEOPqhYj1UgqFRF/arJsxgSnjcw6UnYhYuuWISepLLxxI6JKmSrXSpAixFCAhCk8uqlHP6l8hLhFn4Rhe/a9mogT38IkCBeOX1GimkMQLhvTCrAixoO7ps6sIg4PToxYYmQVSXAG0I0TYffxxuC1U2bNBBUfwhJDLptGp6Q7ffJUiZAKCRBd8RHXbFdJ4pJ9CY3+vM9CJFC0rSYqN065RImtlaTaYkh0qEVXTa3N12GKEyOakOJmgCKVV2Yd0YkT8ROwStqVXYjEUHpCll0ATKwhfkSIhQDRER9xjTYiOlFeP6+uhowLticU/ejPS2Y5UVpNVPE6QYkSP1aSsC0kKjFFUuZjyFkhqyluJUycEBkPODHiA65VhMBaRXTriRBshQhNoEJEJDYIRJDEmAma1PMwFSEBCBCV+Igr9rOIDDOVCjTjzceksGicsy0haGsqTISipAVqSwmgjiuRCWEbK4lKkMjGVY1Nj6GDTv0eGbVoHalDOuAvgLXcwfAh4sSIIcl+TswIKxJkIoPtQ7fhxYmw7Wl4QkQaI6ISIiJriOkdmOqCYeqSsRQhNgIkLtknO7wJptXkTdCpwi6bv45QiVPPE4I2OvEmSlEC+EsNVlkbbKwkQQS2VpPLRpdKxaQ4wTRecGLEkpICZybumSADVhPUPmMhouOW8WsKll35QhAhYQgQU/HBExut1L4Rw/H8HptgK1Rk19449TwhaUc+f5EoEd4d+hUlYVhJKi1IZMd21hFHbeLEiCaehfBs3DM8SBtZwKoqc4ZQNUJkAoq2Q9EJ168I0bSC2AgQXeERpnVD5zgm69DJxrIRKKJrYZx6nhC0kYkSIPd/DMVS4sdKUo2CZDzghNJ4wokRP+hYPERtE/lHVcAq21+23kzFhQiQW72ULFbGS++VxYWELELigu0qAWIjPNg+ZOodCCZ+RHdOKrFhI3Z0wjbi1PMEZ7/KUgJoBLrK4krGsyCpFuuIqzHi0MeJEUMyfdHcArYmNUV0xIpuwCp7jMCFiK4IoU94/VBfHWXWkABESJACROdCXy7LiF9srSFsP1EfE2GS4Owj/7cMZ59UlABya4lMkABit025BUlY1HrdkXFiFYnm/2wJazHkClBHsbgVQmTxMHHPyAJWdVN4C5gKkX6YCxETulAqPERCJAbv1Y1kVuSJoDS+oAOlQiRO/dHEUHoIIHfhpf946LRhoedBFz2Lh/inA/tedMWXqq3o8yXI5ig7IUtjSgBxWrBMcYou1Oz3k0Z0gVRZAGSiwOSia5IG5XCUsnr1ahx99NGIxWKYNGkSTj/9dLz++uvSPvfccw/+6Z/+CRMmTMCECRMwb948bNu2Tdj+61//OhoaGowrpzsxokmmjzpb6lg6RG0TkrZkn07mDLeWiI0QYduFhUqUaIgQGlaExGEvQFh0L9ZxxV8lEM0lruhnIlBMhIlsjjyiEFu5lIGuPFEim4hMJFSrIDFB9zddbbEr48QqUgGeeuopLF26FM8//zw2b96MdDqNBQsWYHBQnIu/detWnHXWWXjyySfx3HPPYfr06ViwYAHefffdkrYPP/wwnn/+eUydOtV4bs5NEwQ6NUVYWKsI25d+rlVLhGeqrhYhQmPgktGJB4lz2oiuCzKrhwrecXQg822mXo9ajsXDZKG7OGdbQtKe/lxE32VVG1lMKZkPbw6+YkpM3TamcSQylw0Ex/FLHyp/71jrrp/6pa/P+z1ta2tDW1tbSbtNmzZ5Xt93332YNGkStm/fjk984hPcsR944AHP6x/96Ef4xS9+gS1btuC8884rbH/33XfxzW9+E7/97W9x6qmnGr8HJ0ZMGUTunGBjHUnkH2XuGbaPVuaMaq0ZAu8EaitEWKsGWZdmgqCNDxHCu1OOK4Yj2AgQdmwRYS+Qp4POHGTfszhnW4Kzza8wkcWWxAGUnjdzqERJYIIEMIsjsSmOZhOT4qhrZJZAA6ZPn+55vWrVKlx11VXKfr29vQCAiRP1rVHJZBLpdNrTJ5PJ4Nxzz8Wll16KQw89VHssGidGgiKRf7QpgCbqq5U5I1p9N0xka8rw2sjaG4iQOOcwvMObChDeuCx+Thgx5IKeyRzGfIwFmP+bTdaUAdSZMCbCxNRaQhYSZI8rEiXGqcA26b82gkSErSDpgl0+d63hXDQ27Nq1C11dxfMszyrCkslkcPHFF+OEE07AYYcdpn2syy67DFOnTsW8efMK29asWYPm5mZ861vfMps4hRMjutAnQROhwaLjnmEhbapKiNAqYCJy6RCkHHwT05ZtD0iLldmIEFHshwjemKLji6hU5qLOcVVfB12BEqeeJzj7VcJEx1pi4sLhiZKyuG1kggQoFRF+17IJAudSGS90dXV5xIgOS5cuxSuvvIJnnnlGu8+NN96Ihx56CFu3bkV7e2758e3bt+PWW2/FSy+9hIYG+/QeJ0aCRGUVsXHPkL7cFF5CmNkwBJGwEN3JyIJTKUxEiI4VxMb6oSM+/AiPOIru/m7w01hNSGi0sSn5rirhHlfMQVeY8ESJqPYKOSZ7vA7O/AJ12wSR+qtK+9Udhx5PFydEHGKWLVuGxx57DE8//TT2228/rT7f+973cOONN+L/+//+PxxxxBGF7f/93/+N9957D/vvv39h29jYGP7t3/4N69atw1//+let8Z0YMWUQudxuVdBqEO4Zsl+rlki5hQhbvGyMeU23BQIVIbpWEHYc0fFYdM/5ovHDRnbchEZ/3ZLvsngNeg68Y8qEicyF0wlgWHA89jgiQQJwRInMbWMqSCDYx8PFj+jj10XjiqypyGaz+OY3v4mHH34YW7duxcyZM7X63XTTTbj++uvx29/+FnPnzvXsO/fccz0uGwBYuHAhzj33XJx//vnac3NipFyo3DMqIeKhEkJEZA0RBaYKhIgsLiQuGIqgI0LYMXjHkR1DhGhcHTpRLE7UgWKsrw2qsIG4ZF9Csk8WYKpajZc+Ju8YIvEhs5Tw5kGOQx/DKpbEryAR7TMtjBaWu8bGKqJbibVaM2rYG6IaoRv+4tEMr+BLly7Fxo0b8eijjyIWi6Gnpyc3je5uRCK5k/N5552HadOmYfXq1QBy8SArV67Exo0bMWPGjEKfzs5OdHZ2Yq+99sJee+3lOU5LSwumTJmCgw46KKy3Mo7hxYwkmNe67hmy3QRhLRHe6rtBIRMisuwY8jwkEaJrBbERILxxeFSqAquftWrinG0JzjYdYSJyNdLHYMeWiRKem0YmStixfbttwhYkpow364gfq0g1iqPq5M477wQAnHTSSZ7t9957L77yla8AAHbu3InGxkZPn5GREXzhC1/w9NHN2NHFiZGwSOQfZS5jY/dMNQoR8pwUzyBXHIEQkblkTEVIHKWYChDeGCxBCI9O6lFlGQlqETydMePU8wRnv0gQqOJL6LHZcVVuGp3QizhnXGO3jYkgAfR/YybxI7Yl6HsFfdyF2SEmm1WbZbdu3ep5rRvz4bePEyOmDCBnck9Qr3mPLLbuGSMhEsSdlE58CO85fTXMC5FKixAbAaIrPMK0jJiOHdRaM3HqeYLZp1qDRie+hB2zUzAP3naeKOKNa+S2MREkAF84mLhagnTXiObihzAXzQsLJ77qBSdGdKFPjglmm6l7xipgVYRfIUKf1HSFCK8dOakrhEicOTx9kTMVIUEIkHIsikf31/EPm1pGbFw3qsyXOPU8weyzjS8hY9LjkXmMCObHEyWBWkl4ga2mtUjCjB8Zb+4ax3jFiZGaQGQV4QkR23ojpm4Zti3HLVMuEcITIGwf2fiq7X7bmmIytq7gkLVXWU3i1PMEs0923RZZKeKccUQiTWQlsRUkQABWkrAEiQjROOWqV1Kt1IFVxG8FVj/B8FWGEyOmEFdt2awi1ShEeCIkP09yohcJEZlLJiwRolr0TUYQoiOO3EljFLnoeVldoITh2KZWER13jcxqEs8/JpjtKmuJjpWEtNWxklSF20YHE8uGX3ERRKZLLbpqHPWAEyNBUHYhwsNvBVY/QgR8t0xcMDygL0J0XDFxThsb64eu8OAdLyhMxk5I9tlYRUwKlcUl8+AJBZmVhN0uy7oJ2m1jLUhM4keCCGidCGCPRnuey7Ueqff3N/5wYkSXXhRPqCaL5KlI5B+lpd5ZWKuIbb0RVmDoxodIUnajAIYgFiK2IkTHCmIqQMJcrZcdP4vc/5quOSJC93sVl+xLKOZj6q7xYy3RESXdkvnw5sFaSapekPAwtbpMEGyv9Quzs8Q4nBgxh5zMgnLPKFG5Z4JyzfgQIh3ILW4GFBc6o7sA8riQOLOPFiI2VhAb6wdvXJP+QaJ7DJloiTOvE4pjmFhFVNYS9limouQDzvFUVpKqFyTldNfUO7Uuvhw8nBgJk0T+MbQ4kaCFiKZbRpayS9CxhsSZPiYiRLckvB/xYSM8RH0yyP2/O1Bcp0aFykIim5+JW4UdS1d8iPqIjiUTJXQJeNKf7iuykgQVRyLMtJFZKGXoCopyV2fVoZrjRupMiPgNYPW7zlUV4cSILoMQu2kS+UfZQnihCxHdE1cIQiQOgKxYTVfTLIcICUKAVEsWjemxTDJpRGIBkFtNdF01JtYSmShR9WUFSOhxJKwg0bWO8KiWcvF+0QmUdevEOMxwYiRoRO4ZGrLPWIj4RSVEDNwyhLjgUJ3MI6+9LC6EHVclQnRLxMva6+yzbU+WzegA0KQ5rq1lxMTlAtiXcDfZTo5Bj8+7BnegtLhoHDUqSMIqFz/B5xi1GuRaa/N1mODEiCkqq4isrd+EF1/umRCFCL0EfCeKqZkia4iuCNGxeuhUZxWNpbPPpl1Q2FpGbANRAbEwCVKU0OOS/z198ee5Udh+PEHCHs9vPRIPQQoSHrVmHXE4gsWJET8k8o86C+GFGieiOln5ECKmKbuAfxHCjqEjStj+on6y7ap9HBq7lVexXLvR0dxjVxKNzXo/u0yvwpnsN31Xt9hZgtNHN36E1142Lg0rEuIacwkqsFW4yB6NrcsmCHeNrYXFCZuqohP+bnJc0bNxyACASdRzEQmmDa9tWQNWdYWIJD5EVcCMjhPpQPHt0G1FcSF0GzIW7znvtaq/n+0MuqIjSFTH5IoVlcDQiftg98XzjwnDcXRECT0maUdv4wkS3lyCCmyVChLbomhhru7rhyCKpPlFN1C20vN0hI0TI6aw1g+boFUPJhVWTQlJiMhSdul2bH9ZcKqJCImjFN30Xon4MBUc0ZjW4kEAgEjeMhKNpdCgYRlJ9rMFXEph56sUJzbCRCdLxsRVwwoFMiZ9PWev77ZuG504EhYrQcIShrsmCEysItWcUeOoR5wYCQJfQas0soBV0xNUGYVIZ7ErupEre87214kLMRUhPgWIjvgwERxBojouT6zw3o9HoNgIkyCsJTrxHWzRM1HGTdCChB2DR2gBrSbummq1rjgcweDEiC70STCRf7QNWhW6Z1h0TjQ84WIqRCSBqnFqWNotA85zgqkIkY0ZRyk2mTV5RALEVHREo0mj9u3pXDpNJJJCQ4s6nSaZjMqPz5mvSqAEJkxUooS0VfXliYVueL/2PCtJ2IIk1IBWlkoLCltXTTW4eBz1hBMjtvgJWuU2Cso9UwEhEkfxm9SBomUkRu1n+/Bey0SITWYN5NYPlQAxFRxBojo2T6yw74cVJ76EiZ+gVB0rCf124yjNuDEVJPRxdTJt2DG0Alp1BAmLHwtHDKURi7p9qzFw1cWLDMYa0NSlWh9C0h9Z1EsUqxMjprA1EAB50CrZp7X2jEyIiNZqJz9U+k4lZCGislyYumRk7hgdqwezzdT6YSI6Ypb+/LZ8qcRODKBFswRrv6RwFDtnlTjxLUz8ihJVPzbcJQ5zQaJz3MAFCQs7qG38SJixI7r4iRvphyt85jDBiREbTIJWaYxX4yUnAtZuzU4EsBYistRdUXwI3Yb0a6b2jzFtwhAhGgJEZvlQCRBb0REksjmwQoX3fmiBoiNMSgJgdQWIjijRddPQxFEqSIDi1z6owFbZMUMLaGUJ213j6pU4qhsnRnRhLSKyoNXA3TP03YlMmIQgRERuGbYfDb2fZ01hX/Pa8/pwtrEixMb6oSs8IrB32xDLSARJNGovTgOkwI8dYefMs6LQ71lHmCitJbaiJCFpQ16TQ3dAnYLrN46ExibDpgSb+JEgrSO6QkZ0zGqO/6jWeTmCxokRU3gnNauaIjS8eiIx8M2kPGsJoFfQjEInY0YmRNggVRKT2Y3i4k08IaIjQmR9oG8FEQkQmfjwIziiEht+a96vG8EQmpDzESehTt8VzYcVKbz3RAsU8lmw7hzyuWlZS2xFSTz/mGDaiLJ2dESNH0Fi6q5hsS6IxqKbXaNDpQNhTQkjbdi5hWoZJ0Zs4AWt+qopwkPlc+VZS3iL3hE4cSI6QoQnHnjZMnRqJu/CEoII0RUgfsWHTGT4QWdckWDhzVsmUIgwsbWWWIsSletG5jLh9U9Qr8spSMrmrmHhiRqR0Kk1QRIkXShGztcOQ21RtLTZB7AOtWWhkfpVEzgxogt70kwIttP7jGqKiKqs0tYRWmSwd1Q8NNwzgFqIxKm2HUxb3v4szK0hPkSIiQCRiQ9T0RE1sKIQy0gUSTTDe/JJClwxsjnxRAr93kTChGctAfjCJBBRorJysG6aTnh/AjaChB6PbU/jV5CUEIS7xq+gkPW3iRmRBbFWs3vHUWs4MWJKYEGrNKwQIT9ymbuGzqLhbVcIEVWMiKkQoa0lJtaQAEUIT4D4FR8mgsMW1TF4YoWdOytORMKEZy0B+G4c36JEZSVJUK/Z74+JhUWUaMY7lt8MG5rA3DXlGKMWcUJnPOHEiA2J/KN20Kqq5DsP3l0HbxvvBxuwEOG5ZeLUNlZQsJaRuKAd+K9pEWJjBREJEJn4MBUdptk2uf9AKzoxwDXmS9N4OXNjBYpMnKiEicpaIhIlyuwb1et4/jGBUkwtLID3mi1z2Zhm2NAEsn5NENYRmcvHxrpSy1aOWp23g8aJEV1E5lmtoFUalXuGPcGw1hHVSaOMQkRl4Ygr9jPPTUSIXwGiEh/lTu01SeMFSucvEyc8YWJjLWFFiTQl2NZK0uGjb+7N+BckZYkfMSWG2krNdcGkDjOcGDGFpPjKrCKAQcl3eh8tUGjhIQpmFYmSgIUIzy0jEhfsMcosQmzEh4nwsM22yf3QWtGOJPdHJ0rhBdSZMoD3/ekIExtriZEo0bGSsGvTjGq2VQkSGlmch0nKL3sMrZLxsgHDto7UOrrWjtq2iiQRQZNBqn9p/wzqJYDV/lNwyK0iHmxKvvP2kW0yN08ZhUgn9Zy07+a0E/WBWIhEo8nCRTCG/sIfIYKk54IaRapEiESRLPzR0OPxxI3sLyxMjyl7D/T7Zt8773PiHYc7LiMMo7GU53/W2D1Ymnatypbi1ZIRtZWNG6ees7qdlzWmcwzZKtMsJfHELbxWPnHWBoc/nn76aSxatAhTp05FQ0MDHnnkEWWfO+64AwcffDAikQgOOugg/PjHP/bsv+eee/BP//RPmDBhAiZMmIB58+Zh27ZtxnNzlhEbjK0iNCr3DCtCdIJZCSEJEbKd14d9rtlOJkIIOq4YngBhCaquiJ8UX5JBE8UQRgVrSQSdxksgnwltMSHvRRVbwlpKdAJdSywlOm4bMv1ueAsMsum4gNhiEoc3qFWU9ku34x1DFtBKY+yu0SkVTzOeU3UdYTA4OIjZs2djyZIl+PznP69sf+edd2LFihW45557cPTRR2Pbtm244IILMGHCBCxatAgAsHXrVpx11lk4/vjj0d7ejjVr1mDBggX405/+hGnTpmnPzYkRXdgKrEqriElNERYiQHTS6shjBYQILTSImb0TKGSuctrpiJDc4b1WEBo/AiTI1N6gCTqNF2DcLRxXjm5sia4oEQa56rheEvnn8fxjQtLWRpBA0E41pqyf1fo1ImzcLrp96tGdU9sumiDp6/P+f9va2tDW1lbS7uSTT8bJJ5+sPe5PfvITfO1rX8MZZ5wBAPjIRz6CF154AWvWrCmIkQceeMDT50c/+hF+8YtfYMuWLTjvvPO0j+XEiCn0iUdqFWGhhUMfinc9oloEgFeI6FRYrYAQIW3Ym31LIeJHhJgKEF3xEUQwK/ELd2IAY4UStV5EGTUmabyAZnCqgbVEFlPCiyeRZt3ILB0scdgJEhrdgFYWPwGtHmxW9qUJyjpSS/El40dkDCAGP9ESA8gA+ADTp0/3bF+1ahWuuuoqX3MDgOHhYbS3t3u2RSIRbNu2Del0Gi0tpe7IZDKJdDqNiRPNquw6MWKDsVWEwAoS3j5y8pFVU+XVEglJiIjiPug2ZFsiP06jt50fEcITDCoRYiM+bASHKChWVsRMhm5Gjd8aI7rWEjbYlR2LDXKVBbgKBQl53U89t8mAoZ/TfXKTDT/DhsbIOmK7Zg090bCFhp/Ve8Ni/AgWHXbt2oWuruJnwrOK2LBw4UL86Ec/wumnn46Pf/zj2L59O370ox8hnU7jgw8+wL777lvS57LLLsPUqVMxb948o2M5MaILe+ejtIqw7hnej4e1jtCChO7DWkV4+1AZIUJeZzltURQifi0hOq4YnZgSUV8RNkXPeH2IZSSKpNAyAoiFjGxhPJGrBZDHgZSO47WWqESJiZVEGUfSAfEKv3GIBQNNGIKExiR+xIPfVN9yxY7Ucq2R8U1XV5dHjATFlVdeiZ6eHvzjP/4jstksJk+ejMWLF+Omm25CY2OpRefGG2/EQw89hK1bt5ZYVFS4bBpT6BNVIv/ItYqw9KH0DggoCowYsx3Mdp6lRLLeTJxpEqYQocehboBthQid8cFmhLAZJLysE9KfFSK62SeiTBSaCFJGf+0YAgC0YwgRRfE1nXnw3ofsvdOflWocVQYOOw47Bv0/ZlO1Pdk2Jtk0ccF2Wf+4ZB9vtekgx5eugci643QKGToc5ScSiWDDhg1IJpP461//ip07d2LGjBmIxWLYZ599PG2/973v4cYbb8QTTzyBI444wvhYzjJig7TsO91AZTqVxY/QJySDOBGgvEKEPFI3+zy3jKlLxtQVw7v4iqwfOtYOmWAIAtn4Kc6VTFTkzMZiIit8VrByCGJKVPEktJXEKI6E/FSIm0/lfhG5aILKsBEhc9eEbh35P8lEeO1Za0otxY04qomWlhbst99+AICHHnoIn/3sZz2WkZtuugnXX389fvvb32Lu3LlWx3BiRBfRQnlCqwivFogo/kO0vkyMs03DPUPvthUivIwZWZux3NPGriSAZq41pBwixFSAmIoO27VqGtFU6J/Jf1gilwxvTqxAERU5E7tgxIXPTESJjutGJEgASfova6nQiQcJU5DYpvvSSGNHTIugqQhCaJTLIuM3/qR+LEdJtBfODXb9x4zaDwwM4M033yy8fvvtt7Fjxw5MnDgR+++/P1asWIF33323UEvkjTfewLZt23Dsscdiz549WLt2LV555RXcf//9hTHWrFmDlStXYuPGjZgxYwZ6enoAAJ2dnejslJkWvVS9m+bdd9/Fl7/8Zey1116IRCI4/PDD8eKLL1ZuQvRJiVv2nT7BsCcH1lVDw7prdOJEWsRxIkBwQiSu0YYiCCGi64rhtSdj8dwcrPvEM2+Ju0bltrHB5Bg682YRu6Xkbhe6r+gYvMJpdN9CO6p4HaBw27CI3CVxjTayc6Bu7TDZeLJVq4WEUQit3qgfoVGNvPjii5gzZw7mzJkDAFi+fDnmzJmDlStXAgD+/ve/Y+fOnYX2Y2NjuOWWWzB79mzMnz8fQ0NDePbZZzFjxoxCmzvvvBMjIyP4whe+gH333bfw973vfc9oblVtGdmzZw9OOOEEfOpTn8JvfvMb7LPPPvjLX/6CCRMmVHZiCd5GnlUE4Eeis0GtPHcN4DtOBAhGiNCvOW0auwfRODoKIHexSaGZ65bRFSEEUyuIqfXDRFz4Te9tyP/UOjGAbKHuud66M0CpFYV+T8RqIluvhp9JY5E1g6TUSiILbpW5bfBha+5FB4AmqC0ecZhZSOj2uQnyA1rZdjSy4FYRgVpHJsDM+uGKpjm8nHTSSchm+UUXAeC+++7zvD744IPx8ssvS8f861//GsDMqlyMrFmzBtOnT8e9995b2DZz5kxpn+HhYQwPDxdek2Iw6XQa6bS9zzbSmusbaUsDJEg4g9yJJgLkxAg5U42heJLJoujrZUVUIv8YQzHoIg5gD3WAWH68GHJVxdIo+J/J+bwduaJjQKmYIJqlGUUhQvaRPux2urQ7WYE3S23P5J+P5d0yo0BkLHeBbR8dRSSSAtK5Cy+BXOgi+UBOwHvx7GTO8u0e4UL6NDBtixkq9Ot26hg5imZQ7wW7uJ09fin+fioN6WbPI6GLc6Ua4NzWd2LY85oWGh0YAQAMwRu9HqM+B9I+nr/q0sfoyrdL5vvH8p/RUL7PxPx3mfRpzY9BxmxFEql837b850hESRt6C/3aW/qRSuWFSyQ3JhElkY7cHCJj+d8onXAURVEs0Avp0dVa6ZRgOvM1Rm2fCG/xwjgzFmm3N9VuAryu2Fbq+V5U/8lUn3ZArnPp89Aos481uxc/iEgkk38kFxM6Jxrg+48amNciY7htqXnRhU18wYMkm6z0/bOQ82ApkUhuu5/zfBD9HXY0ZGUyqcIccsghWLhwIf73f/8XTz31FKZNm4YLL7wQF1xwgbDPVVddhauvvrpk+8aNGxGN2tV/cDgcDsf4IJlM4uyzz0Zvb28o6bJA7ia5u7sbv+s9GJ1d9jEjA31j+P91/znUuZaLqhYjJE95+fLl+OIXv4gXXngBF110EdavX4/Fixdz+/AsI9OnT8cHH3zg65815R/T2LByM5b8+3ykhlpydz6eNWjILVI/incneyBecRcotZSw0PEjjKkjAhRujrtRahGhrRv0607BNl4fdnv+MRekmoPEAExoHsBNm/+Eb88/FM0tqfwU6ViN3J2vyBrCt4SUWix4LgzWEiJyv4isH+0G7hrvcbzHTUKSV59uRnzz55GY/0ughX9nRzMEuXDmWU9ycyjtx1pMeO3Y8dj3ws6Hbs+OlaL60i4f9hjESgIA2UQLfvTyM/jqnBMxOEj9TmmrBP2c/lf2arRhQ1N6NdrpjCubC/21KjGA0Xff7ICsdSPv5oxksGHDLixZEkMq1SBt62WPoo2OVUR0rhL1lY0pO++p5iLeH4mMYsOGFzB//nxuZVBd+vr6sPfee5dFjDzeexQ6fIiRwb4xnNK9vS7ESFW7aTKZDObOnYsbbrgBADBnzhy88sorUjEiqsnf0tLi6wuayuub1FALUv+XHycFFN0mzcj5c5uQM4V+iJyJtAFemzFNIv/Iiy6n40k46840AhhCztQ8jJz5uBPASP4xnd83ipyIGEXR45NB0fVCPwLKGJJcwGHua0PHhwylcz+o5pYUhlsaEUESw2gsxBWMoAFRJDGSNxvH0F84HUeQLBheo0gVnsfQXzDaEoExVuhTPLtn4BUgxAjMxnnQqlu1QJ7NOjVRaepm7n8XbRkAWrzteGvPtDOxAez6Mx1UYAMd09Gef8+0QCBuFTojh7hTSLtI/nhkrFYmHqSZiSdpw0ChbXOhbW5fE4YL/RoxVOjTkh+D9GtoGSnEkUTiuc8k1dSM4YkjxSybLnh/PrzncXjTawc4z6PwXvNpb8Co4DldiK2V6k8/B+DxoNH6lP0KCb9SE+AVCOzFyetaSaUaKDFCFtEE9ZqNK2HvN1k3iZ/7UdYNpNouy6RRXUz1LrZ+z/V++jrsqWoxsu++++KQQw7xbDv44IPxi1/8ovyTYRfK08qgEd21sKKEBJmxP1TDeiLg7DNZbyYu2O4RIvxsGV58CF24jKCTVcNmcdDwMkloyrVAniqgVbTOjM7xVQvjAXZl3slnR4sSOiCVN5astggvDZgObjVN/6UpWWQvzIBWWXVWU+hxreuOhF0PxNUbcVQfVS1GTjjhBLz++uuebW+88QYOOOCACs0ITGCaLINGhsx1A+ROFj7ridDtdIqasfsMhAgrMGiLCE+IlEuE2C6QF8TCeKVWmda80WoADfmAUwIrXHjzC7LMOytKeAXOPIJBkjVT2lY/24YWJNm+XGRoNJZCKpXbHrogodHJrhGNE0rdERq/4kGWVVMrpn2deerXtHBUH1UtRi655BIcf/zxuOGGG/ClL30J27Ztw91334277767shPTritCtvF+SCJBYpjGC4gtHPQQcU5bSyGim7ZLLnJ+rCGmIsRUgAS5QJ4I1cJ5siqqxWOKrSey4mX0eKwVRCVKZFYSqbWDGYftx+sTiVAil1nTJnBBAk7bIKHHDaUq6wTIJ15Oy0e1rGcTg78Kt5UhiUgh7d+uvzr+rFaoajFy9NFH4+GHH8aKFStwzTXXYObMmVi3bh3OOeec8k+m5PojsooQWJGiEiS84maAtntGVE+E3saznoj2WQqRCIYK8SGiNuUQIbYr9AZd2IyMmcUoRvLPWcsIK1Z48xNZT3iiBBDXCuFZQSJIlbhu6DaslUTmtpHVJFEJEi1saorIxqDbVtw6QmMiKHQPzjuGw1EdVLUYAYDPfvaz+OxnP1vpaRQRWkUIInOozh0EzyoCPfcMII4TodvxBImhEOFZOnj1Q4IQIqJ4E/b4vPFl/Xjj66AKfBWR9BSokM+BZ0kxKfNOz1MW30EfSxVPouu2MYkjofukmfcstI6YouOiCYM4LKwjtqKiHPCKNwaJE0bjnaoXI7WDzl2MSJAorCKAnnuGbmcSsMrZZyJE2GqqI2goESJBihDb9WnYcVlshYYO7fk5tiOFRqaAGZspo6rAqhImQYoSkSAhx9N126gECQmAjkRSSKXz/cJw10DRtqqsIzRdMPMpsZaVWq7GqhIqtgXbHNWEEyNWqFw0sn20IOEthMdsF7lnbOJEeO05MV8qISKLD+G14wmRcomQIMWHTdYNISOpOinLlCkeuzQ7BhDFhqgXxZO5VHLtU+DFkphm25gIksJcBKXjfQkSKNqGAX18qywdmauGFRemVhVniXBUF06M6FJyHZL98E1OCqxVRBK0Cnj1S0hxIoC9EOnEANIoFSJ+rCHBrVETbHqvDVGOZUSWKQPILSc2688EbSXRzpqRCJIMU2QtMEECzf1xlNc64qFWXDV+CdPNU5sMoBMZHwsoJmswaFeEEyPGkCJnPHhmUJGvlbWKaAatAv7iROKSfeCvoqojRHJm9lZPO5kQKZcIsU3vlR3HhjHJSUNVZ0QmTkRpuUCpKJFZSej2PFFi47YxFSRA7ns0lC8B7EuQEOII1l3jB3ocK1eNc0eU4j6TesGJkUAQuWMItCChRcdEaAWtxkt3W8eJ8LZpZM6I4kNyhy1erNuRSzZjhYiJNcSvCClHeq8tnRhAE1NGXlVnRKfGiI4oUa3SKws+tXHbmAoSet6q1X6FgoQQlruGHkvHOsI7thHjoUCZzGXk3EnjBSdGtKHvbMmZRpXSy0LHihgErZIuAN8qQu+PU+0CDliVCZHc+i6tiGIIo/ny0qwQMbWGyESIjgCxTe/1jhFMUOuopOS2qs6ISY0RngtHR5ToFDEzcduYCpIxeJdw0KnSykUlLIJw14SOrqvGb9yIw1E9ODHiGyJIRC4awCs8LINWAXn2DN1WFbBqKERKLRXy/fQ+XSGik/7L9uXPzdz6EUZtERERpAprudCIglIBsxojMheOaREz0tbGbWMqSOj3MZz/YvIESaDWEdu29H5T64jQVWNbAE1GvVtVnIumnnBixJgBFBfFY5FZTFj3DG+7YdCqTpwIactuMxQioqqqxf1D+WEHMJbPHGGFSLBr1NjXF9ERH5HhYAXK6Kh8MTJRUCrgL5WXjG1S6p3uL7OSBClI6Do1qqJoyhokfoNZaSpmHaEJSlSEkd4bphul/l00ud+QjwVc6+gS3qhu4hAjs4rwCMAqwm63jRNBkEKkVAj4ESIx9HssK954k2RJH1bA0P3p+bBjFcYcTpb8hUVkRH0seq68yrPs+2M/A4D3OSVL/gciFxzpT7elxym2Ubvd6HFFY/LgtaVjmOg1kgrwvu80cekhxb8xHvR+1XFDpRozVMphsXBWkXqjfmRV6NC3Tya1RdiTRQBWEbZNnLNfI04ECE6IkKJVUSQLlhFWiPhboya4GiM6YqOjT1wXxIb0qKTOCDOfVJtdKq9OfRFTK0mYFhL6/Y0W9g9huDC2vAYJQVmhtVLWEV+uGhf/4RhfOMuINaxVhHfi4GXQsK8DsIrQbQzdMzS2QoRfpt1eiNB38bqWkOJ+vkVBZono6MuU/IVFVONYOlYTGr4lSGzp4FlJRH1lFhKTwGSVGy0q+E6I5giUwTqigh6rrBYRmVXAWQwctYmzjBjTD6BJsE9mMZGdJAKwitBtLN0zNKZChGe+lwkRU2uIVY0RgQVES2wEWZlTcaGi5zPY5b0/oN8DbTGxTeUVxZKYpOh625GYD71aJLzxZAvl8dry0n2Nglll22jisMusEY1Lj1e2+JNaDWKVxYs4wVWPODFihcgq0sVpoxO4CrlVRDdoFQjMPUOjJ0RyF9F2DCEDsYih+9P7QfXxI0KsBEjYi6cNABjLPx+EV8syQoWdJy1OeMLEVJSYum1MAltFgoQgEzgkgya3tlGH2rWj665Rpe/y2tnWHeGh423RKoDWBWCP5qSqEVFsS/0HqcrI/V5affQfUTeqEZwY0cbUfytwxZRsl1hFbIJW6e2SNF5AP06Et49nESmMqyFETK0hNiJEKEB0LzRBVd2Uwbtbp7sLrCbk/apEiU6FVFkKsG4cSWFeHEGiEz9CV/DlzVf0ngBvdk2BsK0jPAJZ68YmbqSWF8FzOHK4mBFjyMmBLTakwsAqYhq0KnPPUM9p94xpwCq9jydE2vOpmTK3jq4QYduKYhUAlMRWCGMxBqg/lkHBXxCQscgUk4pjSObJe1/s+zfJmuFl3NCYZMWosmx0xuLNg7TltuO4F3mxUNqxIyZZMSqRSfenf8+84zoMcC6aesWJkcCIUX/sdoKmVQQwC1qln8f5+9k4kdxQZgGr9DbRxUc2Lr2PHoO+KMra6ogQD6ILu67oEIkUkz8VFsLEVpQU96WEgkDVj9dH5zuhOxY9D5YIT9jkBYkymNV0G02ces67FsY523RQCRoptjEV1ZgKzDK+XTfjFeemCQX2ZGARKwKYBa3S2wV3eDprzujUEpEFq3ZiAFlBP/5FyzDFl3HHcAUID5XoCAsydoZ6TW4B2IsRPQ92Hx3PQJrk3zvrvmEDXU2DVHn9TNJ02bRfnbEInRjAh6wbxtBdE1jsiApeEKqtq0YrxddZBeTYFw+rFAOIYdRHzMhQHcWMOMuINqzvdiL1p0OAsSKA0iXDs4oApXEiNKYpvLyCZqJ+MiEicsvwrCGEEuuAygIi2i4TIr0+/1TI5iHazrGWsJ+FqZWk0E/itlFZNVSF0WSpxABZ28i7X2YdoZG6axS/jZJ9om1xzn4VIleNI4+zgDiKGImRP/zhD7juuuvwwx/+EB988IFnX19fH5YsWRLo5GoHmTAps1WE3s6xigB6bhRVwCrPIgIULyqy+BBdtwyBvrgqRYjpxR2wFxMq6LFIclWfZHwbYUKhI0oIJm4bUR+2vax8v2gs2WKGdB/V8QrtTd01cck+EX5dNSZttakF94tfNMsjOIQ8/fTTWLRoEaZOnYqGhgY88sgjyj7Dw8P47ne/iwMOOABtbW2YMWMGNmzY4Gnz85//HLNmzUJ7ezsOP/xwPP7448Zz0xYjTzzxBI455hg89NBDWLNmDWbNmoUnn3yysD+VSuH+++83nkD9wRMmoswahGMVoZ7zrCKAv8wZWR0RXj9vX7lbxlNMixMXUkAkQsDZphIgMsKMF5HNQTQGu10QU0IjEyQ6cRx+C5mJiqLpjqPTzlcwq2yfTTEzkz6+4kZqhfEglGqDwcFBzJ49G3fccYd2ny996UvYsmUL/t//+394/fXX8eCDD+Kggw4q7H/22Wdx1lln4V/+5V/w8ssv4/TTT8fpp5+OV155xWhu2jEjV111Ff793/8d119/PbLZLG6++WZ87nOfw89//nN85jOfMTpo7UPEhaqYkEbpdyAcqwjkQauAPHOGba8SIkWLyBA7hFSI6MSGKONCeCKEh0h4hBUzwo7biGJWDXsRInPrlozRwWynXw9AGk9Cx5LwUnlVMSF+C5nRKb8E73Fz35t2JJGWxKHwjseinepLiEMvdoTXLsjiZcal4XULmtVS4TMb142zivT1ef+/bW1taGtrK2l38skn4+STT9Yed9OmTXjqqafw1ltvYeLE3PVsxowZnja33norPvOZz+DSSy8FAFx77bXYvHkzbr/9dqxfv177WNqWkT/96U8FN0xDQwO+/e1v46677sIXvvAFPPbYY9oHrF14BYe6qD8dmNLvomqrQCBWEUAetAroBawW+6mzbYpTZkVMAEJEZQ3hWRNE1gebFN4grCGyucrmy+tjaCUxcdsU+ggsJKq2vD668SNSN0wQ1hFCUJYQQjzg8eoaU9FRn0E3OYd11MdfTnRPnz4d3d3dhb/Vq1cHMr9f/epXmDt3Lm666SZMmzYNH/vYx/Dv//7vSKWKv8PnnnsO8+bN8/RbuHAhnnvuOaNjaVtG2trakEgkPNvOPvtsNDY24owzzsAtt9xidODapg/y9Wb6BNsBqVVEduIStRNsVwWt8sWG3D1DP+ddKAgiIWKSLVMiQmhUlhAbC0hQd7eycXjnU1kGjcxaorKSAIXvQ0dfRphxY5s1o2rLq9BKw9vGo1ACPgjrCEHX6iFrb7OOnVv7zhESu3btQldX8VrDs4rY8NZbb+GZZ55Be3s7Hn74YXzwwQe48MIL8X//93+49957AQA9PT2YPHmyp9/kyZPR09NjdCxtMXLkkUfiySefxFFHHeXZfuaZZyKbzWLx4sVGB65vZEIFAHuO1Clyxu4zsIrkDsGrhmrunmEpCo2h/OEHmH4hChG/IsSvAJEJCZs+g4Lt9PvqFrTl9aVcNyq3jW7qrUlbE3dNivmNyNKGZceSlok3rcrqp6KqSWXXusKPBUNkLXGBqyq6uro8YiQoMpkMGhoa8MADD6C7O3fyWbt2Lb7whS/ghz/8ISIRgeC3QNtN841vfAPvvvsud99ZZ52F++67D5/4xCcCm1j1Y+qDlQSuAnqBqyx0O4VVBDDLnqFRuWfYPjzXDHs8TzsmW6aAyiVDo+uGUblUbANUbVw3tpk/bFvZa4XbhqBy2ZgEtOoWMaO3FUVsacyRzArHO1ZhfF5mTfGApcTFzaVtxkUQqmM8su+++2LatGkFIQIABx98MLLZLP73f/8XADBlyhTs3r3b02/37t2YMmWK0bG0xcg///M/4/vf/75w/9lnn+3JrnnwwQcxOBhWZGAl8bNYVUCBq5ZWEcAue4Z+LhIirEWERppdI4sPAdRxEmx8henF3Tbmw4RBlJaDF81DZ7vqPStiScIUJIVxOVY30+waqdAwjB0pYBsnohv7EbfoKxQz9PmiVmMmwsykcVaRsDnhhBPwt7/9DQMDxRPIG2+8gcbGRuy3334AgOOOOw5btmzx9Nu8eTOOO+44o2OFVvTsa1/7Wolaqm14d2dsbAgvmNXARQPoBa6y7QysIoC9e0a3GJqsDZu6SyhcIOkLp+yu30SEsOiKj3IVPDOZMzs/to3stQ9BUmirECS6aboit58I00JoLNJAVhkyIRGGPgjO6l3FuGJnhCG0I4WI9d8Q2o2ONzAwgB07dmDHjh0AgLfffhs7duzAzp07AQArVqzAeeedV2h/9tlnY6+99sL555+PV199FU8//TQuvfRSLFmypOCiueiii7Bp0ybccssteO2113DVVVfhxRdfxLJly4zmFpoYyWazYQ1dBfBWyJQJEwMXDbs/LmlnaRWht5u4Z2hkxap0LC1CIUJQWUNE+2xdH0EXPWPHknn1TOZrahniWUnymAgSmwwbG3eNzhhsG+4+2Zo1BNOaI4S4ZJ9qjEAEjM4gQSulsGuFmMaLOKuIDS+++CLmzJmDOXPmAACWL1+OOXPmYOXKlQCAv//97wVhAgCdnZ3YvHkzEokE5s6di3POOQeLFi3CD37wg0Kb448/Hhs3bsTdd9+N2bNn4z//8z/xyCOP4LDDDjOam1ubxje8zBoeHBeNzPIhS+eN8/eZWEVobNwzxWkW+4wAaC8ImACFCMHUEiC7GbYRGzo316r4gT4U16kBvJkyogBW3nY2g6aXM5aoPRPYqhvUapJhQ+Bl1xT3FYNZ6Uq+CcGHqJNZwztOCaY1R3T62dQbkR3Liong3yiFhbNw1BonnXSS1FBw3333lWybNWsWNm/eLB33i1/8Ir74xS/6mptbm8YYYmlQWUc48EywsvUv6NchWkXobTbuGVbg6AgRT+lynhCh7+hN7/51LCAspgGrIkz7mdRA8Rs/48NCUtxWamkwddfwao/w8GsdKbTJ/yakFVltrSS6peFDqzfiBIGjPnBiRBtZgQCZCOG4aOJMEzZwVafIGfPa1CoiW42X555RCRETi4g0Y4Z+BOTWEB0RoitAbNDtS7cTHdOkLLzqteozIxgIEpOAVlk7WXuCSQ0baRvTQFaTNnGN/iZUdUaOrosmaNdQrQbtOmxwbhor+pH7oXyI0h8qz23TYha4yr5mrScBWEXo7Tor+NLwipplIC+yIxUiOm4ZkwuyzAUjEw9+RAkP1QWG537h1RThtVW9ZgumDTJtDVw2BF5dEV7JeBt3zYjgw+Idx6YNoVBzREYcwblqbAqmecrCO0qpn3iRFKIYMwxCpRmpI3uC8Tuh03dZ7rrrrsLzAw44AC0t9fOlEUPcNTzriKGy1y1yxry2tYrQmLhnVJSIlSCFiI6rgofIiuHXOiKDHluW8KFyK7FtTV7LPkeCwEJCMMmwYdvpumtEbWTI+hfa6ASyEmxdKXV9A+8ncJXXl+dWcq4mh4UYIQvipNPpwrYPPvgAixYtwuWXX17Y9sorr2D69OnBzLIqsajrrMqiAfSLnDGYWkV423SyZ1Sl3ksqsOpUVQX0hYioD9uP7FeJEFP8pPMCxTojJnOqkCDxGz/CtpPFfrTni53JBIZszRoWnTYFgqwzYjpGXNWpkjd0lVxt10LhjYu06PrFyjLy8MMP4+ijj8arr76KX//61zjssMPQ19dXyF0eH5AfC/nBdjGPBOZkEmd2q05QmoGrgMJHzsCLFaGRLYInq7CqxCRGRFeI6NQcUe0Lon6Iba0Rne2898j2kb22hBYkBFPrCA8d6whBK0hVQ/CUEGbciGxfXVApa4ZAnDkhUvMYx4wcf/zx2LFjB77+9a/j4x//ODKZDK699lp8+9vfRkNDQxhzrBL2wOz2iFmhV7ALgPrEpemiodFx0dhaRXj76ZgRoVUkCCFiEqwp2ybqHyT02LJfGi92hGznrVPDiwNRvRb104gfIfAXvJOvX8O204nrkMWeiNrqQBbP8x03UjHKuQRwJa0i44d+dKLVh5IaqaOwT6volzfeeAMvvvgi9ttvPzQ3N+P1119HMmlxl1zz+PjBygqd8fbHxfttA1dzz/1ZRVj3DBE1WkKEhidEZKmpbB/efpmFxKaw2aDiT4c+ybFNrCSi/bqBvRruGkI1WEfYY5i4akwshlrINIFuVkxVx5nYnNd03pCuNcUVOhuPGIuRG2+8Eccddxzmz5+PV155Bdu2bcPLL7+MI444As8991wYc6wBWBcN+TFRPx6dkxT7G5S5aOAvcFVlFZGVfLdyzxB4F0GZ+0G03aTuCN0nzDojpu1lNUZ4Y7N9RfsDEiSqYNbittLqrLaxIyJCc9WwBFlvRKdf1RGURUR3nADcPc5FUxcYi5Fbb70VjzzyCG677Ta0t7fjsMMOw7Zt2/D5z38eJ510UghTrCPizGu2mqqojew1RZBWEd42VXEzgtAqYiJERNYR09gJXh+2fUDxFcKxVccwqS8iEhZA6ecnEhu6cTngB7MSRN8rWTsVbCBrUFk1QnSKnjkcjtAxFiN//OMfcfLJJ3u2tbS04Oabb8YTTzwR2MSqF9HJUaDwVfEiQGAuGl1MY0VoRBaTgntmxECIQLCNF08CmF18SfugREgQlhN2HJ25qj4rk0wbWT8WibtGN9XXJDMmLFcNi7Qaq4hyFT+r6sJn5cLQReOsInWDcfTL3nvvLdz3yU9+0tdkag9e0TMNF41OSm+ILhoROmXifcO7IKqECt2W10YlWlRjq/bpwo6hU/SMF6AKqNesEQWn8sYVBayq9ucDWv0EsxJ469voYtOXLbIWjSaRTOoFuhaIowqDWGsVV1+ERwoRjPpQVGl1k5qhfsq31Ro6NQcUgiSowFW/VhG2v9AqYiJEaDeOXyEis1yUy00j0nKy+BZeW9Frnawi2TECctcQdKwjov4ya52ob5T5bvoiqLojcct+gLvj18F9RnWFEyOBQVdg5ZgU45wuvG0GLhogmMBVervKKiIsfpZ3z0RNhAiNKp6EbcMbkxfUKnJz2LpW/LppSNEz0TFoTCuw6ga16sSPEDTdNYRyBZr6ClJlMYkbqfvYEp1Vf1mLRtipQQIXjaOucGLEGpm/Om8848WLqFJ6wWkjcdGwhG0V8Y0ohZfdZhIfomsV0LGQ2IoM3hg6bXnbWUysQzaCRDQ/Bll2jSx+wyQYlcBaR2Tl5U2xihsxwTT2I25zkGp2cdhm5FR1vrMjZJwY0cZy1V6blF7A6A5MdpK3tYoQZBk3bHGzAiKriOouXMdioHLL6FoddPb5RUeY8PbruG6CdNn4sI7wMLF8kDbsMgJBUBIk66feSN1bRMLET7yIC1wdL9RP+baywluZ1wCbNSok8SKAejl3U/gr+Qa0lKjqDl03ANV0zRbdfTqIVtdVHW8QQKtgf5AVWNmgVl4bVXvO8XnBrASdiqkEm0BW9Zi5oNowxq4+JDdAvnCVV8tJLoDVMLCawgWwOsyIa24zjBeREbaLhrWKFGJFWGuIyO1C72Of6+y3FSK6lhCTNWpMF8szcdFYuFM889IdRzUmb3HDPHrry9in4xJYV42fFF8l5LcWN+hT816GcgkR3o0c78MztIrYX9MdVYATI77RCfhCKCcq1uzsx3Jh6qKxRnXB1bmg+xEisjFNRYVsHB1sBYlJ9VXVcX2IHZmrptDGqtoqWXJAP6smMKrFHROQEbLyVHNsi6OacGLEmD3Ma97dhGb0dwDBq6w4sM2ikY2ZG5djMWErrRJMrSIEnVRTGyEis4b4FR8idIVNUIJE1NbEOqLZlhfISrAJNA00M8aWahEhFcO2fDt7lxWUdcXQKuIKxtU8ToyEBe9Ho1PsTNROeii9k3nQLhpjbC68QQgRHmGJENGxZC7+IFw2uu4aXntdF45PV01pn8rf/oeWUeMQoOuicYw3XABrIBiYIkMIXmXRXTOEUDYXDUE3aNXk4msSvGoiQkyuVTp3Z30ARqAVLKrcJqu8yusr207QDGQlRIaTSLXpOetJgKlJoGtYRGMpJPurJMg1Uc6DabqVq5E6tIoMoBPNPt7AaB3ZE+rnnZSNCYLtHEHi50cSZ15Lip0B+guV+XXRsIGrHWzgKpkW66Lxe/dvUoXVxhoyKPgzwaSPzTo0uuPo9vWx38RVUw1umEDSeyuOxDTlmzBEis5NmkHgqqOucWIkbOKa2wL2WZteAFQumopaRUwuuDpihm0ftKVeV8zYChJZ/IiNmGO3yT7vgF01hHZBvRFV8TPRgntlFUJxjTYVjUkJKg24XPEiBtSwVcThxYmRQPFpeg4weJVFFC9iExNSYhVhL1AiqwgvTkF1kTTJHBH1t1k4LyhUoiQoQSI7vp/9Gu10smpEVEPcSP1AWzdCSHf2jY/MmirxqDnCw8WMWGH4o+JpFEtFLyt2ljuUXryIHxeNb3SDVk0qidpmm5gg66f6f8o+Ot04DVm8h6wYmm4bXjyK5JiyAmgsZBXdaoob8U0nwvWcjEsMXDR1YBVJIYomHwVSxpANcDaVxVlGAsfgx6STSaNh3tW9u4wI2gXiolHFivgNWpVZBnSFiMxKIYoZ0XW56LZLCtrZWEh0xVnQ1hENV02Qa8lUHVWVBhxkJVZZ3IipVcM2XoSDs4oExtNPP41FixZh6tSpaGhowCOPPCJt/8tf/hLz58/HPvvsg66uLhx33HH47W9/62kzNjaGK6+8EjNnzkQkEsGBBx6Ia6+9FtmsmVCqKTFy4403oqGhARdffHGlp2KOyQksLu+rCsLTFRKhuGhU6AZp6gasmggRkzn5QTcOxVaQiPabFozzG1RM4cdVM+6JV3oCNpjGiwRc/KwOrCKVYHBwELNnz8Ydd9yh1f7pp5/G/Pnz8fjjj2P79u341Kc+hUWLFuHll18utFmzZg3uvPNO3H777fjzn/+MNWvW4KabbsJtt91mNLeacdO88MILuOuuu3DEEUdUeip5AjIxx4MZRteFImoX6lo0PGwuvLptTPqEGTeiSqPloeOy0R1XJ9XXdC4MJq4ah0MOx6rsrCKBcvLJJ+Pkk0/Wbr9u3TrP6xtuuAGPPvoo/uu//gtz5swBADz77LM47bTTcOqppwIAZsyYgQcffBDbtm0zmltNnEUGBgZwzjnn4J577sGECaLU2iqiDD8gk+yWIGI9fI3h54Jv0tdvrZIwCMpFoottIbdyfBYMde3KMSFRrgPRbhiReyeIFF/WChJwoTORqNZZsLJO6evr8/wNDw+HcpxMJoP+/n5MnFi0hB1//PHYsmUL3njjDQDAH/7wBzzzzDNGogeoEcvI0qVLceqpp2LevHm47rrrpG2Hh4c9/4i+vtyPLp1OI522X+MwEmnMPwJANv8HABkAYwBGUVhDkYiRdgBtyK3S2orij6g5/9eEohxsyP+R4cljfvjGriQwCkRGR9GeHkMbcm6SNmTQiixa851a8kNHMQSgAU1oRFP+II35V7nDkUnkemXzS8lmMYp2pJBBGzL5Y4whjdH8+KOjucf0aN5NM5Z7SGcixUdynmP1S5J6j+R9D1LPm/KPfdTU2G8offM0yLzmrYY7ytlG5tIu2Bc0Y0C6Lf/5tHGUKjvvFErP3yPwnoSbBc+B4ucIeG83yPN+zvg0tKs3wzzm/9/oLc5ndDQ38GhjJN+kPd+lzfNIvmMRjCJZCNrL/wPTuTfRkG4ufEfJI/n+ksfm/Jeo2TtC4TdAvqtj+UmT73E6P/ns6KjnMTOW9r7HLPNIIN/dAZR+j8lnzn5vyeQGUfw/k8e2/CP9PeSWb8nNLxIZpR7HOB2ynOf0TUsD9byRea2CHjvGvJ7AzGMMXng/QvZc3FK6TXRTx/vddgORtlx/P+f5IPqbkEQ7mnzcvY7lP+vp06d7tq9atQpXXXWVn6lx+d73voeBgQF86UtfKmy7/PLL0dfXh1mzZqGpqQljY2O4/vrrcc455xiNXfVi5KGHHsJLL72EF154Qav96tWrcfXVV5dsf+KJJxCN2kctb9gwK//YidwPkVxxQ1jKm/x2E7C4ayLKx4wR5tGWzR9sKL4gJ+oY8wgAk30eqEbZvGKDulGlGRI8DwH2+jx582d8fDWC+hYzdDCPk4Id3pQNG/TOhTk6Bc9NocUHe84L4Rzog82bN/vqn0zWXvzTrl270NVVvMNoa2uTtLZj48aNuPrqq/Hoo49i0qTij+BnP/sZHnjgAWzcuBGHHnooduzYgYsvvhhTp07F4sWLtcevajGya9cuXHTRRdi8eTPa2/VuY1esWIHly5cXXvf19WH69OlYsGCB559lypQpa7FhwywsWTKAVCqG3JWVuIzI604ALUVFH0XOdNiZ/yMns+78804UTYsdVBvSh2zvyFtGkEvtjURSheJQESQRwRCiSBa2tSOZt4zkikgRF0u7ol1uykm0FwpN0cfJF5EaSSLKqbqazkSw+Z0NmL/3ErQMpArb2XYAvOcudh+9n3Ud0G3Yfez5UHY+MTzXpDVcHy0KE3G6LYLN127A/NVL0DLMcUvwdDLv60pbR6KC7Wy/qKINb3+Uacfbl9+WzMeMpFpzO1L5H8AAdfFL5rcN5R+JZYS0SaY7Ed/8eeyevwnZltHidkQxlL8VLumT3z5Usp3Moz3/6N2fSuXnkC8Hn+nLvyE284sEZ7OZYQPMa7oNu0/Ulzc+/b0sfEWKd+mRSAIbNryAJUsORipFTDHE8kEv4NkveM5rI3KX0O5wtg372qQtUCqKDGJFeL+T/G8v0pbGhpWbMX/+fLS0GGQ1MhBrei3R1dXl6/qm4qGHHsJXv/pV/PznP8e8efM8+y699FJcfvnlOPPMMwEAhx9+ON555x2sXr26fsTI9u3b8d577+HjH/94YdvY2Biefvpp3H777RgeHkZTU5OnT1tbG1cVtrS0+PqCplKZ/COQShGfCm2jbUbuR0UdoxE5k2Ircjdr5PZvNP83Bq9pmNx80C6bxtzwjc15M3ZzMxpamtCCRkSQxDAa0YQGNKOhcNpqhtdUPYYMIkghAyCDMWQLppeia6kBIwXR0ojhwmNTXqw0I5XLmGgGWpozxbdN5pinpTGFlgZBDADt2QLznLbsjjKPQKn4YC2p9M2wjzgRnvDQ+tYMqQUJALTsSaGlifP5DKPUF86zFtPbRgXPP4TXf04+437qGKqsO9YNQbt7GJdEc3PefdLSkN+de+zGUKGWSGN+gAaPm5B+Q3nXScsosi2jyOS/EBmMoRWDSCGCNgwgiSgi6EM/YoW3TEYYyR+XPA7nj0keh/LHTuVdQqn8byrT1OJ9jw3MI4F135DPNUG1Yb+7xcl5H3MTQ35iOeivBfcnlJ93qokSI+ykeRMnkIn3cbbJoMdTZdE0Ma/ZS4yP8u8d4FvqmPtUv+d6P33rkQcffBBLlizBQw89VAhSpUkmk2hs9IafNjU1IZMRLxnBo6rFyKc//Wn88Y9/9Gw7//zzMWvWLFx22WUlQqSqMMlc0LSeqhbIC5LAgwptUnp10e0jESI61g8V6V49QaKd2aKRzWI8pi0G45PCZmETRapgcakKyl701OQOXhSY2ofA0279YvIvjYc1ifpkYGAAb775ZuH122+/jR07dmDixInYf//9sWLFCrz77rv48Y9/DCDnmlm8eDFuvfVWHHvssejp6QEARCIRdHfnTk6LFi3C9ddfj/333x+HHnooXn75ZaxduxZLliwxmltVi5FYLIbDDjvMs62jowN77bVXyXZHKYGtJ2ODrNhZUGMHRBBChB7LWpCYCgo/AsRwdV4dTFbwDRJS3bWmkP00ufcBNr9lW3eD7RozOlk0LAZWkTpkCFE0+vjuZvgRz0JefPFFfOpTnyq8JiENixcvxn333Ye///3v2LlzZ2H/3XffjdHRUSxduhRLly4tbCftAeC2227DlVdeiQsvvBDvvfcepk6diq997WtYuXKl0dyqWozUBpW9q7C1YJhUWJWm9bKiow/yIP2gK43qjGU6hk+0BYkOrGiwEQxBixaHnAqkSRexFS2i85js/GYqWjTSeXlWEdF3N254eAdOOukkaWVUIjAIW7duVY4Zi8Wwbt26kpokptScGNH5cKqauGA7z1VjEfwuW6VUp1aI9iqn1bImh45VI2T3jGhcpSAJ07VSRhHhCp/5IBHEIEHUBtEh6LWEXGyGo4g7gziMKJSB94vtWjWmwakVECK+xi9XgThVv4re3dcJCb8DhFHvgue6CcK6q3LROKuIQ44TIzWGal2akvY+KqcGFnMS1oXNh5gIW4hoH0fns5GtXmz62Qb5v6gW61g9EKr4M7Gc0K6XIF00LD6sInHB9qpawNBhSs25aaqPKoxG1yT0Mty2a80EFfdRJXf36V6Ur9qrwxEKflw0AVtFLA9RjfSjE40+Jh+QnboqcJaRcUzoaZE6JxOTE06dRtQ77CDf36rPpDGxIGln0phkysisGOVw0bCEYBVx1DxOjNQYyaTZiddPvYf+oG43TEVEUKKjSsSLNJDVZo5V8r4qZRYP7HupQyLAsUJxa5kGr+q6XoJy0QRoFYnbH8JR/TgxEhYiF0FCsJ13onI++dAILPW21uCd5H2Km7AzaVJMCfkgyfTm3zz5rbGPEGwnjwmLg2qFYvGCV3U6hnFllo0ZglVkHLhnHKW4mBFffAj/gVz+SCLiif1IISpMz00i6iugterohq8g1pohaMuKDhW0vhDLR5DiozBm3rJI1qUJ8AA5yE1IQqOPThvriQA5YUDcORNRakWxcdEEfL7T/TfEgz1stZBKRtHQbP89zybZFZJrF2cZCQNRXKjI0qFpAQn8BMpB5X939STMKKsFJgwBYTB/Un2ViIiiqPDGdoj2B0nVxJPo/LalgdY8a4jIQqKb/cYTIbouGpNxWbOFpVUkLmjrrCJ1hbuyVCsBuGjK6ls3RffCWS3xEeUiqPc7Tt1Q5VgTxwqd37Nxcls5ip3R5xDWKuIz+NWPBq3iU5vDDidGykUI8R9+7vrovlUjWsK4gI43McOD9xnwPmvZZ1Wln2OKsbJUPdbxIqrsGdOaQLSQCML1UmariKPucGLEGtGP37BqYkKvWSHQrgJU7d0mi+FHFLYLRTl+lV7gpVTJnENJSxcFpwYZvErw07cEXSGiIzpoUWESuGpIGFYRV/SspnEBrOWgH2ZmxQSs7gj6ERNWTU0hgkiQRc46UZlsnw5UTTGzsjBO3S0sbJxJEJRk0thiE7xKMI4XMcF21V4eftJ5A7SK1JkQSQ60o6HBXpllB8JYMqAyOMtIoBiePAKqth4G2neeVXKnPG6ols+7TCf/VAAWkNAzaUT4LnZG4IkKVbyIrhCxcdGYpvMy6Hz8cYPxalSIOLw4MWKFwR1HIpwZqO4QQ6+uylItF8kqoSxZNEEUk/NZdyTo7KqB/JUlCNdgxTNpCEaWl0re6eq6aGQEZBXRGdpRVzgxEhiGJlGRebYMxc9CDfZT3SRVwu1QK0LJzzwr/B5JWq8KUVrvUIDCoexCXBffllDdNF/Rucg2+ybAcvK2VpE6c884SnFixJg9+k3LGNsQxJ2kagzdCw4A/2vO6Pb3KW7qshIr/Z78iBSLz0ZVYyQMwiiSVkBVmdWGRP4xlHgR2zVrdEWFrJ3CKqJT9j2uOQ3ACZE6wwWwBgJn5d4U+D8+cjKLC/axPzDOtmQyimi0WElVVYVVVnmV7csiC4rVggSc8gJPedt4VVXZdrIgVtE+SZ+W7vzKugGgJW50xRc7Vpg1SHTSeitsfQlC4JB4EWUZ+IRiILJfFLwaarzIHgANmm0JJlYRUW0Rn7EiLH7cM3UiRLJ9HchmfPywBlwFVkcB3o9c4PcVnaASgn2cbabBd6xLhr57FNUa4Z3k6X6FOAFyQjD9LZleDFX7dS/aHeJ9Ld38P1202/OunzZCpEPwXGQVCTJeJP9/J98DI4uZBuS75mdNGrb+iOkCk4FBfsPW9UUIPKHRr9hP4J2jgqi4SmNhFWGJawwro1bcsQ4uToxoQ34VE/KPfTAq1WxawTlRuolXa4QVDrJ4EJ3MBPrEb+17JxdE9o7a9OJnss/EimBw0hKJFGPRYnItDLM+ia4QZP+HGtiWgffjwmFdNIHGi9SMiyYoiwgQilXE1j1jEidSJ5aS8YwTI9bwfoyCk0JCMITPIFaZ1SP3WnxipvuqTuAeq0lQd8GyC67u3b0fa0q57qIk1hjuPPxURg3TKkL2VeikH6SAKZuLxqgEvGnVVR3BIrKI+LWK+Ky2qvPbc0Jk3OHESGAIThwiwZEQbNd11yjMzuzJ2mvx4PdVuWpoSlw1pHnYAsHEXRPE8WxRiRDd4+u6Z3QwtYpICNpFw6b0+nHRECpWX6R0IjkSJo1pZKLEJHNGVVNE1yoiIyD3DA8nOuoaJ0asID9O8uMVxI0EsXqvZtyIrhWERSU66HGNLgwiVw0P1QUxCMuCLI4kaGxFlZ+A1TCsIgaBq9XkogkkcyeMEvAEcoNiFbiqg64QCcMqosDWPSMSIrUuUAYC+KsTnBjRZoJkH/tDl8SNyIJYNbaZxo2wIkUUN2IiOsjdsO+CVyaWDD/uGp2xgxAlOuNEBW10hIhO0KqIIKwiTOBquagqF01xUjn8uGgK+AlcpfkQdhYRoCxWET/uGR61LkQcHpwYMUb1a7GIG+GdWwKKG5HtUxU/017Zlz0p2LgnTC+Mpu4anX4dBn+8frbYpgOr2oUcF0NEqd/6Hux3y7loePBERp9iPyAWIiFbRcJ2zzghUnc4MWIN+SFq3HWUKW6k1AqiJ0y0RQdCcNXYWkdU+7oFY6vG0UUkTERtRR+bzvuXvTYtcGbrotE4+ZfTRUMIxUVDEFlN/FRStXbRyDAVIiw2VhGfQatxjSEBMyFSjwUMxxFOjARGF/OYp4xxIzSsqDAtAa9K8S1x1Zhe4MOwjpj0D8o1I0N1DBshYnJsk+OI2jGEVVtEhEjA2CxpoO2i0XWxqFw0RrVFTANXVQRYwt20T1DuGRPrR9ygbTUxCH/xInW0grkTI1boWkUEvuAyxI3QyOJGbFN8hbBZNTqxEKqLpezuX+dEZ+u28YPNuKaWDZOgVdF20xRfBr+Bq2wQ6hDaPa9tqBoXDSGRf9S6cOiu0CsTKiZCpAxWEZa4RhuTgFWd8Rwe7rjjDsyYMQPt7e049thjsW3bNmHbdDqNa665BgceeCDa29sxe/ZsbNq0qaTdu+++iy9/+cvYa6+9EIlEcPjhh+PFF1/UnpMTI9rILsQCqwgPk+JnZYgb4U9FvxqrNkFeCG3SdcslSHQsLjwXks5nYTNPP1YRxkVT7sBVFRV10fi5I5W6aAi6gau0UGFriNB0wb7SqqyfYdBqnDOErpHLxYkEwk9/+lMsX74cq1atwksvvYTZs2dj4cKFeO+997jtr7jiCtx111247bbb8Oqrr+LrX/86/vmf/xkvv/xyoc2ePXtwwgknoKWlBb/5zW/w6quv4pZbbsGECbLEDy/VdXapCQxiRWgGIbZ88E5sIcSN6JaG5+3XctUQRDEifmJHZBdv3YBSVRyJ3wBWHbHAO6/bCBEdq4gqnsRSDLKBq0GvAq1rTdEay3QtGtNUSbYf66JJQILMRUPgWUV47W3cMkFYRSjCdM+IhEhc4xgOD2vXrsUFF1yA888/H4cccgjWr1+PaDSKDRs2cNv/5Cc/wXe+8x2ccsop+MhHPoJvfOMbOOWUU3DLLbcU2qxZswbTp0/Hvffei2OOOQYzZ87EggULcOCBB2rPy4kR34h+uOSEkebfCYlyxBOc7Zxtmd6OEvNzyiMeotopvrx2IusIa1YvXJhkd80yocG7eOpcXHntTEWJn4A3XQFCjiP6mujMOUghYvq5G6xD49dFY4LIKhLoWjQVC1y1sYqI0BEiuv0N3DO830VcYwpOiFjR19fn+RseHua2GxkZwfbt2zFv3rzCtsbGRsybNw/PPfcct8/w8DDa29s92yKRCJ555pnC61/96leYO3cuvvjFL2LSpEmYM2cO7rnnHqP34MSIb/qY5/Rrzgkkwd/sJ8U3mVQJD6+YEFVjTTEneHa/1rof7MWOjR3xc2Ek+01jSGSiwa8oEY2nGldHdPDaVECI8Ag7VqSiVhGVdUQ3cJXs52JrFdFF5pZhP0ORVcRAiKhODXHFfmB8CpGAAlinT5+O7u7uwt/q1au5h/vggw8wNjaGyZMne7ZPnjwZPT093D4LFy7E2rVr8Ze//AWZTAabN2/GL3/5S/z9738vtHnrrbdw55134qMf/Sh++9vf4hvf+Aa+9a1v4f7779f+KJq1W457YijN6yc/4j54f7jsaxTviNiLCzmBxQXbO+Xbkv0RRGPF260UooggWXjdjxhi+RNeEhFEQbeNIJJ/Tbfj9aXHJduTiCKKJFJtUbSMZosdO/Nz7YDXBdUNoJezHRrbRGORfWDaAvzxeNvJeKaQ45v07UBpXLOOu0TXTWUS1KoZT8JaRfzWFbFFVXE1lBV6E4WD2xOKVUSGiZvFpsBZCHEiQQiRIGO/aoxdu3ahq6v4/2trawts7FtvvRUXXHABZs2ahYaGBhx44IE4//zzPW6dTCaDuXPn4oYbbgAAzJkzB6+88grWr1+PxYsXax3HWUZ8Qd+5sCcR8rof3KwaUTXWfpTeUQ3wt7FZNax1RBYT4tc6UuKuaZVcAGQXW94FU3ZB1XXb8Nqz2/2evHSsKqpjmVpD2D6mQkT2GdPtNIJWg7KKsFWBg6hH4tsqIrKOVNQq4kcRxWBW3lRmFaEIK2DVCREjurq6PH8iMbL33nujqakJu3fv9mzfvXs3pkyZwu2zzz774JFHHsHg4CDeeecdvPbaa+js7MRHPvKRQpt9990XhxxyiKffwQcfjJ07d2q/BydGfKMjSChCCGRN9kc8d4Oq8vDeeJDS2BFR0KvORSHJLqAnctfw9qna8V7rZqXIxECQJ7IO6Ikd3diQMISI7JgA90IgClrVct0FQFVYRWwyaEKzipi4cERXfx33DIthGq/OVNjvmxMiodHa2oqjjjoKW7ZsKWzLZDLYsmULjjvuOGnf9vZ2TJs2DaOjo/jFL36B0047rbDvhBNOwOuvv+5p/8Ybb+CAAw7QnpsTI9rIUpRkgoSQDi2QlcYkY0a1kq+pdYRQsqKvqSCBpB3vNS+OxNQiwooInT+dcVl4a9PoWkNMhQjbn22viBMxcc/4tYqQ18XVe6vQKkIQ9QvVKmJbAE1HiOj29+me8VPYjB1LdEyHkuXLl+Oee+7B/fffjz//+c/4xje+gcHBQZx//vkAgPPOOw8rVqwotP/973+PX/7yl3jrrbfw3//93/jMZz6DTCaDb3/724U2l1xyCZ5//nnccMMNePPNN7Fx40bcfffdWLp0qfa8XMyIMRNQ1HD0SUMUQ0JOIswvMcHfDCB3nmK3D0AdP5KMIhotxovQMSJsTIg3HqQ0doQXI5Jrm9tOxi62N7xDpmM+CCQuhI4PUcWGiOJIeO3ZY4n26aJzIiRtRL80lQjhtdEVIgHGidCEXfZdJWDKYhUhVKVVxC+sENENWqUIS4iYFDWrByGSADDio39S3YTljDPOwPvvv4+VK1eip6cHRx55JDZt2lQIat25cycaG4t2iqGhIVxxxRV466230NnZiVNOOQU/+clPEI/HC22OPvpoPPzww1ixYgWuueYazJw5E+vWrcM555yjPS8nRnxBftQfUo+ioFYKcsFlSeQf6X2iQFbqdaa3A0mgEMhKTs5ETJBAU6B4Aicihd5HnouCXgsBq0yQLE2qNYpYJoXBrkZ09GVKg1l5QoPephIkELSHoC1pA4QnSnjj2bQzLYSmK0QCiBMJwj0js4o0GY/GzI+pturbKkL2s4j6sVYR7vfJxCpSaSHCYpDGG2deOyFSdSxbtgzLli3j7tu6davn9Sc/+Um8+uqryjE/+9nP4rOf/az1nJybRpsY80gzkfqTuWwMXDUGgaw0siJosvVqeJYN1QJ6vJokAKcYmshdA5il/JoEtoosAEG6aTo4fVWIXDKmwbi6n4VMiNBoxInwCN490y7tF8QaNcYEUm01zdnJWkVEcSA6LhqdGBKTQo0G7hlTwhAiMcEYjprBiREruiDO4+f94PtgXHOEhRUrHAGjKoLmaSuMByk9yfPaslk3A7pnAlmMiM42E7eETsVVv8jGEMWY0KhiQ3jH0E3fVX2eGnEiNGGsuusH0Ro0gVlF2N+mqJ+WVaQ46xwygeHXKqIbX1IB90xYQsRR8zgx4hvVmg+cE0MKYsGRYLYlwA9kZaADWWVF0PoRk2TLeO9Wc9vE1Vd5kDGE1hEaXfeB6mIsa0vaq6wlQaFrJbGxhpB+vPY6cSWKz8nWPePXKlJiWdO0ipSF0KwiBJVVJCws3DNBxImwjHch4lbtLeDEiDa0m4b349VZiMqg5givIivPncNs82sdMVnRl1eTxDOWyl1DP9cRJCI3haotjY4Lx8+fCHotRVtriG76ruxzYttz4kR01p5RuWeCRhW0Wj9WEaD0CqtjgVUVL7NZeybkOJHxLkQcHpwYsUYkPtjtH6K0TDyK1pEE093EOkJvGxBbR1KICq0jdN0RkbtG1lbkrtESJKzoEAkS0cVWldIrEwpBrE8jQ6csvEiEsGJCt9aIqB/vs+6ElhChUWXP0JhaRUisyJCgnw6BCxFRgTMdSqwiPNeLzCrCEySi6qkioaFztRa1N4gTiTOvgxQiIpHvhEjd4bJptBHFRLBl4OlH9iRDp/lSP/aE4DDkZEj/8NjsGuY1L7OGzowB4Mms4ZV4B4AIUiVZOMJy8IiiDWOFKfLKxUeGk6UZNkDuRENnz5ALKC/LBuBn0dB9eG3BbGf3hSVIaKLUY4bZJ3ItseiIELavRYwILQB003h5bfSFSMRzIjJN5WWtgaGjsooYBa3SkAFpK6wqfkRHiNisPUNhsxovwU+MiOg4oqmW43fsCA1nGTGmE3qyXCRECGn92BGSWTMgaQOUuGtEVVnpFX1pq4lOZVZRwbOhwoVGUC6eZyHRzbIRWUZUVgSZZUQ3tsMWHfeNzGIja2dqDaHbBShEaPwIEaBoEWEtaxVzzwRS9p0gc8/wUnnp57wy7rzzT5BCJMQ4kbCECG8MR03hLCPWkF8Fe6JhTzhk/0Rmv6AIGlD8wZJt9A9TtYDeAJBBBxq7c2fTZDIKRFEoZAYUa4yo64kULSm8trQFJILRwnR4+7kWEmrOHgsJULSS6BQ8Y/exlhIw7ei27Paw6UCpZQQwrzFiag0BtIWIzNqhI1ZMhQhL1QsRHkKriKl7hsBWPozBe27pgjr2xGe596AzZ5wQKWUQoIzK5gwFNZHK4ywj2vCCuURCRFS4SNM6olN3hLRht+XJ9HZ4zNc6i+iZxo/Q49pYSLQDW0VxITKrCd1XZm0ol2VE5IrRKWUvs4zoWEMAT3xINQgRmkCzY4ISIjJCc8+IbmQIosBW0zgR0ZhOiDgqhxMjxuguFMWaXdliaMyJhgSz8qL4B5nXGsGsBBN3TXG2xQuKLN1XWPRMIVjoi6BWYCtgVvTM5OJPtzf9k/UXQbJpdDNqVGKL3idqpwhUDUuIyGD7BuaeCYoEvOOWxT3Da6uKGQnKPRNg5kyQQoTnpSL9eWM4ahYnRnQpsSirrCJgtotep0urspJUX/oEq5PqS782zK5ht4vqj7DiRZhFoyFIpLVIdOJCVBdrmUAIIptGJjxYqwzPMq4SNqLjmFhDNDNm/AoRHir3jKiK6vh0z/QJnvPG4l2dAxQi9Lmu0kKEB6+/o+ZxYsQXKvcMe1LRtI7Q6KT6BuCu4V2M+hGT1h9R1hlRCBJAM7CVPJdZBXQsIzrCxORP1l9EVDAH3W061hD6M4Q3PkQmRHLfBzshYhonInbrCMrBl1uI8AjdPSNbSoInSOisG0KAmTO1JEQ6BeM4agYnRnQh590IkLuTEBU/4wkRVngYWEcICZRaR1jLSAKlIiWPyl2TO2RpTAi9P8URL8X27flD6ltIhJk2AN9tw772U2ckiJgRnRoi5E9kQNARIazI0YgNAdTxIQA/mLTcQqQqCbS4Gf17Vy2EZyJIAP8FNyRxIjRxxWErLURqFT/VV3mxhTWMEyO+IIJEFp3OmmQl1hFeITSTYFbea013jW1AK92+OD15YTSvFUYjsBXQD+Kk2+oKED/xIqL+IkTteNt0Cp4prCGAOFC1EkKEBxtjUnGriEiI8BC6Z3TjRHhWEZkgkeHDPSMLWI0z+8otROKC/sx33lHbODGiC7koREFZR1gmwvtrkrllONYRmgRKT4IJyINZfbhr2O26Aa1k31DhItZe0oceT0eQAAK3DeC9AOtevMOwjPgRHrw2NKa1RgTWEEA/YwYIT4jQ1KR7hkDaaLlnCDpxIrx9phaSEANWaSohRHg4EVJ3ODHiC9pdI1sPQmYdAbjWERrdYFZCAqUiJY/IXaMTP1LsI7/oCINWOYJEK7AV4IsS+rlKlNDtgraMiMbgERW0kaX5WlpDTDJmghYiNKo4EdZqEpgQsUHXPcO6VT2NbVfklfWT7StTwKqpEImjVEzwfhuyjBkeIiHiBEpN44qe6UK+6N0oFprxnJB4YkSUSRNjXnehWOQof6KgBQlpTk6Q8fwjfV6jS6zzSsVTxdAKFpJosRhaBMnCRYEUOQNQ2E6KmJH9KUQ8JeNJrMgQomhHn6cPWzreeyxvm8L4VIE0AN4iaeS90Sc1tmAawBckbBE1ur8JKosKuz+L0gJFqpLvom3MSVdkCQH040Potjol3un2PCGiG7AqTPENIoU36Cqr3O+IjnsGyAmGD8Ev8U72dTHbaLqQ+xIBZcmciTOHtxEiLGFZQ5wIqQucGLGF/LBSxDoiS7/7MP83Ed6TFf2anDzSuTHJSSJBNacFBz2HhGB/HEJBAqCkOquJICEQQdKJ4cI2eiyRIBGNy1ZsBeARJQCK69uAen+0EJEJDVYAiMRJ0HQhJ0a6UGqP1BEggG8RktteauXQFSzcbCiJlcxEiLRRZWm1LSL08wT4bYISIjRacSIySwY5X4iqqPKECI8qjRFh+7Pji8YV9eUdQ7W9VuiFvyqqw+omtYITI7p0Uo/D4Jyk2F8WccWwK/iSEwhdJp4+IcVQECRA8UdMmrOl4kmXBLOftqIwFhN6MT1bQUJbMgCgEU35w3Yiw9Q35okYWpQo21EX2hJrCVsWn7WWAPwTocx6woMtLc9DNpbO4ngaAgTwJ0IAc2sIvZ+X6i0LVjW1iKRSEaC5SoQIjVU9ERZiAaEhgoS3j+wnxKixRUKEpgqEiF8RwjuGarujJqlqMbJ69Wr88pe/xGuvvYZIJILjjz8ea9aswUEHHVTpqXl/PKkWeIPY6JMDfQdEn3CIawYQrltDu4HiKHXLAKUr+zKWkEJf+iTeaSdICDxrx1A+ADGJKNrRX9hPX9CELhlN1w0AO2sJQbYejcpNY1ocjR0/Q21vlLQDlAIECE6E0O1lIoLuo+OW4Y0hq67aTgnYiggRHqSNVT0R+saDB8+aCuiJjAmCNrKsPgHVLEScCBlXVHUA61NPPYWlS5fi+eefx+bNm5FOp7FgwQIMDpo6+AOADhrk5sHzao/Q2TVd1DagtPYAfTLrh+eEJ0r3TVD76XRfJrVXlAZMZ9iwKb8AP6hVtnIvQVyLRBK46gmi5Ldja5Ow2TclGTiiTBxVam0QfyxR6pHXjjNf8p5Egan051L6eUWUQkSV3usnPsREiAB5iwjKLERotANWaSGiqsL8IUqtHTyBwp4b6G0E3klHJkQ0rCLVKkRkKbuy7WG7Wh2hUtWWkU2bNnle33fffZg0aRK2b9+OT3ziExWaFfg/iMLJiv7F0ZYOYiGht9P7WbdOPqCVFz+iCmiNg28hYV9bWEhYawehKa9rh9COsbwZgO2jFbgqaEfgBboSuNYS8p5peHE3Ikx0r2ysMc42DQsIILeCAGJLCGBnDfHu9xcfQreXlXmnV30umxAxzpwxESI0ZBsrRETWkSoSIjapu7xsGVUf2fiy7ap91c4AgBEf/f30rTKqWoyw9PbmHPcTJ4rNn8PDwxgeLkb19PXlThLpdBrptKwegJzIXrm+kXg6d7JqBdAGoD3/l0T+pJUGMIrc1acDOfv8HhR/MSRrhj6ZZfPbMsgNPoZC7AjSAFpyu6LIBTu1Ifcl7Mw/DiL3n+zIz6EJObdCEkADdehE/lCd+cNn8n0+bEV2dBTRWArZvlakI7mLRRsyiCCJMbRhFFlEkcQwOtGJAYwiinYkMYIIohjCUDp3xhlOd6ANgxhBRyGOhDw25L9u7UgiWThD5U6WnRjAIFoBANn8YztSGEAbgJwoAYDe/OtODKA/7xqK5Pf1N+bOtpGRnEunl9FMUSJO2lGKSHTw2opgBQd1Uk6P5eaWbo8g/3EAo0CSFR+jQKqVmXj+a5tihMYAdRZmRcgQ9ZoWFN4+7VT7KBqo/Y35fo1AwQXXlN/WlG/XTI3RnB+jJb+vFdnCcVuRRQrtaEOmEKw6gE60YwypVAQRjCKbyH0P2va0INOULv4/6P8LEQ291L6G/PaG/OvG/P6m/PZmFH8fvShemweR+w0PUI9t+Tbk9xxB7vcUAbxChFUvtKDIMtvoK/H/oRTyY4xRj1lqf/F1JNINoA+RSAbFL1sMoIRcbjwyV8p9TH896K8X637sFDwH+GJbVpSPNwavj2x80RicPpH8e/Vzng+iv8OOhmw2m1U3qzyZTAaf+9znkEgk8MwzzwjbXXXVVbj66qtLtm/cuBHRaOkdvcPhcDgchGQyibPPPhu9vb3o6rKIw9Ggr68P3d3dwHm9QKuPY4z0AT/uDnWu5aJmxMg3vvEN/OY3v8EzzzyD/fbbT9iOZxmZPn06PvjgA1//rCmXpLFh/mYseXQ+Uv/Xkruzos28vWCsI2QHe/e0hxpVtNbEBHgXwepE4ZYugtydDbm7IP5VcofQnX/eybwGpx39Ov/Y2JVENJazNEQiqXy3AUTyAaSRfB4aCSjtzH8InekkPr85jk3zd6O9pd/Tpp3pQ/drp7ZF8+06Kbs83aedqTQVZV53euz5RYsJC7GcqChYUgwosXTkGR2N4JmtP8KJJ30Vzc25eZVYQFBq/SAMMLeHupYQui9rCeGNTfelg5JL27ULx2Grquaec7JmUIwRadvTgg3/sxlL9p2PVGNL0SJC/0tpiwi9b5DZL9rO68taW+ivRol7hpduw/6uZUXNAPHCK7LSALmA1Ugkgw0bdmHJkoORSsWp/azpQOCaCcoiolMfR9ciohnALW2fJ9KVxoaxzZg/fz5aWiQVZhX09fVh7733dmKkzNSEm2bZsmV47LHH8PTTT0uFCAC0tbWhra2tZHtLS4uvL2gqbxVNtbUg1dGSOz8R8+4wcqbdQjE0Yh5tRu5E0oeck7QPwF75RqTIEa+UagNy9uYuEAN47gSTn38qvymen8OHKLptRpE7sY6iGIqSQdEaTNw0xKzdkD9sY377YDdSzc2IxlJIpWOIRpMYQjdiaEQESQwjiihSGEEHokjiw3zcx0D+TNfX0oahltxFnMSOkEeS8htFEn35s2Q2b2LOxYmQr2Pu/5eLNWku9Bku+Ddy7YfycSpElCTzn08s/5mOFN4cCmMAQLrFuz0yzBcnI7JkCAG8H1SqLYrRdO6Y6WgDsvnjNxfmXbxKNOW/RGwGU2O+oAARIY0Y9sR4NGCkME5D3pFcHCOd75f7rHP9Rqk2Y9QcxihBlMlvz+TbZbUDXaXpwcloMX23ORcjkmnKXfBTjS1IJVu87hegvDEiuYnnybtJ0Y/cf7cPRT9bH3I/nA/zE+3PP7KpvfRFghycFSX0d1JU1CwGYBdSqSakUs2ctkCJECHvowPF81Mc3voUMRRjDzrhjUOgvT6kL+0VIuKA3pb7Snn7AKXp7Z3weqRE2+h9PMj4+X5+z/V++jrsqWoxks1m8c1vfhMPP/wwtm7dipkzZ1ZuMqrocVpTRMAUQyOpvXQAK12RkR0A8AbCURVaSUCrqkIrnfKboN4DryiaIqg1GuXXGyFpvf2IYSI1f3Gxs2LVVoAfFCsrhEbgFU4r7iutVULECWsxYNOFbYgMJ43780qmA/y1XHgLzLEBxKK0XrY/Txiw/f2m7dLtuGJFVNCMjhEhQoSQyD+aChEaayFCN+YVNVOtwku3ZVP+Aa8FtB9iIdIFb5wIoBQiBFGwajkyZtg+vHFtt4vGryWSkC9tpKKOwluqOrV36dKl+I//+A9s3LgRsVgMPT096OnpQSrFN7+HCu0W4aVkxpD7YXh+oESQAN71a+hUPnLCiYEvTPrgPQmmvfVHEvnmvFReeg2bBLwneLod3ZdJ+6XXsulHrCT9ljXhDzH7ecWyeFkZ/LTi0hRf0o+XEkz3Yxf/o//YcXh/uvCECG+8VOECXboOS+m8St8D/73y03rpMdh+vHRpMi/Z/0UnbZdt52mTjOqXeA9KiIj6hipEVAvj8Ra9I38yIcLCZsxUWIjQpy9RH964ZJvJdjI2b3yHFnfccQdmzJiB9vZ2HHvssdi2bZu0fSKRwNKlS7Hvvvuira0NH/vYx/D4449z2954441oaGjAxRdfbDSnqraM3HnnnQCAk046ybP93nvvxVe+8pXyToaOtaBNkIn8ozDdlxYkdHovgVhJAO/aFQYVWhPUbrbYmUlRNI6FhF7LhraQsIwiC2AEA+hEqZPMC7GSsNtKS80bpPhSF0QS38JezIk7h/ceYvDe2ZoIEhX9iCGT/1QG0IlGzoqpOhYQ3rx003pVlhB2u2n9EanVhBEhAEeI0LEcDZC7V2pSiKjQESKkDX2ysVhrxkSIxJl95bCG2FpCOqG3+vI456c//SmWL1+O9evX49hjj8W6deuwcOFCvP7665g0aVJJ+5GREcyfPx+TJk3Cf/7nf2LatGl45513EI/HS9q+8MILuOuuu3DEEUcYz6uqxUjVxtbGOdtEruCCIElDT5SEKEh0YFw5JYvrAdxaJK1U5B+vtoi4JLxalLDwxvKOWXoRZ9059FhkzuUgiQgaBQZJHfEB6Lti2LYmIoQeV1V/xMotw6sdQkhwtpeljgggFiJskUJeG3qfKOBIZeWQrTdDB2YAViXeZULExhrCwvbhjSvaJtvOG5ftU6WXi2pj7dq1uOCCC3D++ecDANavX49f//rX2LBhAy6//PKS9hs2bMCHH36IZ599thBPM2PGjJJ2AwMDOOecc3DPPffguuuuM55XVYuRqoJ84TvALyFAYAuRAZQgAfiipI96ZNeosBQkNHGoY0h4dxX5/Zn8WYgtjkZDFjpLIloSOEpjI0pEhdZE45W2MRMoOrCZPKpxMhx7ka344B1PZAVhxzQRIfS4VkGqUFhDeGKDzYjhtU0UDpZDVuK9aoSIzvoxPCFC9xNYRETWEEAsRIJwy8ja88aUbbexhtRysbOAIfW0CKJEjpGREWzfvh0rVqwobGtsbMS8efPw3HPPccf+1a9+heOOOw5Lly7Fo48+in322Qdnn302LrvsMjQ1FZMKli5dilNPPRXz5s1zYiRU6PRYosATzD6afpT+iISiBPAKEsBbGM1CkMRQGqyqE9RK2rLbqMBWD/nrEBEjKbSjCcOe4FaemLC1lPCCXWVjssGv3mPpuWIipe9aKj5445JCbkOIoIHKCtIVH7xj6lpBcm313TH02KYuGcDQGsJ+z3j7WGtI7oA5VIve0W1JG6vKqoBYiNA3DzZCRHf1XbqYWZ6whYiNNSSIAFV2TN2xao0ESjxtRuS/EtOnT/dsXrVqFa666qqS5h988AHGxsYwefJkz/bJkyfjtdde4x7irbfewu9+9zucc845ePzxx/Hmm2/iwgsvRDqdxqpVqwAADz30EF566SW88MIL1m/FiREb2B8CXYadt539QZeIEoC/nHjAgkQFT5hI3DZ0tk2hfgSiQjeEH1FCtosu1DxhAojjQ0QChYeuaOFBjkPESBLRQiVagm4WDW8uugKE3a8qB+9tI3bJeNolqXY2a8x0QFwHhLSxiQ+h22itvisr8R6EENF1y/DaU/gt714ua0gYLhkeMutwnbNr1y5PnRGeVcSWTCaDSZMm4e6770ZTUxOOOuoovPvuu7j55puxatUq7Nq1CxdddBE2b96M9naTktVenBjRpRs5l20HvK4NmkGIf0gJSEQJLToAr6smQEFCu2Jod1McfBGS4Gxj3DaE7GjOnz2ATrQokrT8ihKyT1eYqI5D5hRk0KqIgZLCDTl0xQeg74Zh9+ms5isSN9bWEEDP5TKI4m8ry7QjbfzEh9D9rAJVee3KLUQ4FhFdawgQrhCppAgh+8xrFNYNXV1dWkXP9t57bzQ1NWH37t2e7bt378aUKVO4ffbdd1+0tLR4XDIHH3wwenp6Cm6f9957Dx//+McL+8fGxvD000/j9ttvx/DwsKevCCdGdCEXZ7qKIMlAoRmk2hNol00CxTtAuihRgYAsJGx3lSABtd/AbUOsJJFIcSBRACt7sdcVJbntRWESYcbSCXoVZdjIjhksLXnbCITzEFlg+JYTcysIe1xbEUK3t44NSTDbGjht6HZBxIfk3kSeIDNmWCESRHwI+z9n1plJIRi3TByl0OMGaQ0xFSEqV0y9uGrKSGtrK4466ihs2bIFp59+OoCc5WPLli1YtmwZt88JJ5yAjRs3IpPJoLExd6P5xhtvYN9990Vrays+/elP449//KOnz/nnn49Zs2aVxJXIcGLEFFK9lMALWAX4Kb9ElCTgNUkDKP7qycmODWSlg141BAlbGE0FHUdC0HTbAEAybxlJpSJIpZtLAlwJNqJEZrXQESaiAFiZQJHBBq8S5OM0oxWkjHruZ2ciPnLj6wsQdpywRAjgwxrCColJ4Fs9Ao0PAewCVen9QQkRS2sIEMyqu2y/arGGOBESKsuXL8fixYsxd+5cHHPMMVi3bh0GBwcL2TXnnXcepk2bhtWrVwPILcVy++2346KLLsI3v/lN/OUvf8ENN9yAb33rWwCAWCyGww47zHOMjo4O7LXXXiXbZTgxogvrj4wz+0WpvXT9DnofPRZ91+OBFSTkABaCJE51UaX+xsEXIQkIyYwV75BTzdTXKkBRQtroCpNcf77VRJahUzquN5ZER7ywY5M4kSFECyXwCTqWj+K4+gKEnWsQIgQI0BpCP9IfMWkXWnwI3cE2YwYQp+/SsP/bAIRIFN7S7qJDVYNbJkhriGrfmGR/tUJWlLaFzfbW4IwzzsD777+PlStXoqenB0ceeSQ2bdpUCGrduXNnwQIC5IJjf/vb3+KSSy7BEUccgWnTpuGiiy7CZZdd5mPipTgxogu9oBxdrpr9gSQgXgsLKL3Il7hq6JLxgLcgGkElSPJtWJdNHHxBQjB123RSj/kTf6YvCkwcKQlwlWXV6IgS0lZXmAB8cZIbR+zWYYufAf4CWAkkwHcAnYU1emh0xQevrUyA5MYogwgB7Kwh5JGc+0hqb+jxIYBZoCq7XydGJOD4EJa4oCsgFiJxpl09iBCHMcuWLRO6ZbZu3Vqy7bjjjsPzzz+vPT5vDBVOjGjS2JW/deuAt4h+AnxBQtrSbdhUW4DjqhGtcSFz25D99BoYpLx0CycuhYFn9dB12xBI0OEgkGkqDXAFULCS+BUlpL1MmIiCX2UCBQiv+Bn9lTERHqL2JgKEbW8rQgBNlwz9PMHZpqovQu72QosPAewzZoBwhYhEhND/0rjgUNXgljFJ1TV16+jud9QcTozYIBIfspTfBLWdqzlYVw1rIQHkbhuyXyFIVKm/cZi5bcg++sSWt5bQAa6FYmmAsSgRtaX76FhMTAVKkDTma4vkjpOzjJiID8CfAGHHDVSE0M9NrCF0GzrGLTC3DFCe0u5+hIhhfAiQC6Ifhr1bJojaIWGJED/7HDWNEyOaRGO5M1xjVxIYzKfUsD8M3ZRfNn4E4KxjA3hX+iWv2bsyeuVf0Ykzv1+W+uvHbQN4LCN0gC+bBmwjSmSFz1isVu01DGCNSExNorGa8raRIbRjjMpBNFnBN9feXoCw/U1ECKBRyt3UGkLa9aN4JrKxhtD9tOJD2Ocson2qYNUQ4kNo4UBn8tWqNSQkEdLYPYjGUYsAikozCK8QN6UW42QEODGiSSSSO8tFYymkmptzF1leaq8uSkHCW79Gxoec52xFV0HqL4uu24aF1IngVIPn1SYp4MMoYRKI6hdd8cITGESMJBH1iBFAX3gQwhAgQICWENF+nlhhY0N6UdQRJtYQQNMtQ6B/G+yaUOw+Fr/1QzRjQ0QZM50ARqjn4Dxn+9SiCNEQII76wYkRQyKRFKLNzUgCYkHC29YPftl4HinWQkIG4C2ux65pI4o5IXACW4OgAbn31ytvlqE+hKCFSViILCsy2At9c16hDaAzv8KxF5OiZ7zxZQKEHcfICpKbdOlzlQjhtaXbsSKEPhPppOzSfY2sIaw4ENX04e0jfdj2NCb1QwzTdmXCw9YawrZVjS3bpjOWzj4nQsYlToxo0pk/M3ZiAENR4v83FCQsoqJpEXAEg0iciNa0kYkSxm0TBA0o1onQXD3TxlpiuridrMaIzjh+CqLR4zcXtrULs/H8FD0zCmBNMn11s2PY5wnBdplgob+WtEumhdlm5JIB9GJDyHOeIOFVPaZ/VzYihH2uCFI1KWLGs4ywfdgxTa0hVSRCVAIkGkshUotuGkcBJ0Y0oWtNxNBfuFgWBIkOCYjrkMjwrGVTnEURkbnZ0ErCW9tBlkFDQ9JFBqAnRqhxyedHgl1L0DRIiIJfbfGzqi+BiALyKQ8hyq0oA+gXOytuN4wfsbWCsM8TFm1FIoTdNoDchdbYJUMfhFc7hMZUkLCU0RrCOxwhKGtIOYJTLcSJjgBx1A9OjGgSyVcZiiCJYXSWCpLeDn6WjWkdEh0K4oTNvmFPqEBpNg4v2wZmVpI4imuJkCGIe2YQuc+FrUOSUA+rdOEAZXPjBBmL0o8YWpEFMIIBdGKEF1STR1d4AGbiA0CJ0CuLCMlNLIcsS2aE2he4NYRXH8REkIB6TROwNSTODM9aQ8h6Ah3Uc7aPSWyIjmhwIiRc6Bo7NtTRejxOjGhCghcjGMIw/e2xESQsbBveWjaEkjVtyNnG1PfNttUMbhVBvkm9COQHoiVMACtxorKg6Kx1Y0sSUakYybXxsWheEAKEfp0QtAlKhJDnrdRzegwtEQLIK6mCs40Icx1BwiLKqgnIGiJL2aWJM6+DtIaEJUIsXTE6AoQkGThqEydGNOnMnzGjSGIEDUgiUnTdUIIE0HDbDEC+QqWIBJg1bVTWERNBAviKJSHfpAEEm27W6b2AmggTG+tG0K4ewmjeMkJQuX+0A1eTpe20BQj72iQeRNSe544RjUfHhZAVz2kLG8E4XZcnQkh7Og2etRSSR56FkVBmawj9nP5Z0v38WEN0hESc00a3r2o7xCJER4BEo9QZQeT/dNQEToxo0o4kgFZ0YqBwZ8sVJP0RNHYPegWJjvhga5GQfuyJJZF/FFpHRIKE7NdBULk1DnEYCh2AKIoj040/kUB/rsIYE4KhpggiRkQESedNob1gWZMJJWH8iKn4APQFCKCfnsu2tREhdD8iQpIofu+MrSEyEUK/ZgUJGUMmSMpoDVEJhm7kfmN+0nVtRYip2LBwxahEiEeAOOoGJ0Y0ieZjRtqRFBvwTQSJaY0S+gQutY6IoK0jdOaNZqE0FcTM3gv9FGbAl0BhLVDap6gKpA9nCmIkWhAjKlcQT3gAGuID8C9AZM/p9n5FCGnXTu0LxCXDihA2Vgoofv9VgoTGoICZX5cMT4TwxvObJaPjkqmwCHECpP5xYsSQKIY8N/5JROFZUj5K7zPItAG8tUgSKP6gyUk8Ru0DBIvsyawjPEHCwrp0NEUJESOD8G8u1Qx4ZdERJ54KsPT2aHhxIgCQzvuuBtCJIarkokhwFPZzLD9a4oPdJhMgsra2IkQ0hihLhsQZ+XbJiEQI/bqLaqsjSEC9ph+BUKwhOum65CRUjrgQn+6YMK0g9MKWbbUYzZmAC2DN48SIJnSdEbZoFU+QJJPR3IUP1EWStYboWEcShQkorCOs4JC5awC9NTcIGgGuxOdPByMGTRxiKwr7+XTyL9qVur/K5msgpFIRpNLen53M1cQVHoBafPBeJyT7dQJYTawg7HNfqbqA2hqiEiGkPRu4SgsScPazqb1ltIbEmXZhWkPY/rw2JtsQrhWEt7q2o7ZxYkSTKJg6I+oOfEFiAh1HwrOOkCE9sSMqQcJiKkrAFyXEzB6mGAkA0f8hbJFCxEiyP4JUc+nPTig6ALEAU4kPwE6AsP3KIUI8eiwMEUI/FwkS0X6grNYQuj07FmlLgsTZtlUsQoK0gjjqDydGNGnPx4zk0j7bS34Y/Yh5BEu+cQFrQUIji68oCAQbVwO7ZsZElN4Z0hkCaeaYKJoLe+H1/9cItv+bxu5BuZAg44/lPrNMXxSZJo6Yk8XN6IoRQC4+VK/ZvkGIELot1xJC4Pn2VMIC8H7fRUsmyNaXUa0pUyUiJAbvgmps2yoVIeUQICSJoCbdNI4CToxoQoRGO4ZKFjrT6AzAIPXXBuK2sRIltAgBvCdiTppiAY4oSQJ53WYPfaNKI3NrsfsS4J9wTQOHFWQGNP+X5CszCL6P2EaMJDTamrhu2PN+EFYQehxuYKopbMot+c6ylj8yGVpM0NYOExHCtg/ZJSNzxwC5QFbyfSqXCAnQCiITILrWj0jFHK6OsHBiRBMSMxJFEhmMIYVIqSVERjT3I0wmo8FYSVRoiRJyAueJENKXFSG87IZYcbvsRognMlTiQCRMdPqaELBIKYGEGQ2idFVjVTZRQrBdJ26E3caOJbJ+sG11BAjdR2gFEcWE8Mxpqvo4EwXPZcLE1hICKK0hcUlXUxHCjhGHV8T6FSFldsUEaQGpK5LgrnKujeY6YLWAEyOatOd/CJ0YQMZPVS/aStKtiBWQuWXi0Ms40baUdIHrkiEnXam1tR/F8P50sVx9Crn+pHS8iqAFgWg80fYE7IrR6UBOGgMoPfkkFH1NrCYq143M+sG2NRUg7HjKoFSgaK1QKTKVMCHfXdYtw4uVUhUuMxQhQPF7E4YIYelA8ftURhFSTgFiIjxI8kBrPV2ZxyFOjGhC1xnJCqt6aQ9WoCxWEkBDlHBcMiVJHrKaD0SgDSD3tVKkBCdgd+GXCRvbMWnINTEMK0kHisW9eMcUYSJGEpw2uu4X3ngmbhhA0wrCS7dlvz8yRNVRgVK3DC9ehG0XgAhhu+um6opcMnQbMkYD85rdLzoGb7/BtiBFiMoCoiNCovI7I0eN4sSIJrnKq0SU+BQjAF+Q2BYAi6M0BZgk1tD7gLw4SKP0xMuxhhT68A4qukvthzfSTqNOSQL86rOdnOcsKqsLb2zVmDr7TWlAMa6HvoHT+X/L2iQ422ytH7zXxgIEkItWCLaRf2I/gAnUdpFFRGbdYEu8Q7CtRkQI+7wTue+QTTyIz/ogprEgQbhfnPgYHzgxokluTRoSO9Kmam4yMACN4NYASqkX4FpJBNaQElM77+TSh2JEXT/4EZpElFDHsiXs+I4wjtMAYFJ+TFMxkpDs4/07ZOKDd8xABQg7gGglXRZeHzaIVBdeRk0VpumaihDeGKq+pttQKkJsglH9WEBMxAcdt1eTbhq/wf51hBMjmrTnfyBRJNHgWe88ACrhtgG81gpy8iWH5lpmVUu1A8AeqCOyJNYS3fgSEQmUnpxFY8qCY2Xj2UD0GV1tlIyvQnZe5/2f2DFNxAfbPxQBwnOxkAsJEbM8i4jugo8sPFFTJSKEbVshEWJiBbFxwwTlfjFKGnDUFE6MaEJ+KO1IocHjhgjsAAUCFSSqu3sSZMo7HDcAkUZ2YfiQeuQVkSJolptPQCwKwraU6IgWFeQr0wvvqsY6mYziQpZ66b3sNpn44B1PKw4EEIsQUXwH3Z/8A/cA2Av61hEdiwd9rJAXs4sz24MQIezrbsjjR3TGgFksSBgCJGjxQVZWd9QmToxoUkztTaHR12ICCnTcNnFYrd1SiCVhYQ+hZRVhLzasidSmWiIjTIhQ4pGAmTjxm0Hj12JDfmkDKIYcyUQGTUKyTze9V+R6IZRFgMi+E7ylCoiIBexdNoQyryETlggZlewvoxWkGgSIq8haXzgxokkk/yPqxAAa/cY8qCiH24YEtwJecVNSoEpVGZO3UFmDoC3dR5TlAHjO3rqrBuvg14KSgL3LhvzSBlG8oCQ0+5oWROOdo9ljScUHYCdAAHmpdlEfQieK3x/eejG2cL5D1SRCdMen03lNLSPQFyFBChAnPhy6ODGiSWQk94OJIIUmzaijGPrtV4JlBAkQsJVECzZoVXbh4aFaIZiMwQoTQaVMWfAra7lIQF84mIgUW0FDpt2L4seqG5Asa6cjPABD8cEb3I8AEf3/abFB9++C163Ca8eiEZQKVFdgqmx8Ud+EpD1nm18rSBgCJGjxQeaSu5jVmpsmDX/LnPtdIr16cGJEk2hfLuowMpJEc4ufknl6RJBEClGzyq2qjBt6pV9Z20HkTtrcrBt2AT7AezGKUQOzqZWiuBF2bBr2hKQpTkQkEF5hMxnkHDkIFOKfdcWI7Jyc4GzjuX+41wf2RKYjJFRWMR6y9FzZWjOqaqnsc0NXDOBPhJgUKzNxx4j6EstIB+TVWKGfEVMOAaIjPkysHnVZibXM3HHHHbj55pvR09OD2bNn47bbbsMxxxzDbfvLX/4SN9xwA958802k02l89KMfxb/927/h3HPPBQCk02lcccUVePzxx/HWW2+hu7sb8+bNw4033oipU6dqz8mJEV3yv+1oXwYjzcM5sdAWlfeREEUSScr8URAf4g45Yikk+yPyuiT0dpVA4e2Po7gqcMl6N+zKwIRM/vUEyLNp2HVwAPHFhcCusEqPk0bhwsOuAEufj1mrCeA96eueC2mtZQIRIz0Ahg36JRT7RXEnSuu4jhABvJVNyWvynBWjplZAImi7kLvSZvNjkO+P6DvCvq6SeBC6DW8MEysIr00HpJaRMNwwYQkQJz4qx09/+lMsX74c69evx7HHHot169Zh4cKFeP311zFp0qSS9hMnTsR3v/tdzJo1C62trXjsscdw/vnnY9KkSVi4cCGSySReeuklXHnllZg9ezb27NmDiy66CJ/73Ofw4osvas/LiRFdyO9hEOhozudmdiW1S44oxQYF7d6JIoUkOavKXDcmooPoCdF+loJFJV/inXvRISkiMZTWGaFLcpusB0KvKcIGMtKihFxYBaIEKF60eaJElzjUokU0Jll6pRf6tQV0AlyF1wNKpHH3mcL7H9CihLd4HYFXkp0lhlKRQx+Lfe5DgLDdy1kjxLRSKv2apIR3oJCdVc44ED/ul7DWnqHn1OxrkZfxw9q1a3HBBRfg/PPPBwCsX78ev/71r7FhwwZcfvnlJe1POukkz+uLLroI999/P5555hksXLgQ3d3d2Lx5s6fN7bffjmOOOQY7d+7E/vvvrzUvJ0Z0Ib/5JAongg5khIJEJj7YWBLWSiJC5LrxrHFDi4o4Si+OuqJEKm6IKJmIYjXXPdSgstRnVQVMUiFWBE+U0FYSMga8F2qZtUR10SftEpx9uhkx5EKShIbVQrNNAdHnxW4XiRNRgKrIGiGzkvDa82AFShxFy5puuq4PKwhg7oqxDUiVjc9rK+pPpYTruGFMK6KWW4D4FR+OIn19XjdoW1sb2tpKL0wjIyPYvn07VqxYUdjW2NiIefPm4bnnnlMeJ5vN4ne/+x1ef/11rFmzRtiut7cXDQ0NiMfj2u/BiRFdyP+aWQKeCBKZ20ZXbABeoaLtugEn60YlSrwHFd/xs+XlWQouHLpWtexrxbu4iC6S9EFZt5DKdcOMK7OWyM5vEcgFh+m5MaXTx9RyITvp01fQoILdZFYSepvoNQ/y3Y2hbAKEfU33C9sK4qdAWVcS5DfGipAwY0D8ul50xYet4Oi08p9WGjb70KY/MH36dM/WVatW4aqrripp/cEHH2BsbAyTJ0/2bJ88eTJee+014VF6e3sxbdo0DA8Po6mpCT/84Q8xf/58btuhoSFcdtllOOuss9DVpZ+S78SILknkREgfSrwQHeS2N28lsRUfuu1o100sSvWn40l4xFG6ho33ILnvNh3oWjoR75o3pG2EXCRaIA8qlZSgT9HbdS6cMtcNIBUlJfCO16IQDiYX9zTzKEJ0Qo8p9ovEgCQAmLtfJPhYeFYSURsW3riFoCjOHAN0w7Cv2X46IiRIK4hBcbLG0VxOeDSWQkOz99TNipBqECBhL3pXeuwQ6z9VObt27fJc+HlWET/EYjHs2LEDAwMD2LJlC5YvX46PfOQjJS6cdDqNL33pS8hms7jzzjuNjuHEiC6DyJ2ckigVsvm00oLbBii4bkxcMvQ+E0GT71BEFeQah1yUEIjQoNuLIDERniycwoSo54ILixairAxdUUKOr0pjJX1lwsG05gEpLiJblVaUAkunuurU2+CVUScEXatB9D+RWTd4kM+HXI0l3xM/FVJ5/XSzYoKyggSUkqtrBRGJAtMA1HIveufqiujT1dWlZYXYe++90dTUhN27d3u27969G1OmTBH2a2xsxD/8wz8AAI488kj8+c9/xurVqz1ihAiRd955B7/73e+MrCKAEyP6JJE7SSliBFgriQxdl4yoHS1Y6HgSesqA5orAOrEiceRECds2hmK6ahS5GxTPmiaKO9tBal/JuUrmQwK8d9nsHTevBLhqwT9W0IiOaQpx+LOrGsvG5LlATI6v40JRjcuzYrBWGtHnT7dVbaOtWJyVo/3GgbD9bASIyfiqvpzXSgGS12uRSAoNLcXvUBACxDb2Iyjx4Ud4kLk3jWPLiC6tra046qijsGXLFpx++ukAgEwmgy1btmDZsmXa42QyGQwPF9MCiRD5y1/+gieffBJ77bWX8dycGNGlD8Bk5K7wstLgBlYSEX6KpQmDXMERJTLhQfbFoZd1QsRIN4pWEh68z43rFhJZMHh34ryASpU1QHRBV13obSuBylY15hWPk1UdlRWbowNJeSJEZjXRhQQNi0QJUKoSNOvBBJkNw/aTCRC2bVAZMQZuGIIoGLU9PebZxl7ATQJQwxIfsmPqjC8e06X3BsXy5cuxePFizJ07F8cccwzWrVuHwcHBQnbNeeedh2nTpmH16tUAgNWrV2Pu3Lk48MADMTw8jMcffxw/+clPCm6YdDqNL3zhC3jppZfw2GOPYWxsDD09PQByacGtrXqF6JwY0YWcL/rgXXVVIkw6BjK5kw/HSkLHfdi4bjxxIzLxYiJK4hALD7KPNVSQ7eRaMwXFRAneOSdOPRcdqwSZdYQWIKyVhNeGxlRYqCrOyiAVq9hVjXnvi646KoLuR//v2X5EnIisI7puH5loYf8/mhVQeduJZQ0IPw6E11YkJEK2gpik43ZiAC35D6nSAiRo4TH+RIdfV5R5/zPOOAPvv/8+Vq5ciZ6eHhx55JHYtGlTIah1586daGws3jANDg7iwgsvxP/+7/8iEolg1qxZ+I//+A+cccYZAIB3330Xv/rVrwDkXDg0Tz75ZElciQgnRnQhv5H3Ib7zFwiTDmTQgQEMdjVau25MRYmnPkluA1+U8IjD647hT9T7OyDfpA4UrSQqyHFouK4aFpG4APiWEVFZehNx4fekQcQIHT0vcs2YHksWpMqr/6HrtlEdU1QtlaBRip38ZshvirWsVVKAqI4RtBsG8jiQNuouiBUIpgIkLPGhGls1LxURyfEbw1hNvU5ZtmyZ0C2zdetWz+vrrrsO1113nXCsGTNmIJtlF0o1x4kRXejrGbGWytw1vcidWCl4acC+1q/RQClKSI0SHZeNrA35JnWjWClS1o89XyldNbrWEQLvQs8THzoXfj+LtNF0AtiJ0hWOAb5QMEEng0YlTMg4IkGnK1wU1pAO5nkcXpEehABhx2HbhhULYuGGMQ1EjSCJ4bxlxCQDJox1ZsIQHjLB4ahfnBjR5f38I73qKg1HfBSgRIsswFXkuhFZPyJIIcXdnrOoWFlKWAHBExLsvjiK178OwedDQ9ctiee3JVB6MfGsPUOKqwHFcvQEkXsG0BcgKhHgx0UD5KwhvDQK0/FFi8XJUmx5gbzkeOx4PNHBG1MREyITIID4At+J0rXOwhAgQVZG5bwOWoB4tw2hiUnpMxEg1SQ+bIUH7xjOMlLbODGiC12BVXWxVY3TUeq6EcWN+BElBJ4o4QW6kpRgT0VXAhtXwsuoAYBp8MaM8NKDeUKHZ/jwWEvomh+k8mvxHYqLo/FECrumChkDnEmw47BjmTLBsp+OVUJmYTOpkKrKiOEEo5qKD1EWDE+MsH3LIUBU6bcBuGB0hQIRCK2URS0o64dMfAQpPGxEx/iIH+kH31KqSy0WeuPjxIgudAXWNPRdNHQ7znY/WTcsPFGiCnQtWFGi3n1cF47MTUNu1DqQC0KkhQuvmBrZxwukpbUFaQeUihNAIFB4woLnyuFtoy/aImsFb/0cFbyF4FTofgdE1hKCrugQHdPQ6gHoiw92jA7k/o1xzj7RWOyxKhQDohOEaipAvNuShceR/HcoyPLuKvGhIw5MRYdfwUHPucFdzmoa99/Thfxm+lB6MpYJEw3orBtRPAkdzKqqT0KLEgJPlPCsMUSUsNYSbql5XhZOJ/hCn7ShhYaN1YRuS48BoLRiKs+CApSKFEBduEtkDeEJAZ6IyebHkIkRlajgobKYiPZriA7ATnjwhlfEVRREejdylkeTAFTV+GUUILbuF5MA1E4McJPeTa0fMvERtPCwFR2u8Nn4wYkRXZLU4x6YW0U0ntPxJLaihOe+Ebl6CGQ/68LhlZoXWktI0OrU/GfAExWyFGJdq0nujedQWk8A7qJ5JSKFDCo78Zm4aXgCYAzAKwD2h3whQRNU6bYyNIQHoHa3iA5lIgjo45CYI90aIDbHC7gOSJACROZ+yf3HWrljqcYVzUl1XIKO8LARHEGJjdzn4S5ntYz772mS7s0/SUJ/CXiCSIgI2vkRJQTeNp4o4YkYFqW1pBO563Mi/zwGfnwJDU+YiKwmEIwjs56QPgRtkUIjKgevii/hQQKNYrD/2em6bSQFxkR1PoKweIi2s+PwjkXHjIxx+hhaMPwIkHLEfxRf68d+tCMJoBXtSHrC1mxcLzLhELTwCEJw2Kzw66gtnBjRZCD/W0j3AhiiTvc61g8ROvEkgLEoUblkZEXW2Pa8gNdi47y1BADG8lGHHcgVrRK5cnjWDnobaZebjFyc0GOwz0l/QC1SAEGZf9UCe7R4US2AR/Z3QrsaKTsHHWTr/ci+j3HONh3hwdvGjqWK+QC8bpoMZ1wNC4atANERH4BagJjGfsjGko85hFGOL9TW8qESH7rCw0Z0OJGxB/qFmXgo1iepIZwY0aQ/fyvy9z5gQjp3PhVeJnQEimmQK1CSDqyKHZG5ZOjnrIiRWUtYFw6xlkQi/cAbQOO+SWCQyXFWxZiI3DmAWpywbchFRmRBYcch0GPwkIoWQCkaCv8GxarGxosH5lEJ37hkn46bRbSNN65pwCndpwPFmCOT2h+8NgYCRLcCalACxCbrpZmJNQpafIRl7QhKcKgLrhkKd0dV4cSIJv3UY9MochelQSDWkf8J+AxiBTuGQJTwKrnyRInMJZPbltvPiythUVlLYtF+pNP5cWMpNExIq1OEZa4b2fM41OKEbUfGADMm/ZqMRxCda+PU84SgDQ9SVXQvmLv5ZMQ12si8OzpuFtFxRN93k2wXdhsdAK2KM9GxlhgIkDDFh+5YsjEJnRjAGFWN1TbeQ0d86IoOP4LDZCVfR/1SE2LkjjvuwM0334yenh7Mnj0bt912G4455piyzmFn/nEP8ktnEKftYPHcWyJKDKwfQixFCYG1fgDyFYJtrCVAsVT1PpH38UFLt7x2CStIRGLDRpwAXoFCPjdw2pOxAL4wgmAfwSR0hPyfVAsJqjDJ+BYJDdX+OGcb7zsqmgvbX8fK0sE86oyjMa6pAAky8NRPGi9vjKZC1dWkR4x4x7dzuYRp6QhabIjmmkWrL4eHo7JUvRj56U9/iuXLl2P9+vU49thjsW7dOixcuBCvv/46Jk2aVLZ59CN3niMri3yIfMTAKNDXC3Q1M6JEhI4o4QkUhSjhxZTw3DSyDBw/1pJWFEtVx/InTVHgKwBv8Kuum0ZHnLB9cpPNYSJSaFTCREe4kEJenSgt6qVCJSpM+8QVfXWFB28cXfcOu42k9HajmPlsETdSLuuHn7gP02wXcqwmZrVnP7EeOuJDV3T4FRsufdcB1IAYWbt2LS644ILC8sbr16/Hr3/9a2zYsAGXX3552eaxB14xUgIrSnqBFh/WD2E/g0BXAs9NI7J88NCxlgznrwy5UtXDBREkijHxBL/S6cKAf3FCXgNqkUKfB+nPWhQbEs8/JgT7RSKAKNTJ4Me6yiwzJsQV+2XfQ11LB8FWePDGpNcz0hUjCKbwWNCBp7rjmWa6tOf9e+0YQqawQJa8j+pYgJmlw1R4hCU0eO83i9EatIwMAAIrlx71EwBc1WJkZGQE27dvx4oVKwrbGhsbMW/ePDz33HPcPsPDwxgeHi687uvL1YRIp9NIp1UZD2IGI7kLZyJStDTQZazI12kMwGj+Recw0EJM8qMoZmS0IBdA3YHif2AQxfITjfm2HcidmJPwZrFkqQNmkLugRoHWvOtotCuLbgwi1RrFXuhDChFk8n6CDMbQhn4MIYIsyOdErpQjiOD/MIQoupDCADrRiWEkEUUHRjCE9sLdGQmmI9OPpgGgFdPS72EPOhHHYMGqQlw4hVVHW3oxgE5MnNiPVCr3eSZHi8mKmbH8m42gKAqi1PMOFC/e3cgJNKB4saKvTzGUxoXQ+0lCTC+8xPOPIksHLQg1iDTnvnuRvdL85QT21htHiG68kkzkiNZWEo3NG4vXljcu0y6SzX8+kTQKBgA2mHcMaOxirAwR5uI4CkSYbZ3p0n+iaM0Xz9icNp2cL0S7IOPFSwOnf9Ht4qUxP653jMZ0Y+GxtE+xdg1vjvSpvnS+pZeB0vkTSu2+/OPlyGqYAW3qk/DGzaZz2/yc54Po77CjqsXIBx98gLGxMUyePNmzffLkyXjttde4fVavXo2rr766ZPsTTzyBaJR/56/DrA0bAAD75R+BYpxdH4Jb11WLIQQaBNnKPPrhrM2tKKaqlek+hb0Asq/L581TsuG0zZWeQvnhia8Ev+mGv1f758P7lQTxy9HnsM1zy3o8FX5/5UGfJTZv9vcdSibrx9pQS1S1GLFhxYoVWL58eeF1X18fpk+fjgULFqCrS3cJ9FLS6TQ2b96M+fPno6XFpZCxuM9Hjvt85LjPR437jOQE9fkQa7qjvFS1GNl7773R1NSE3bt3e7bv3r0bU6ZM4fZpa2tDW1tbyfaWlpZAfsBBjVOvuM9Hjvt85LjPR437jOT4/XzcZ1sZGtVNKkdrayuOOuoobNmypbAtk8lgy5YtOO644yo4M4fD4XA4/NIXwF99UNWWEQBYvnw5Fi9ejLlz5+KYY47BunXrMDg4WMiucTgcDofDUdtUvRg544wz8P7772PlypXo6enBkUceiU2bNpUEtTocDofD4ahNql6MAMCyZcuwbNmySk/D4XA4HA5HCNSEGHE4HA6Ho/7YA391GupnXZ+qDmB1OBwOh8NR/zgx4nA4HA6Ho6I4MeJwOBwOh6OiODHicDgcDoejorgAVofD4XA4KkI/+Mt46xLgImUVxllGHA6Hw+EYR9xxxx2YMWMG2tvbceyxx2Lbtm3S9j//+c8xa9YstLe34/DDD8fjjz/u2Z/NZrFy5Ursu+++iEQimDdvHv7yl78YzcmJEYfD4XA4xgk//elPsXz5cqxatQovvfQSZs+ejYULF+K9997jtn/22Wdx1lln4V/+5V/w8ssv4/TTT8fpp5+OV155pdDmpptuwg9+8AOsX78ev//979HR0YGFCxdiaEjfcuPEiMPhcDgc44S1a9figgsuwPnnn49DDjkE69evRzQaxYYNG7jtb731VnzmM5/BpZdeioMPPhjXXnstPv7xj+P2228HkLOKrFu3DldccQVOO+00HHHEEfjxj3+Mv/3tb3jkkUe051X3MSPZbBaA/2Wh0+k0kskk+vr63KqOHNznI8d9PnLc56PGfUZygvp8yLWCXDvCZTiQ/uz1TbR6/cjICLZv344VK1YUtjU2NmLevHl47rnnuEd47rnnsHz5cs+2hQsXFoTG22+/jZ6eHsybN6+wv7u7G8ceeyyee+45nHnmmVrvpO7FSH9/PwBg+vTpFZ6Jw+FwOGqF/v5+dHd3hzJ2a2srpkyZgp6em32P1dnZWXJ9W7VqFa666qqSth988AHGxsZK1nabPHkyXnvtNe74PT093PY9PT2F/WSbqI0OdS9Gpk6dil27diEWi6GhocF6nL6+PkyfPh27du1CV1dXgDOsD9znI8d9PnLc56PGfUZygvp8stks+vv7MXXq1ABn56W9vR1vv/02RkZGfI+VzWZLrm08q0i1U/dipLGxEfvtt19g43V1dbkTgQT3+chxn48c9/mocZ+RnCA+n7AsIjTt7e1ob28P/Tg0e++9N5qamrB7927P9t27d2PKlCncPlOmTJG2J4+7d+/Gvvvu62lz5JFHas/NBbA6HA6HwzEOaG1txVFHHYUtW7YUtmUyGWzZsgXHHXcct89xxx3naQ8AmzdvLrSfOXMmpkyZ4mnT19eH3//+98IxedS9ZcThcDgcDkeO5cuXY/HixZg7dy6OOeYYrFu3DoODgzj//PMBAOeddx6mTZuG1atXAwAuuugifPKTn8Qtt9yCU089FQ899BBefPFF3H333QCAhoYGXHzxxbjuuuvw0Y9+FDNnzsSVV16JqVOn4vTTT9eelxMjmrS1tWHVqlU16YsrB+7zkeM+Hznu81HjPiM57vPR44wzzsD777+PlStXoqenB0ceeSQ2bdpUCEDduXMnGhuLTpPjjz8eGzduxBVXXIHvfOc7+OhHP4pHHnkEhx12WKHNt7/9bQwODuJf//VfkUgkcOKJJ2LTpk1GbqiGbHnylxwOh8PhcDi4uJgRh8PhcDgcFcWJEYfD4XA4HBXFiRGHw+FwOBwVxYkRh8PhcDgcFcWJEQ1Ml1seT6xevRpHH300YrEYJk2ahNNPPx2vv/56padVtdx4442FVDhHjnfffRdf/vKXsddeeyESieDwww/Hiy++WOlpVQVjY2O48sorMXPmTEQiERx44IG49tpry7RuSvXx9NNPY9GiRZg6dSoaGhpKFmILYil7R2VwYkSB6XLL442nnnoKS5cuxfPPP4/NmzcjnU5jwYIFGBwcrPTUqo4XXngBd911F4444ohKT6Vq2LNnD0444QS0tLTgN7/5DV599VXccsstmDBhQqWnVhWsWbMGd955J26//Xb8+c9/xpo1a3DTTTfhtttuq/TUKsLg4CBmz56NO+64g7s/iKXsHRUi65ByzDHHZJcuXVp4PTY2lp06dWp29erVFZxV9fLee+9lAWSfeuqpSk+lqujv789+9KMfzW7evDn7yU9+MnvRRRdVekpVwWWXXZY98cQTKz2NquXUU0/NLlmyxLPt85//fPacc86p0IyqBwDZhx9+uPA6k8lkp0yZkr355psL2xKJRLatrS374IMPVmCGDhOcZUQCWW6ZXhpZtdzyeKe3txcAMHHixArPpLpYunQpTj31VM93yQH86le/wty5c/HFL34RkyZNwpw5c3DPPfdUelpVw/HHH48tW7bgjTfeAAD84Q9/wDPPPIOTTz65wjOrPlRL2TuqG1eBVYLNcsvjmUwmg4svvhgnnHCCpzrfeOehhx7CSy+9hBdeeKHSU6k63nrrLdx5551Yvnw5vvOd7+CFF17At771LbS2tmLx4sWVnl7Fufzyy9HX14dZs2ahqakJY2NjuP7663HOOedUempVR1BL2TsqgxMjjsBYunQpXnnlFTzzzDOVnkrVsGvXLlx00UXYvHlz2VforAUymQzmzp2LG264AQAwZ84cvPLKK1i/fr0TIwB+9rOf4YEHHsDGjRtx6KGHYseOHbj44osxdepU9/k46grnppFgs9zyeGXZsmV47LHH8OSTT2K//far9HSqhu3bt+O9997Dxz/+cTQ3N6O5uRlPPfUUfvCDH6C5uRljY2OVnmJF2XfffXHIIYd4th188MHYuXNnhWZUXVx66aW4/PLLceaZZ+Lwww/Hueeei0suuaSwiJmjCL2UPY07X9cGToxIsFluebyRzWaxbNkyPPzww/jd736HmTNnVnpKVcWnP/1p/PGPf8SOHTsKf3PnzsU555yDHTt2oKmpqdJTrCgnnHBCSSr4G2+8gQMOOKBCM6ouksmkZ9EyAGhqakImk6nQjKqXoJayd1QG56ZRoFpuebyzdOlSbNy4EY8++ihisVjBN9vd3Y1IJFLh2VWeWCxWEj/T0dGBvfbay8XVALjkkktw/PHH44YbbsCXvvQlbNu2DXfffXdhefLxzqJFi3D99ddj//33x6GHHoqXX34Za9euxZIlSyo9tYowMDCAN998s/D67bffxo4dOzBx4kTsv//+gSxl76gQlU7nqQVuu+227P77759tbW3NHnPMMdnnn3++0lOqGgBw/+69995KT61qcam9Xv7rv/4re9hhh2Xb2tqys2bNyt59992VnlLV0NfXl73ooouy+++/f7a9vT37kY98JPvd7343Ozw8XOmpVYQnn3ySe75ZvHhxNpvNpfdeeeWV2cmTJ2fb2tqyn/70p7Ovv/56ZSft0KIhmx2npfwcDofD4XBUBS5mxOFwOBwOR0VxYsThcDgcDkdFcWLE4XA4HA5HRXFixOFwOBwOR0VxYsThcDgcDkdFcWLE4XA4HA5HRXFixOFwOBwOR0VxYsThcDgcDkdFcWLE4XA4HA5HRXFixOFwAAD+/ve/4+yzz8bHPvYxNDY24uKLL670lBwOxzjBiRGHwwEAGB4exj777IMrrrgCs2fPrvR0HA7HOMKJEYejjnj//fcxZcoU3HDDDYVtzz77LFpbWz1Lq/OYMWMGbr31Vpx33nno7u4Oe6oOh8NRoLnSE3A4HMGxzz77YMOGDTj99NOxYMECHHTQQTj33HOxbNkyfPrTn6709BwOh4OLEyMOR51xyimn4IILLsA555yDuXPnoqOjA6tXr670tBwOh0OIc9M4HHXI9773PYyOjuLnP/85HnjgAbS1tVV6Sg6HwyHEiRGHow75n//5H/ztb39DJpPBX//610pPx+FwOKQ4N43DUWeMjIzgy1/+Ms444wwcdNBB+OpXv4o//vGPmDRpUqWn5nA4HFycGHE46ozvfve76O3txQ9+8AN0dnbi8ccfx5IlS/DYY48p++7YsQMAMDAwgPfffx87duxAa2srDjnkkJBn7XA4xjMN2Ww2W+lJOByOYNi6dSvmz5+PJ598EieeeCIA4K9//Stmz56NG2+8Ed/4xjek/RsaGkq2HXDAAc7V43A4QsWJEYfD4XA4HBXFBbA6HA6Hw+GoKE6MOBzjhEMPPRSdnZ3cvwceeKDS03M4HOMY56ZxOMYJ77zzDtLpNHff5MmTEYvFyjwjh8PhyOHEiMPhcDgcjori3DQOh8PhcDgqihMjDofD4XA4KooTIw6Hw+FwOCqKEyMOh8PhcDgqihMjDofD4XA4KooTIw6Hw+FwOCqKEyMOh8PhcDgqyv8fYNzVp7TdpT4AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def error(D, x):\n", + " return torch.abs(5*torch.exp(-1/20.0 * ((x[:, :1] - 3)**2 + (x[:, 1:] - 3)**2)) - D)\n", + "\n", + "fig = tp.utils.plot(model_D, error, plot_sampler, plot_type='contour_surface')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "anim_sampler = tp.samplers.AnimationSampler(domain_x, domain_t, 200, n_points=760)\n", + "fig, anim = tp.utils.animate(model_u, lambda u: u, anim_sampler, ani_speed=10, ani_type='contour_surface')\n", + "anim.save('inverse-heat-eq.gif')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Training with the TorchPhysics wrapper in Modulus" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we want to do the training in Modulus with the usage of the TPModulusWrapper.\n", + "\n", + "Remember that an additional Modulus installation is required!\n", + "\n", + "First, new initialization of the model and the conditions:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "model_u = tp.models.Sequential(\n", + " tp.models.NormalizationLayer(domain_x*domain_t),\n", + " tp.models.FCN(input_space=X*T, output_space=U, hidden=(80,80,50,50)))\n", + "model_D = tp.models.Sequential(\n", + " tp.models.NormalizationLayer(domain_x),\n", + " tp.models.FCN(input_space=X, output_space=D, hidden=(80,80,50,50))) # D only has X as an Input\n", + "parallel_model = tp.models.Parallel(model_u, model_D)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "data_loader = tp.utils.PointsDataLoader((inp_data, out_data), batch_size=num_of_data)\n", + "data_condition = tp.conditions.DataCondition(module=model_u,\n", + " dataloader=data_loader,\n", + " norm=2, \n", + " use_full_dataset=True) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As the Modulus losses are different from the TorchPhysics losses, we don't add an additional weight for the pde_condition." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "inner_sampler = tp.samplers.RandomUniformSampler(domain_x*domain_t, n_points=15000)\n", + "\n", + "def heat_residual(u, x, t, D):\n", + " return tp.utils.div(D*tp.utils.grad(u, x), x) - tp.utils.grad(u, t)\n", + "\n", + "pde_condition = tp.conditions.PINNCondition(module=parallel_model, # use parallel evaluation\n", + " sampler=inner_sampler,\n", + " residual_fn=heat_residual,\n", + " name='pde_condition')\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n", + "/home/gea3si/.conda/envs/mod_tp/lib/python3.10/site-packages/modulus/sym/hydra/utils.py:150: UserWarning: \n", + "The version_base parameter is not specified.\n", + "Please specify a compatability version level, or None.\n", + "Will assume defaults for version 1.1\n", + " hydra.initialize(\n", + "Setting JobRuntime:name=UNKNOWN_NAME\n", + "Setting JobRuntime:name=app\n", + "/home/gea3si/.conda/envs/mod_tp/lib/python3.10/site-packages/modulus/sym/trainer.py:453: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n", + " self.scaler = GradScaler(enabled=enable_scaler)\n", + "attempting to restore from: /home/gea3si/NewModTpWrapper/torchphysics/examples/wrapper/outputs\n", + "optimizer checkpoint not found\n", + "model model0.0.pth not found\n", + "model model1.0.pth not found\n", + "[step: 0] saved constraint results to outputs\n", + "[step: 0] record constraint batch time: 1.765e-01s\n", + "[step: 0] saved checkpoint to /home/gea3si/NewModTpWrapper/torchphysics/examples/wrapper/outputs\n", + "[step: 0] loss: 2.444e+07\n", + "Attempting cuda graph building, this may take a bit...\n", + "[step: 100] loss: 1.170e+07, time/iteration: 2.093e+02 ms\n", + "[step: 200] saved constraint results to outputs\n", + "[step: 200] record constraint batch time: 1.608e-01s\n", + "[step: 200] loss: 9.220e+06, time/iteration: 1.023e+02 ms\n", + "[step: 200] reached maximum training steps, finished training!\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optim = tp.OptimizerSetting(optimizer_class=torch.optim.Adam, lr=0.001)\n", + "\n", + "solver = tp.solver.Solver([pde_condition, data_condition])\n", + "\n", + "trainer = pl.Trainer(devices=1, accelerator=\"gpu\",\n", + " max_steps=200,#8000,\n", + " logger=True,\n", + " benchmark=True,\n", + " enable_checkpointing=False\n", + " ) \n", + "\n", + "tp.wrapper.TPModulusWrapper(trainer,solver).train()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To use LBFGS with the wrapper (Modulus), the max_steps has to be set to 1, the number of iterations are set by max_iter!\n", + "As it one main iteration step, there is no output in between the max_iter iterations - iteration takes several minutes. Reduce max_iter for testing purposes." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n", + "/home/gea3si/.conda/envs/mod_tp/lib/python3.10/site-packages/modulus/sym/hydra/utils.py:150: UserWarning: \n", + "The version_base parameter is not specified.\n", + "Please specify a compatability version level, or None.\n", + "Will assume defaults for version 1.1\n", + " hydra.initialize(\n", + "Setting JobRuntime:name=app\n", + "attempting to restore from: /home/gea3si/NewModTpWrapper/torchphysics/examples/wrapper/outputs\n", + "optimizer checkpoint not found\n", + "model model0.0.pth not found\n", + "model model1.0.pth not found\n", + "lbfgs optimizer selected. Setting max_steps to 0\n", + "[step: 0] lbfgs optimization in running\n", + "lbfgs optimization completed after 200 steps\n", + "[step: 0] saved constraint results to outputs\n", + "[step: 0] record constraint batch time: 1.846e-01s\n", + "[step: 0] saved checkpoint to /home/gea3si/NewModTpWrapper/torchphysics/examples/wrapper/outputs\n", + "[step: 0] loss: 9.196e+06\n", + "[step: 0] reached maximum training steps, finished training!\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optim = tp.OptimizerSetting(optimizer_class=torch.optim.LBFGS, lr=0.5, \n", + " optimizer_args={'max_iter': 200,#10000, # number of training steps\n", + " 'history_size': 100})\n", + "\n", + "# make sampler static.\n", + "pde_condition.sampler = pde_condition.sampler.make_static()\n", + "\n", + "solver = tp.solver.Solver([pde_condition, data_condition], optimizer_setting=optim)\n", + "\n", + "trainer = pl.Trainer(devices=1, accelerator=\"gpu\",\n", + " max_steps=1, \n", + " logger=True,\n", + " benchmark=True,\n", + " enable_checkpointing=False)\n", + "\n", + "tp.wrapper.TPModulusWrapper(trainer,solver).train() " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_sampler = tp.samplers.PlotSampler(plot_domain=domain_x, n_points=760, device='cuda')\n", + "fig = tp.utils.plot(model_D, lambda D : D, plot_sampler, plot_type='contour_surface')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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kPPPMM3j/+9/P7fPEE0/g5JNPxnnnnQcAOPTQQ3Huuefil7/8ZaDNmWeeiTPOOKPU5jvf+Q6eeuop7bk5N01MmK4fQ+AHtoarzhpA1ypCk5b0kV0r4xIiIhcMb5vMAi4UIrruGBU2LpioTfesKyisW6hemQjvkTAI5+5x0GQymcDfyMiIVr/+/n4AwKRJk4Rt5s6di2eeeaYkLP74xz/ixz/+MT70oQ8F2mzfvh0vvfQSAOBXv/oVHn/8cZx++una78FZRmLGNl7ExkUTi1UkrdlHRZRCRGcbbzyCVISo0BUhukR58RivF98wyD6T8WhNAZy7p3FI9AKJECaBfAFAFpg+fXpg+5o1a7B27Vpp30KhgBUrVuDkk0/G0UcfLWx33nnn4c0338T73vc+FItFjI6O4jOf+QyuvvrqUpurrroKmUwGhx9+OFpaWjA2NoYbb7wR559/vvZ7cWJEkxw6rT39KnSzY1QuGiUiq4hNTRGTwmbVFiI1cclUU4A40REN7OfoxImjMdm9ezd6e8vf346ODmWfZcuW4fnnn8fjjz8ubbdjxw7cdNNN+NrXvoaTTjoJf/jDH3DppZfi+uuvx+rVqwEA3/ve93Dvvfdiy5YtOOqoo7Br1y6sWLEC06ZNw5IlS7TegxMjFti4SGzTcXVdNKGsIjQiq4gqToRWalELEZO03UitIVFaQQapR5NboXoVHuMxdVX0WY83kSL6kTT6/2/i0tvbGxAjKpYvX46HHnoIjz32GA4++GBp29WrV+OCCy7Apz71KQDAMcccg6GhIfzTP/0TPv/5z6O5uRmXX345rrrqKpxzzjmlNn/+85+xbt06J0aqgU1ciEzIkH08F01VrCI27hlWOMgCX9kxdVJ3Y7OGhBEhJpkzpsQtPuKMKQgzdj1n0/D+J+NNoADy/58TKuOBYrGIz33uc7j//vuxY8cOzJw5U9knm82iuTl4A9XS0lIaT9amUNAPunZiJAKIUGBrj4StLxK7VcQme0a3D9svLTm2aLwwQqRmIsTkguyqrFaik01TDxfGiSJQCAOo/ec+Xr7jtWPZsmXYsmULHnzwQfT09GDPnj0AgL6+PiQS3nVk8eLFOOigg7Bu3ToAwMKFC3HrrbfiPe95T8lNs3r1aixcuLAkShYuXIgbb7wRM2bMwFFHHYXnnnsOt956K5YuXao9NydGqoBpfRGdNkqriMgSYlqSXVeIyIREmtlnEx9iEqRqLETCihCTk6Rt6qrNscYr9RoDQX9PxqMwoT/3KD5zXVffOP7OJ2FfdggAxsyab9y4EQAwb968wPa7774bF154IQDglVdeCVg5rrnmGjQ1NeGaa67Bq6++ine84x0l8UG4/fbbsXr1anz2s5/FG2+8gWnTpuHTn/40rr32Wu25OTFSRWxdNDr9AcYqosLGPSNiEMFvUjWESOwiJEorSAZ2bohxfBKOlHqMXyHfn/EoSoDyZ27yOet8n2kLjPv+R41ORdQdO3YEXre2tmLNmjVYs2aNsE9PTw82bNiADRs2WM/NiZEaYOuiKe1Trc5rYhUJ656RlYFPM/tsAlVDCZE4RIiJADGhVifev1bpOPtX6TiEegnSnEjWknoe01HvODFiiGedyJfEgk28CCFsCXkapVVEJ5U3rdFeJkREY/GOaSNEjIJUTYVIWBFSS/FRLUERBtkcqylUonY1mDDehYnDYY8TIzHDihUeti4aI6sID500Xt1rpszCYipEqmoNqYUIsYkZaQTBYQt5b/sBeIvZF6dQqWXsyUSpceKQ0oWqxozUM06M1CGs1SWwjxO4GtoqktacmMgqMgigTTBezYWIjQiJSoDQ45jEjIxn4WEK77OIS6DUizhxwsQx8XBipMrIhIbRODZWEZ20XFP3DHucfkF/3jFVQiQWESLbF4UIsXG/1IP40J13rQNDgcrPq1riBKjO+x/vwa8ORyVOjGiSQxLd/nPdeBEdwRE2i8bIKsIjLWhrEycioy6ESFwixFSARCk+qh3sZ3q8aly8qyVOALssEluctcQxcXBipAqwYkWGqYumRBRWEVPo4/Qz+2THq7oQsREhUVtB3gI/JkKHRs4uqEVVz2qIk2oHwtqmhzscjYETIzVAJ6iVoB24SjBJ5U0L2pq6Z9IA2jX68+akEiJ1J0JqaQUZb1Trgh63OKmmtYQcrwnOWjIOSCLcVVi6+Gdj4cRIHVB26+jVFiGUXDSqtFhV0GpYIUITqxCplQixtUw4IaLPIDzL0SBQcojGRVzipBbWEsCJEsd4wImRmBDFk5jUFgllFSGo3DNRChEamRDRyZiJVIjYiBBdAfJX8C9mUQqRHjS2q8aUal/U4xAn1Y4tcYLE0dg4MWJIDgkAo6GKndH9YreKEFQl300zbdgxdC0iVbWGiC7gYawgPJFBb9tf0MZhh44Ii/qCT/5/jSRKnJXE0dg4MVJnhLaK2LpnVOOKxiDHITEjWUn/0EKkViLERFw4IVJ94qoPEocoAeIVJk6UNBTdcDEjPk6MxIjIRaOTzkswtooQ4nTPiI4jO0ZNhYiNK6beRIXMVeMWFgsStTiJUpQA1bGWOFHiaCycGIkQk/oiNDrxJMZWEQLPPRNVnAjdV9ciopW2SwuRehYh1c6iYGGPS792wqRMVOIkLlECxPcdcqLE0Rg4MaJJDgmq6Bk/XiS6Y/njRmEVIaQV7cIIkSEAHRr9Y7GGVEuEyPqaXlTCXoR0+jhriZiw4oSNEYqCuIWtEyWO+saJkZhRuWi0CqFFYRVh2+ugK0REx4xUiISxhoguyDIBYnsRV13oRP4qE4FhAq9PIwoUWbxP2AtsGCHQaNYSJ0oc9YkTIxFjUtBM1q8urCK8/rqVXGMVIvUoQlTjNcGro6FqVw2XT62tJroXRN0FCaMqmx6FKAGiFSZxW0oAJ0xqSBLBRUZNyUc1kdrjxIgltim9psRuFTEJWI3dIhKVNaRaIiSOu8xaiBL6uNWE/r81U/MoRDSm7f8l7P+g0VKDXZ0SR+1xYsSQrF9nxLyPubWExsgqohu0GrawGd2+U3IcQuBjq6Y1JAoRYrIKcBQn9jjvinmIjhWHSOmFvtUjDGGFSb2Jkjgr0zpB4qgtTozEgCqwlY0XEblojKwiNu4Zdiz2Od3fxK3DO06kQiQKl4zpRVZ0orY9idcqA8cU1TxtxQr7mdkuB21yDFvqRZTEbb1ygsRRO5wYCQHrojFN6dWlwioiO2/buGd47dLUc9OsG7p9KLdMGJcMEJ0QkaFz8qYvYnGvu1JtorKo9FCPRdhbTnTjUBrZUjKA+ESDEySO2uDEiAUm7hZVFo3KKlISIjx0rCK67pmwQoTnpolUiOhYQ3RqhIQpL16N0uRRBT+GJWzRt7BZPOwFMapsGuIianRREmcsiRMkVSOJ4IrnpuyLaiK1x4kRTWwLmUUGKxpk1o40Z5/KPUOg+4apzKrtlonKJROFEFGd2KM48U+Cl1lT76gulDZihf38woiTqHCihI9LAXZUFydGIiKKINXSWKxVxDRolSATLDxBkeaMy44tEiJCi0iU1hBbESLqS4grfoO+yBT9v/EC7wJqKlDozz2OmBERvADaWouSqOJJ4hAlTpA44seJEUOGkQAwppXSa+uiEWIStKpyz7Bj0tjEiBDDkZUQiVOEiPoTVCdv0UUiymXn6ymYNUw8TRiB0k091rIoW61ESZTxJE6QOBoPJ0ZiIAoXjTRWhMXWPcOLEwkTrBpAJETCWkOiXMBOdtJWXRRsLxo9qG83TdTZM+znpPP/q3X9EyAaUQLYlZqvRyuJEySx0AcXM+LjxEiE8Fw0toGrJUSxIlG4ZwhpSXu2j1bWjI0QidoaIhsHEJ+oowgg5Y0dJkMECF4IqlGjQ0TYwFT6841CmNjGoejWOqlFrZJ6tZK4OBJHfDgxEgJZSm9cab5C0v6jqXsmTW0zKYbGCpFSSeNhlL9WIiFSLWuIzsWLRnXyj/LEHuaEHncwpym2AiWMMJG1icuaUm0XTlRWEhfc6qh/nBjRZJgSFzI3jImLRpnOq2sVSXMG13XP6LRnn0vriBCiECK8C1SYC42tNSTsybwXXonzjD9Ws7x5zZBdXEyFiqmrZRI869EkAG8ZHssW20qwYUXJeBAkgHPdOKLEiZGIsHHRRI6te4YXJ2IsRIap5zZCRGUNCXu3Ww0hEseJ2eYiElcJdxbdC7mpK8XUYsI7XjViTWxFiY0gATyhZktcgsThiIZ6vUWre6Jy0cRqFdFxz4gCVtn2bFvjGBE2PkRXiAxw9ptiI0R6JP0A7wLE/unQTY2t82eDyfhhLlA275+dn4r9qb96hf0+62DznQ5rMYpDoNUyfmkckATQFeLPMFdi3bp1OOGEE9DT04PJkydj0aJFePHFF6V97rnnHjQ1NQX+Ojs7S/vz+TyuvPJKHHPMMejq6sK0adOwePFivPbaa0Zzc5YRQzyhwV9VlOeiUQWuRoYssJUVNGlOf1VVVi0hQiMTIhDsAyqFiC2mAkTnwmh6waUZM+hbbWTvPa6qqeyxdQJ8TSwmZFzV/Mmcw15Uq5kK/BbsxZlL/Z3IPProo1i2bBlOOOEEjI6O4uqrr8Zpp52GF154AV1d4gzO3t7egGhpaipnBGazWTz77LNYvXo1Zs2ahbfffhuXXnopPvKRj2Dnzp3ac6trMTI2Noa1a9fi3/7t37Bnzx5MmzYNF154Ia655prAh1FrZC4aHUJbRWR1R3TiRFQL33GFCGsNGWVe8zrzTvgyk7qNuT1KEWK67kwY4j6ZVzs4FbAXJzoF0Mj/s9aipFYX4TCxJM5lM1HZunVr4PU999yDyZMn45lnnsH73/9+Yb+mpiZMnTqVu6+vrw/btm0LbLvjjjtw4okn4pVXXsGMGTO05lbXYmT9+vXYuHEjvvWtb+Goo47Czp07cdFFF6Gvrw+XXHJJzeZl6qKxLnJmi0mciCpzRkuI0AwAaIFahIjYH8ELDE+Q6J5ITQVIHOKDjEk+vB7U5menu4CcDrYCRVecmAS/1kKURC1AbC0k9SRInHWklmQywe9rR0cHOjo6lP36+/sBAJMmyeORBgcHccghh6BQKOC9730vbrrpJhx11FHScZuampBKpdST96lrMfLEE0/gzDPPxBlnnAEAOPTQQ/Gd73wHTz31VNXnIooDKQkLiYtGh8isIrppvKI2vDEBQdaMyAJiKkRYwcETJCZEKUJ0jx3niThKIWF6DNsgVSCaC7+uMIlblFTjQlvtmiROkNScJMqLjNrQ4j1Mnz49sHnNmjVYu3attGuhUMCKFStw8skn4+ijjxa2O+yww7B582Yce+yx6O/vx5e+9CXMnTsXv/nNb3DwwQdXtB8eHsaVV16Jc889F729+t+HuhYjc+fOxV133YWXXnoJf/u3f4tf/epXePzxx3HrrbcK+4yMjGBkZKT0mijGfD6PfD5vPZfmfHPpsQXNyKETrQBa/WqapMxGG7w04HYU0e7HloyVHscwjCQ6MYZcLoEERpEd8B4LY/7cSDhKEXzLRhPzSEKQW5jHIXj/3SFqcqTS34D/PAugw29Dr7rb6T/SJd4DdUQAVogkEl5MRCKRoSbFEyiikx9bBtwmxZOoe3oNGPp4ou1sDFA39ZwX68F7D8IcZwBAIpGkHk1/dvKxzaLYTIULb2xdNw3bV+R+6UYi4aU+JxKy9Xvo/4toDuQ7oPrukP+hyiXUp9gfB4MIvlePRCL4GOSvsMu2IenmUcGPp6sG3ncIoc7zUfSvBbt37w5c+HWsIsuWLcPzzz+Pxx9/XNpuzpw5mDNnTun13LlzccQRR+DrX/86rr/++kDbfD6Pj3/84ygWi9i4caPRe2gqFot1u3pXoVDA1VdfjVtuuQUtLS0YGxvDjTfeiFWrVgn7rF27Ftddd13F9i1btiCZjMkt4nA4HI5xQTabxXnnnYf+/n6jO3sTMpkM+vr60H810BvCMpIZBvpugvFcly9fjgcffBCPPfYYZs6caXzcs846C62trfjOd75T2kaEyB//+Ef8/Oc/x/77m1nr6toy8r3vfQ/33nsvtmzZgqOOOgq7du3CihUrMG3aNCxZsoTbZ9WqVVi5cmXpdSaTwfTp03HaaaeF+mKty9+AWdveiyfm/x5jbQXk0Fly0eR8E0IWiVJxtAH/zqbsxvG+ccNIIpfz25PA1YwvkoiLZQiVbposNZk0p90Q80g/7/cfyc1kltOG56bh1hHhu2YSiQw2b34dS5f2IJdrqtjv8RaCd286pn3ZHS57Jyi6w5NZY0REm12TSIxi8+bHsHTp+5HLyY4bNcPqJkps3EC61hPvy5ZIFPzvz4HI5eiKA7rj6LRTWUuquXKwLuXvSiIBbN68H5YufRs5URJbCRsrSZQWkuoHyJLv0Pz589HW1qbuIICNvxhPFItFfO5zn8P999+PHTt2WAmRsbEx/PrXv8aHPvSh0jYiRH7/+9/jkUceMRYiQJ2LkcsvvxxXXXUVzjnnHADAMcccgz//+c9Yt26dUIyIAnfa2tpCfUELxNXSVsBAWweAIkZ9s38e5ZiSfb7/ZB+ahYGrubz3sedavcdCS5t3HmyG99iEyrXUiP0q7T+S8+YY8zjK7CftvUl5jDBteCm8Fem7rfAuSnSpdzpQ1buA5HJNvhgZoN4E7cen0xLJgekTVy/k8SNkGwt78hMJhTDBqyZilrWle6bfXK4buZz999Ac0bGUVzMK9sKmc7LeT7MPcYOMAXgduVwzI0ZoN4lOUGmYuBJy4Y+iHkdU8ROVcR25HDTEiE3672DFseypXQmrsOf6MH3rnWXLlmHLli148MEH0dPTgz179gDwMmISvv9v8eLFOOigg7Bu3ToAwBe+8AX8r//1v/Cud70L6XQaX/ziF/HnP/8Zn/rUpwB4QuRjH/sYnn32WTz00EMYGxsrjTtp0iS0t+utBFjXYiSbzaK5OfilbmlpQaFQO78ki23gakU6L41t0KpJGq+REAH4xcxEQauqku5s0B17wuUFtIoII0JUFwudi0nMaxCpfqGqcBIhsnmrrnQ2Kbu6qbPdCJruRMdVZeKEESWyMapV3ZWmmtk2UQW1umBWLboQSQCrLiSOY968eYHtd999Ny688EIAwCuvvBK47r799tu4+OKLsWfPHuy33344/vjj8cQTT+DII48EALz66qv40Y9+BAA47rjjAuM+8sgjFccSUddiZOHChbjxxhsxY8YMHHXUUXjuuedw6623YunSpVWfyyB7d6JZW0QrnVcmIvQnWEnafxQJEZpQQoR13fAsInT7Hmr//sx+3ROhjggxtYLEJD5aUf6l0c+jwGQsbeHCe48ygWJSKj4qQaESN7qiRJZ9U2+1OAZRaXFSESb9NyxOkNQbOiGiO3bsCLz+yle+gq985SvC9oceeqjWuCrqWozcfvvtWL16NT772c/ijTfewLRp0/DpT38a1157ba2nFkBWW4TbPg6rCNs/rWjHGyuAjRChkVVSpe++2JMl2Se6A7UVIbYCREN81PWviII3TyuBouPiManXUUtRopsSbEKjxxy4omiO6lPXp9Genh5s2LABGzZsqPVUSuTQCRLAoeuiidwqonK5pCV9RfVLAEFRM4KORYTAEyJsvQZWkACVVhKaaomQmMVHd8j+Oph4EURzkYoU9jPStZrIrCWyA+rUGhkvokTHksBP/RXj3DWO+qeuxUgjIXPRBLZFYRXRRSVatKurmgiRtxRt6ZMT65rhnTRtREjEAsTkVyI6f4fxC5uiuoboiBX2PUciTnRdNK2K/UC8oiRKQWKK7sXbVCw4QVKXJBEu7GwcLXXrxIgFtPDQddEYW0Vsg1bTmu1otIQIOPsIPdQ2UqyMPunTF4heph/N/pJ9tiLEQoDo/CrCnKOjzOy1iTMSzV0mUoxcPDouHZUwCSsqdPrbWkl0j01jYiUZj4LE4ZDjxIgmOY1bW5mLBjC0iphgEicCpo22EGHdLSy0IKHvLumTvkiIyKwh7ElZxxVjKEBUvwLTczFPbHRS+6Io/SE7Fg8d0WJa0Z3+3EILE9EAcYqSsK4bk8yauEr6N4ogcdYRhxwnRiyhrR8qF03sVhEVIvdMZEKEh0iQ2FpDbEWIhQDROe9Wq24Z7zg21hDbcdjPQnTtjVSY8FJ7VZYUVVxJr6Af3dfGdRNHqq/pRbsa1gsnSBzx4sSIIZ7wEKcxqUSKtVUkKvdMJEJEdmLqgZfaO4DgnaVMhABia4iNCDEUIHGID7Y9qcPXJZmH7dgyZIJDNI6sj4440Yo3If8jnihRWUNsrSVhrCwqQSLqVy1MxEIt030dDj5OjISAtn7QLhqeVYS4aADYWUVkkLbGQoTGRIiQR7ZQWYZ5TdqyJ3Jba0iEIkR23ta52FezonsYbCwiJn10El2kVhPyPxNZM4BwosRmXFE/nboktoJEZrnRJW5B4qwjkdMFF8Dq48SIBSLrh6y2CGBhFbGptKqTOVOCrSWiK0To1+xdYQ91UFaQsOhaQyxEiIkVJC7xQf+7SUVkm0V7TZHFCgHRCBReexNhUiFKZDFZYUSJzGqh6icSJEB13TYmNIKFxAkSRyVOjGjCulxEVhFee6VVRJTKK0PknlFR4Z6xESL0SYx38qNP8ryLga41JCIRIjo3iwSGrvDgaMu6QDQvmUgxFSjdijYqYSIVJW2Qu28A8wwcmUgQWSUa1W0TpyBx2TWOeHBixJAcdQE0CVyVWkVo0sw+WdBqWtFGGSdiK0TIIy1I6EXGyOJm9EleFfshEygKEVINAWIrPMg82qnX+wRtw6Bz3WPfg6kFhfd97VbsB+TXZuEZSBZTAsiFh424sLWSqNw2LNUSKPUuSJx1xBHEiZGIELpuBjh37zL3Co8o3DNCIUKjK0To1/tz2tEnGt6FQZauG4MIsREgOuKjnm4QZXMRXf9471HXemIjTKoqSsK4bqJ025gS5QW6EVw2DoeHEyMWmASuBhjkPKfPb2nJPrZfWtLGCLa6qm6J9x5qu+okplP0TOaSsRAhpgIkTvGRgud5ADyjUd5yHJa0ZjuTG3RdgRJGmKhESSskga4yUWJiJSETiUKQALWv3BoFJoLEuWsioRdQhBrKaVM3aRScGLHEOnBV1yqiU1MkMvcM7wQrEyLkkRYkk6jt5GQuExg61pCIRIiNANE9z6Y028WB6thpyT7d+iFA8HMKI0xEogSoFB/CmBKblGAbF0zUcSS1Ik7R4ASJIzqcGNEkp5l/pQxcpZFZPnjbZAvhRSJEeNt4QoR+TU5Gb6G8vDnZzhMkEVlDVCLEVIDonFNTGm1EdFGP2ivlcjApeJbibEsL2upaT3SFiYkoofexWIsS01iSasWR6CCy5JA8zgEABWq7yrXj3DWO+seJEUNMXTTKwFVeBg1r8aBJS9pUVYjIkBVEI1haQ2xEiI0ASUn2qY4XJ6rjqcRKirMtLWiryoiRCROZtUQlSlo5xzMWJaZWkmrFkdhk9ajQCQaNy4oRZlwXxOoo48SIBSYumgC8dF7RflnQqo4LxwhbIUJOQvvDq0pbBF9wWASo0t9MnaBU9rWpAEkJtsuOYQJtGRmz6K/7P5bNUTRGinmd5rTRFSYm1hJ6rqzIEGkDqSiJ20oSldsmDkESJS7dt2okUb8lAqqMEyMRouWikaXzAnpBq7x91lYRGyFCn3h4J60QsSEyEaITD8L7YdsIkHqrwhrWIiIag9cvRT1Pc/bLhElYa4noWDxREomVJG63jYioBUktrSMOR3icGNEky7gPjF00MssGu9+kpgjdLjYhQp/oREKExIj0MNsAK5cMPYyNK8ZUgMQtPkwsI6r6HzxsxYqqlkiKep7m9A8rTHiipJWznSdKIrGS2NYk0RUkquJo1baQ6AoSZx1xVBcnRgwZpoWHTm0RW6uIrXsmEiEiqhnCEyJkG7t4oIEQsRUhulaQFGcbbzzdfbLjR4HpuDriRdcqIrNepKjnaU5f2fVb5MYhx2MFhSwDJ7SVJO5sm/EiSOplXMdEwImRmDBK5+VZRejtaU5/6dozpkJE5JYRmX5ZIUKeZ+BdRVr8bb3MI2DlklGJkLACpBrCo5t6LAja2NaMUc1NJFbC1ApJUc/TzD4dawk7J9F74ImS0FaSuN02jSBIdHCZNY7q4cSIBdqFznSLnLHbdWqK8MaUrsYbVojQwar0a/jt2CuspRAJI0JS4GMqQHSFR9Q3gTbj6QgYnUJmKneNLAMmRT1PM/tE1hKZKOG5abo522KzkkThtolSkMRBvVgxosyoaZSltCm64AJYfZwYsUS7/HuUVhEr9wyPqIQI3YY+EUiEiI1LxlSE1FP1VfYYXaj0aBFssqJk84qikJks0FQmTNLMdpUo0XHTmFhJuMJcZCWJwm3DIypBomsdMb2w6wgSFzviqA5OjGjixYoEVzgzqi0CmFtF0pL9odwzoomR/aZCpAflqEx2fwhrSBQiJM7qq1HfiNmMJxMwURQy082A0XXjiESJKEOeZxHRsZIY1SWJwm0TdwxJLyLI4Xc46hYnRgwZYK4YxoGrJlYRCPaXJ+MRyj0ThRChH+n9HCFiYw0xFSFRV1+NQnSkqEeRZSRtMa5uYCqBfa9xpObyhElaMA+eKBkRjK1jJdFy2wBmVpJ6ESS1Cjp1sSOO+HFixAK20JlROi8QnVUE0HTP6AoRGpUQYcXHKLPfwC0TpwgxFSC6woOdSxSYjpkWbBe9B55IiTo1V7Qv5T+mBcenBYgs80ZlJYkkuNWkamu1BYkKmxiMWguSCVyJNWzRM9GNTQPixIglbN2RAFFZRXSCVgHolXvXESKs0NAVIvQjIBUiNtaQlKQP73XUAoQ9vin0fGQFe03ri6QE29OC7ap4DxthYlr2PeU/piXHpo9pI0jIeNrBrTqCRLQ9TkEiogdAv2Q/z8pZTVzciMMcJ0Y08dwzbwW2aQeu2lpFROMRAidWkziRKIQIWz+ETKbTe1C5ZaolQkwFCHtMEXFEwEdVXyTF2ZZmXquyZ2zSc1VBr7qihL42844VNrgVYH47YeNI4hIk1Uz5rbV1JCx03JqjEXFixJCcrosGqLJVhEYnjZcmrBABSiIEkAsRmYAQCRFTEcI7p9qKjzCig55HEd4FsBtAk6KfrnVeNDeeSElRz9Oc/TILhyoThj2mjSjJKo4nEiU1s5KwmAgSHjaCREajuT0msKvGAcCJEStYF40wcBUIZxXhpfJyg1ZZqwj9XOaeMQlWZV+zbpm897TVf8pzy5haQ8KKEFMBUqv6Irbji65RqroiKWZfmnktEiam1hITUdKHSkSipOaCJGxQq0hgmAoSWXyLDVG6V5yrxmFGc60n0KiwFpIA7InQ1ipCb5cGrQL8OBFC3EIE6vgQ8pwu8tNDtU2hfJHs5vRlhQwrZnqYtqwQSSF4DHYskYuH9yejW/Kn2s9rL8NkfrLiSinwPxt6vqJj6xxH9J5448pEiegYorHZbbw5t4JzS8aLB2Mv8qKLvu4FWNRO5AYRtZetgh0HpvEtDikkgNX2z2DReABYt24dTjjhBPT09GDy5MlYtGgRXnzxRWmf3/zmN/jf//t/49BDD0VTUxM2bNjAbffqq6/iE5/4BPbff38kEgkcc8wx2Llzp/bcnBjRhCc+tF00gL1VhEYatEo3kAkTIBohkgBXiLDuGPqiYGoNYfvyhAwrQmhSqLzIsj9mej46F/WwAsIE22PJ3ofo/RNS4AsT0XFVx2HREQ2844uOIRtbtI33v41UkKj6ySYCiAWJ6J/eazAXFdWsAuuoNo8++iiWLVuGX/ziF9i2bRvy+TxOO+00DA2Jo+ez2Sz+5m/+BjfffDOmTp3KbfP222/j5JNPRltbG37yk5/ghRdewJe//GXst99+2nNzbhpDsnRsBAxcNOz+0FYRwM49Q6BPhDZCxId8g2jxwW6zESEEU1dMCpXY1BcxFRaq9gV4YrML+rcAqhpXvGNGmb6b8h/TguPqxJboum9Ewog9PuuxiNVtI1r9lyaOgNZ6p54DWR0ytm7dGnh9zz33YPLkyXjmmWfw/ve/n9vnhBNOwAknnAAAuOqqq7ht1q9fj+nTp+Puu+8ubZs5c6bR3JxlxAJjF02aem1qFVEGrdLiQ0eIsLeYEQgRWcBoVEKEnTZ7R52C2AJCI7qT17E6VMsionM8kznSyCw/IqtJCnJrCYuupYTXv4uzjT2ujoUkjNsmAGsh4VkgeNt4H7CJ9WI8XOxNRZZtdpCLTclkMoG/kRG2ciCf/n4vRXzSpEmhjv+jH/0Is2fPxllnnYXJkyfjPe95D77xjW8YjeEsI5YoXTRRWUXo7VyrCNuRIBIiNJZCRBYf0kXt72LaAHYiBIJ27HhsX9EYvHF099m2HfMfu1Be1FhFVJaRbsF+mcUE4Fs1Uv5j2mB8HUvJMHNs1pqRgrysfJTZNkoLiW6l1rgCWrsF7XVR1SFxwadVI+zNjF/0bPr06YHNa9aswdq1a6VdC4UCVqxYgZNPPhlHH310iEkAf/zjH7Fx40asXLkSV199NZ5++mlccsklaG9vx5IlS7TGcGJEE9Y9A3BcNLLAVcDeKgIoglbp5zplpPfn7ItAiNCEFSJhRYiJANE5GcRl/QhzTJXw4LXRESaAWkCk/Me0YHwdUTLEvGbhCRL2mHG5bepekNQa56qpJ3bv3o3e3rK47OjoUPZZtmwZnn/+eTz++OOhj18oFDB79mzcdNNNAID3vOc9eP7557Fp0yYnRuJimHHRGAWuAvZWEQDyoFX6OX3y4rlk2H2GQoR1lwDli0mS0y7Fac8+F1lDTERIFALEQHQ09+mXTG0e9a5szb1ZNLfq/eyECy4SVMKDbaMrTAB9q0aKep7mjM2KEtV4XfCsSKQfb5wUxoEg4WESP2Iba8LewDjryHigt7c3IEZULF++HA899BAee+wxHHzwwaGPf+CBB+LII48MbDviiCPwwx/+UHsMJ0YsoF00AMwCV9nXaeq50ioCBINWeZVUeXdRIhHC22YhRGQZCylOm7AixLa+iG62BYOJ6IgS1XErxIrKbWMjTHRFCSB34ZAxReOxritWLOi4bVSChJ4Hb0wesQmSuEvG84iyYiugZx1xoqaeKBaL+NznPof7778fO3bsMA4yFXHyySdXpAi/9NJLOOSQQ7THcGLEEmn5d0AeuMq2Z60i2kGr9PMBVJ6wVCJEU0nrCBFan0UpRFLUcxsRYiBATERHskeVZREk4VtGkj05NGlaRiq+YwzsfLmWFJHI0BEmqvgSUSZOyn9MM2OqRA6LjSDhzU9mJeHNyzjLxmS1X9M2BJ4giSITx1lHJhLLli3Dli1b8OCDD6Knpwd79uwBAPT19SGR8M43ixcvxkEHHYR169YBAPbt24cXXnih9PzVV1/Frl270N3djXe9610AgMsuuwxz587FTTfdhI9//ON46qmncNddd+Guu+7SnpsTI5qw7hkgZOAqIK8rYhS0KvI108iEiEbmDA0rRFhRsQ+VQqSaIiRCAWIqOqJEdmyeUFGKE1NhEtZakoKd64b+mvPEAz1Gyn+kj2PqttERJBXYCJKw8SNhiNoq4oiCoZ4mtPSq1oeQ9EcRJkv3bty4EQAwb968wPa7774bF154IQDglVdeQXNzOdH2tddew3ve857S6y996Uv40pe+hA984APYsWMHAC/99/7778eqVavwhS98ATNnzsSGDRtw/vnna8/NiRFDcrlE8FMLE7iq2lc+KvWcZxUBgndNEQoR1ioiEiK8QFVdIRKXCDEQICaiI5nMqhsxdOa9dJpEIoemNt10Go5LkMyBM19WoNDvMzJhYipKUv5jmhmLHYf+SNkxdFwsKcQrSCqsI4CeIGExqUHCMgmVF55aW0dcIGsjUSyqhQsRGIRDDz1Uq9+HP/xhfPjDH7admhMjNmQHEuEDV9OSfVWrgRSDEOmDtzaNynoCZj8gFiIyEaIhQETWD5UAsREdLAn/Ktvh5/Z2IosmP0BCWq9GMQeeSGHfDy1OtISJjrvGNlsm5T+mBeOIMrJMBEQKNRAkLDrr2OgIkqiDWaNe5dfhiBYnRsKgE7gaxiqiPPHRmAa5RSxE6DVFdISIqQgxtIKYChAd4ZFAeHFiMx5PtPDmywoU+r1qCRMTq4iOKNFJCeYJgn7qtY0gocePXZBEWaWVpVEEico64mJPHGqcGNEkl/NO5tkBjpuGhg1cJdtk7Xn7hEKEPqHYnFx4C2uFFCIpasgueHOXWU9UQiRiEcITIDLxYSM6kooLUgcKfrthtDCFj9lVoHXnw4oU9j3R4sRamNiKEpN4km4AtNvcNDNGlf5bdUESdYaNLnHEmjjiZLgjibYO+5iR4Y4i+AuZNR5OjFhgHLjKvk5L9g2AOdGZBlDK4kXYeiL0tpBChP5IRG3DWEMMRYiJANERHyqxEQadsXmChZ23TJzwhIlWjImtKNGNJ0mDTxSZMfT4sQe1RpVhw8I78CQAbxmOE4Z6tmzU67wcpjgxYgvPrSILXGX7qMRLxQUqKvMq7Z6JSIiwAapjnLY8IRJShMQhQExER9LAgtLuW0YSyFZYRgAgq4gf4c2LFSgycUI+Bx1rCVD+bGMTJWyAaz+1j13BIIyISEFfkLCwY9FYxY/wto0X64gLZHWEw4mRMPAyadLM6yGIrSLsvgqrCCEKIdKLyjgRIDIhQgsLkRBJcdrQ7XjHZbdDLUJMBIiO+DARHbaojsETK+zcZeKECBOVtQTgu3GsRInKdSOq5GraNypBwhMf9Dar+JEo4jRMC6E5d42j8XBiRBNygi5kkpXVIlWBq2SbrH0AXtl33msVPPFBx4lEJER4lhEdIaJjDQkhQkytHyaio8fwZN/up2T2YBD7wPcRD0hMzry5sQKFfl8iYaKylgB8N05z35B5TInKSiKq5CrqK3P58I4dlSChiUSQ2FhHejjbZEQtSOrZVeMYDzgxYoPIRZPmbKNJM/ukVhETISI78cjiRBCdEOmiXheYfSmmDTsdnqiJWITYChBT0REG2bF4QoWdOy1ORFYTG2sJESUVVhJAzyqiY+kgU0gBeFvQVyeFmG4vExYmgiSWDBsVOhaVqMrEVwMnZnhkkeC6bfX7F+ACWCc6PBcNONt00n8DqGJFZBdH2mfbw3nOiROhd4cVImC2030srCEmIkRXgIjEh4nosMm2aQMAtKATuQrDGo2o9ghvfqxAod+byGpCW0xk1hKgLEysRImOlYR+S/R0U/5jmupr4rahofumIK9DIuqnM4cKVAGtNnVFeNaRagqSehMU9TQXR1icGAkL7/yhEhy8QFeuVUQlRER3TrT40IgTAaKxiJDXRei7ZTSsISYiJCoBEnVNEV1kx2WFCjt/WpyIhAnPlcOzlgCVbhxrUSK6qPMsHTQp2AkS2THpMdlxwmTYRFIQLepgVoejcXBiRJNCxj9JDwEVVjXeXRkvcJUtcia1iojWoWH38fYD8noikC9+ZypE2BuUFPVcllWjsIaEESEmAkRXeIRJ7/UsI91IIoe8pJ2s5ogqlZd+bybCRMdaYuW+kVlJRBd1chhe+q9OHEm1BQlNQ7trZCXhdXEZNQ57nBixgXcySgu261hFAmQQToj0QFlPRBQnAtgLkS6qfRH8+BBLa0gYESKyfsgESJw1RVTIjq2TLUNQCRNTUSJLDaZFiVCQsK95AoW+kLPpv2nBOLUQJDShC6LZumtkUbZxUm+uGsd4wYkRW3gZMxBsS0NuFRkF+HdQMiHCO1AEcSL0vpSgjczaQe9XHUdhDbEVIaYCpBrpva1oAtCNBHJoU6yyKao5IkvllVlNeMKEZy2RuXBUokRoJTFx27CvefVI0px21RYkxgGtYXHumkrGhyDKhQxgzZWyBRofJ0bCouOiEbWVWkUGmO3SjhQ89wxgFCcCmAsR0j7J7OeNobCGRClC4kzvNYkrafXTeRPIYtQXI6JgVdEcbFJ52eOQz8jUWqISJUZWEp5AERU649UjSXPGNAlGpaHHUx1bNqbUoBGXdYRuw3PV8MaRCZsoXDUynFXFIcaJEV2GqEe2TEQaYhdNmrNNaRWxESKse0YRJ6IT/2EqRCDYzxnL1BpiIkKiTO0VjRcFJsGqgF62jLdPXWNEZi1hRYmJ+4a1khi5bdLUPlYUAOI4Ep4gMbFs0OOxmKT80sRiHelXtnI4GhUnRmyQuWh4d1Ja7hxVXRG2Ax0sxnPPANI4EUAdsCprI7KISKwgMiEShwgxFSAmosM0roSYYhMYxhhlWhUFrPLmEggqjajGCFBpLWFFiSimRBTkqnTbqNw05Oshc7+kEJ8gsU35rbp1hKWR6o44HEGcGAmDyIQrctGw2yusIhnoKReeEFG4Z+imJgGrvDasEBHVGZG4ZVTWkKhFCE+A1HqBPNn4qoXxKhbFi6DGiKko4VlJtNw2KjeN9gVeo08UgsQ2oDW0dYR982ydERt3DlBbV42DxosZkVUeUvUfi3A2taXuxcirr76KK6+8Ej/5yU+QzWbxrne9C3fffTdmz55d66l5pKHvoiHbta0iPBECiIWIosoqEBQIOpkzukKEZEMIhIjKLSOyhoQRISYCxER02FRlbfYtI90YQIGyjIhKwKtEio0w0QlQBbz3J4sp4YmS0G6bLuhlzMiybGhsBYkIk/gRIabWkXrFxX44oqeuxcjbb7+Nk08+Gaeccgp+8pOf4B3veAd+//vfY7/99qv+ZMiJaBCVMSOA3EXD215xx8RaRWyFCJM9I4sTobulmH28NiIh0gMEEkQ0hIjKGmIrQqISINUqA29eAl5etAwQu3NMAlRFga6ieBKVlUTbbUOXhjcVJDJRYFOl1SRLh0ZqHTGtPaKyfugEsprgrCKO2lDXYmT9+vWYPn067r777tK2mTNn1nBGPiKBAcjLxHOLnKliRegTi8w1w77muGcA88wZHSFC9qcRtJRA7pZRWUPCiBATAWIiPGxTfJt9U2wSORQY06oolVdVAl4UtCqsqGqRNaPjuhHFkogECcBx29AfichyYSpIokj51Y0fsQ5mrYZ1ROSqaVScVWY8Utdi5Ec/+hEWLFiAs846C48++igOOuggfPazn8XFF18s7DMyMoKRkZHS60zG+2Hn83nk87Lal3ISxXzgEU3wTlhZeBVZW/y/VnjlNvsBjABoh3ciIn+D8HTCKKiNAyifyYoonzjeomYwyd/XQz3SK9KN+WP1+o/58n+3E2Ux0YHK9Ns2lEUE+9hMPU/5j3SwatF7LH0+hXxp5d7m3iww6guRUSCR8C+eeaAzIESGvb4Ba0j5zN5Z4X4pv06UnpdTZ4OvhxGk7C5ht/HGr8TOv9ucbwk80nRjpGIbwI8b6aNU8CB1Uu7xx8ihM9C+h3ovRCT0+tty1Ph9jChp8z/HYf/1/v4FcsD/IrVjsDRehz+nrH/sDgxg2N/X2TaAXM4XOgnvMy+Jkq7+UmXjxBj1/QG87xh5q13gX/RpK8p+CAoI0rcPQZHQy+yjbyraqOftkue0Hh0VPO9kjhsQI61A4HvJKhU2DiAJYBCJhPd7TyTYOjXs60kInjsAvjk3DuQ1dNT7depm8OMkEglve5jzfBT9HXY0FYtF1bejZnR2eie3lStX4qyzzsLTTz+NSy+9FJs2bcKSJUu4fdauXYvrrruuYvuWLVuQTPLvQB0Oh8PhAIBsNovzzjsP/f396O2Nx4KUyWTQ19eHn/Yfh65e+wDWocwYFvTtinWu1aKuxUh7eztmz56NJ554orTtkksuwdNPP40nn3yS24dnGZk+fTrefPPNUP+sqT/NY/PYNiwdno8c2ry7ozTKWTJD1B/gWUYG4LWjM2kG4d8IDSMYJ0IiWwdQaREh9DCP3czrXoDcGbci6HYh1o0k1bzPf+xCpUWEdtWkqL70PqpNopDH5te3Yek752NkP+/OgrhmiEWEZw0BytYMkTWEbwkJ9vWes1YQ1gJSOR4Na4HRgZepwqMp34IZ2z6IV+ZvR7FNLwJ+WDGm6JiDHDM2azHxtiWZ18Hx2PHZ+QyUvgCVY2Wp4w1T+4iVBEDAbZMYG8W/Pvc4lr5zPnItbUGLBf18UPCctlakNfqKxmdf06U96K8SfTy2v2iOFa4a+vuqSuX3LCObN7+OpUsPRC6nrKCISutI1CXkuwXbJwm2A3IXi477RXRMzzKyefMfMH/+fLS1tQnbqchkMjjggAOcGKkyde2mOfDAA3HkkUcGth1xxBH44Q9/KOzT0dGBjo6Oiu1tbW2hvqA538qZQxtyQ23lE14BntWQeEnyCJZ/H4F3zhkGszrvKLyPv8Xf0QzPlEqbU+lFp2QBq/RjmzdsHmVBMuw/7wKwz/9L+W1IFswYSu6VkgeoG55godeaEZV37wLwOjCyXx651lYke3LIoRXJZBbDaEECWYxQcRMjaC7FH+xDM3owgH1oKomLPMoXeu95tvSpAWURMkq1I5d4OtaCGH3pWA/aEEyLGZEql7ltSJ+EIu6kyf+pJdoGUWyrDCDgCYsOzpj0Rb+TurgE40DKV1AS69FeivUoH6fNv4qSMdsQjAdpY4RWC3KB43dgqDR+i39hJX1bsK/UrwkjpX5NbftKcSRNvmilRUmupc0TI70Ixn6InpOPiHbr9KH8+0xS7ZNU+06qPetSoa30+wTPW6n+rUx/WmOwX51R0YtJCAqS/ZjXfSDKKJdrRi7Hul103DDVuu+UzUW2T6csuvrCHfZcH6avw566FiMnn3wyXnzxxcC2l156CYccckiNZsQgKn5GEJWF517cRDVGAH0hoghaFW2XpfBCso9+pG722YwZWbYMLRx4Aa2qwFR6TDbg07S4mWlNEVUwqygwVffYJrVGRFkzlQGoGgXMJEGqvABXUcYN248X2Ap435fi25wLgChANKqgVlFAq00wq2jebJ9AMGuYVX2jKCHvcNQPdS1GLrvsMsydOxc33XQTPv7xj+Opp57CXXfdhbvuuqvWUwsiFB0+wtoivKh5kxReDryaIgA/e4ZtIyvzLhMi8INVgYBFBDAXIqIMmbAixFaAhFkgL9i3zZ9HDsFbbw+ecFEJFJOsmUrRIM+iIePoZM3wMm50BAkQXHAP8L9HQ31QEmXary02qb4V0IIk6kwaV5G13hlANwohLsND0a45UFPqWoyccMIJuP/++7Fq1Sp84QtfwMyZM7Fhwwacf/751Z/MIDxzruzaJCp0RuB+b3TuVHhFzdh9FlYRXpqvKIWX3serI0K9N5EQ4dUOUaX3hhEhJgLEVHSYrleTg/wCKyvvHmzHj1ERiQl6LH7tEEVVVUZYsG1FxdJkgiQwvm8loVO/K9J+TQuUqai2dYRFuzIrK07E8RIe9W75CJuS61J6xzN1LUYA4MMf/jA+/OEP13oaQdKQnxRJHRKhi4ZXW8TkJCJwz+haRWQuGNKG3aeorEoHq5IYEcBciIisIayAoIWIzIIi6s/2ExHVInmJgLiqtIzIqqgSVNVU6ePQY+qKEmG9EEu3jYmQYdESJFHWIOEhEiQ0kVhHaEysI41SsdXhUFP3YqSukRU/IwjjSkxPIoo4EVGlVVpQpKjhRO4Z0kZjFd+AEKHu9ERCRNctYyNC2H68vrw+or4qTONLihgLxD+qjm+yaq9KmOiKElMricxto1MkjfRJJrMoZrxiHsmeHHI5r28sgoSHrqAgRG4dCRM7ooJ11dS79cQxUXFiJApIddW0pI3QRWMiSiQBq4BZ0CrAd8HQ23nBrBKLCOCl746gxUiI6FhDdEVIlAIkjkXyEhhGE0eWiNwuNDZl3umxeKIC0BMlpm4b3TiS0hwTlCgVrWcTRpDAoK2puyZy6wiNqfWjmmIjyjVqGjst1REenVwqB1COFUkr2kmtJTwXjSweRBOboFWeC4ZuwwtYFQgRep0ZQGwRSSCrJUSSyJX292Cg1D+JbEV7Xh+6LU+40P3ovuyfCDInkz9evRP2M1Mdmzd30XvljRV8z5WfTWUMjl5ftl2wki5fRJI+QrcaE0NSgiOMK57zfkopQdu4sTqWyYV5olzEx2e8SA6d1K/a/I9XP0jGY489hoULF2LatGloamrCAw88oOxz7733YtasWUgmkzjwwAOxdOlS/PWvwcDo73//+zj88MPR2dmJY445Bj/+8Y+N5gU4MWKHKqWXtLFy0fBqi/DguGcIOkGr7L6U/ygLWIVYiJCCZqSYGU+IEFghwhMpdF+6Ldue7WMjQHjIhEUYujXHVIkjlTBhx2L7idqz81H15bUjbekxZX0ChesYCwnBWJAQRIK8PDl+W9G49Bg6fWXHC9ilZQXuJorgCMv4FCxRMjQ0hFmzZuHOO+/Uav/f//3fWLx4MT75yU/iN7/5Db7//e/jqaeeCizJ8sQTT+Dcc8/FJz/5STz33HNYtGgRFi1ahOeff95obs5NEwYSpKryMY8C0fmEqRMTK0RMg1ZFfSRxIkClEOFdEL1hgkJENz5ENzjVdKE8mfDQxTbVt4CxinqYsjnortgL6GXS6MSD0O1pN0tUbhuey4YQqM4rWWBPG1t3TRzEkVYsxcWFOPicfvrpOP3007XbP/nkkzj00ENxySWXAPAWqv30pz+N9evXl9rcdttt+Id/+AdcfvnlAIDrr78e27Ztwx133IFNmzZpH8tZRqIiDY2A1hgi39lrlknQqso9wwlYFRc0Cy52JxIi9J24yC3Da8ezarDtZG15gbBiq0RW+BcWby7qcXUtJzQ6lhKe60Zk7SDzoPvS/WTH4LXjiUyRQKRdf+Q7VxV3TTWsI1rIrCHs4CrLyf7M6/FoQZjY1qNMJhP4o5dECcOcOXOwe/du/PjHP0axWMTevXvxgx/8AB/60IdKbZ588kmceuqpgX4LFiwQLtkiwllG4kDqookJmQuGtX6I3DMaAasAT4jwM15040PYfrrpvbrt2PFZdEVGmDTfMb9EbSey2Mcpaa2qMyKynPCsJTrpvSapvEGrhqRmCNTFztjia6TtmL80bieyGCbHYiq1AhYBrQRVdo1pMKspIkuMdlZNL4C3LQ/uqFdySKIpsFy0aX+vTMD06dMD29esWYO1a9eGmRoArwr6vffei7PPPhvDw8MYHR3FwoULA26ePXv2YMqUKYF+U6ZMwZ49e4yO5SwjuqT9xyHopfQSInXRCDANWpX10YgTASrFRNkiMsjdL4sPYYNUee1EcSGy+BF6bF6BNFV8CfsXFTpj61pOgn3U1hJbK0nwfyQPjKXH57XhiU7ewolAWfhqxY/wEFkueP1NDAYpjWPEah0Zj9YNEar3OrGtIgCwe/du9Pf3l/5WrVoVybgvvPACLr30Ulx77bV45plnsHXrVvzpT3/CZz7zmUjGp3GWkTDopPSGpheVP0aOD902aNUyTgTgWzy82YmFCNuXZ7qXBaeyx+C1M7GAmIiMMKm+Y4pFylR1RkSpvDpVVcn4MisJ3d7WSmJiIaHfF6/aLLetKOWXYGLFSMGu9kjs8R9x1hxxjFd6e3tjWbV33bp1OPnkk0vxIMceeyy6urrwd3/3d7jhhhtw4IEHYurUqdi7d2+g3969ezF16lSjYznLSFRIq66a8FdU+niBCvXfikqNYhK0mqJeW7pnaIhFpLMkQKITIry4ELqNTIioLCAsojTfqGqOJDGsNbbMciJKy1VZSlTxISZWElEfXnve+xNn2AxXtOXFjwRQZdTUi3VEdBztW0LZ5NgL0XixnDirSC3JZrNobg7KhJYWz81cLHo3WHPmzMH27dsDbbZt24Y5c+YYHctZRuKAxIsEXDQZ5nFA8JpeII/9ofWiwirCs3ykmP30vojdMzxrg4kQUcWR2FhDTIuc6QqNMAGsA4p6AKJMGYJOHAgQbal3XltbCwlvPHoM9juSRSI4FifDhmsdoVFlzKRQp9aROKjnRfOcoKgWg4OD+MMf/lB6/fLLL2PXrl2YNGkSZsyYgVWrVuHVV1/Ft7/9bQDAwoULcfHFF2Pjxo1YsGABXn/9daxYsQInnngipk2bBgC49NJL8YEPfABf/vKXccYZZ+C+++7Dzp07jRe0dWKk7uGIEpVVBBCLDkB8R6dwz4iEiE5qrG6gqo4QUVlCaEwFSBQZMzwSpdiaHFqZY7MXbHZuqkXxeC4ck6qqZAxdt42JIKHfk25AK/teeYKEUBIkJmIhylTfFMwX0SNota/n9WfGi/WlduSQAPzgbbv+skUmKtm5cydOOeWU0uuVK1cCAJYsWYJ77rkHr7/+Ol555ZXS/gsvvBADAwO444478C//8i9IpVL4+7//+0Bq79y5c7FlyxZcc801uPrqq/Hud78bDzzwAI4++mijuTkxogs5eQ3Cwh2jaxUhyFbnpWBN0mGtItRznnsGEAsRzz3jlYHPU/tZMaAjRHSzZEzWqeH1F/WVkRgxEyu5DvFicKLjy8SJaX0RnijRrTWiY/FQCRLdDJsRRjErxQ2v/ghPhNTKOmLaz2qtmvFeT8S5aKJm3rx5JfcKj3vuuadi2+c+9zl87nOfk4571lln4ayzzgo1NydGwkCCV0OfD0TigxO4yv7H2CwZso0gSuVlD6vpngEqhYio6Bkva4a3n24T1hoic+WI+rCYCg4ZZKzRUe8EkNiXRWuhPCeeWBEFq3r71GvPkDFY9w1PNJD+MiuJrSAxGY/Owhrxv4zcsVTpvoSw1hETUog5iD0Oqi1knBXFIceJkaigq7EG4kVUVhEVHPXPWjnINhp2v0igCALsZNkz3uF4tUS6kUAOoxXb7YWIrjVER4QIY0kMxEdXpqDVbqhXLzacPTYrTkTCREeUmFZVZfuGFSRRZNjI4kditY7YLIQnGp8ltpgT1qVTC8sJL/g+DpxVZLzhxEhDwAlcBSoFRwr6VhG6P8cqAiCQPWMiGMIIER1riMwloytCZAJEV3DIYMfIj3qvk5kC2lrL+1jRQs9LR5jYihIdK4kojiRqQcJDFfxaaicLZjUphMbro8LWVUPT8HEjYRGJCmdJmWg4MaILOZHoFjsLYGIVkaziKwpctbWK0Ps4VhFA7p7xtpUDMz3LSBajiE6I6LpkZNVZS+0FAsRYfKguVgbFruhj2wgTlSgRuV9Y4UD3lWXb8MYWjatqS+8f84/dg0H8lblAGbtrbKwXKYSzjvD6m2IVN+JoZHJIoBgigHWYU825UXFixAa62JlJNdYSuibGXn5bNp0X0LOKaAStmrpneKiECC9jRrcOCe/YogycUnuOCJEKkLAmdF7/Mf9xCCidP5j/oY0wYS/uJu4X0+DWLJJGLhuTDBsSAB3sb2YdCUAEQ9zWER6qRfoiJYwrplEDYJ2LZjxiVPTsV7/6FW644QZ87Wtfw5tvvhnYl8lksHTp0kgn11AI40VEGTMmcAJXAXOrCMC3ikDPPaO35sywfxhzIUIX6eItmEe3ZdtXtBvJlv5oujKFSiEyyPzpMCT500VyXDJP3nzZ98UrgqYq2a5TxIzXj22jKozGa6sqiBZ8H+Jj0oKZECgVT1AVQiOkFO1UBdNUqIqzaWPrwqhWPIfDYY62GPnZz36GE088Effddx/Wr1+Pww8/HI888khpfy6Xw7e+9a1YJjnxEJxseIGrQOVJVFZXBBCeFFXuGXq7zMViK0RE/Uk7nmipaCcRIIGLukp8yMSGSnDw2pMpZSVjSOYURpSU2lOfIWlPYAWJSHCIxuWNKWtrUqFV1o63dk0J3m+Fty2syCCkLPoAEm1BW3tMrKmNgosXcZTRFiNr167F//k//wfPP/88/vSnP+GKK67ARz7yEWzdujXO+Y0jegz+AKGLBhCfZFV1RQRr1pi4Z+jtoloiNFEJEd6ceNYQgsiqwL3Y21g3wlhEeGOwKCwmNKz4Yj8XlZWEwJaUNxERurFF7DxZOjXbcQWS/x2O1ToiG0/VR4eqO86rceGP8hiNJLbU5JCAeAEKnT9xEHijoS1GfvOb35TcME1NTbjiiivw9a9/HR/72Mfw0EMPxTbBuoOk8KZRJXergYuGbKdJMa+ZkyjvxK2yitDbE6XHYX/4SjFBE6UQKbVhLARSNwyNrZVD1CcKawoPDVHCswiprCSidiYiQmVB4Vk4dF0/ptYRLtWwjqjaSSrWO6JkfAmViYa2GOno6EA6nQ5sO++88/Cv//qvOPvss3H//fdHPbf6QnYBktYXiQjRzUUK6pojsqJogJVVRIZIcNgKEVVsCEHoimHhXfjDxn/oohpfx1pCYWMlKbXVjCMRtZeNqRs/ooL3vZCNEYl1REZYYREqVmQ8YeOicWJjPKNtFDzuuOPwyCOP4Pjjjw9sP+ecc1AsFrFkyZLIJ9f4ZOApFNMznkEWDb2PfZ2iXrNum271iry6VhHvIsNkf2jElpSnZrFWDScupIRIgPDQFRxhhInsAkaPy7YbEmwnWSJkt//eSQYO+WxEWTeyLBeTrBhV3RBeNoyoXR4aWTOozOgp7eOk+pYwzY5JwT5NN0xfIxql9ki14j96QeVFOxoQbcvIP//zP+PVV1/l7jv33HNxzz334P3vf39kExu/9Gr8EQT+QNHFjee60bSKBLZpWEVYsdDNufst9+MJnRCr96qsITQqC4Roe5QWEt0AVhNriaWVpPxcL7BV1F7W1sZdwzuOrK90DJl1hKDrqilPQm8MVR+T/gDsgljHI7L3PpE/l/GDthj56Ec/iq985SvC/eedd14gu+Y73/kOhoZiTbBvIEIEl/AKncHfloKeT1yR/mtjFfGe8wuT8cSErRBRuWVKsBdnlStGZ7uIMLEhvHFk4/P20RjGksiyWEyCUHWFhK6wSXD66tSyCWyTxY4QTF01UcWNqKDnYBzE2mjZJy6LhpArneHs/ljrYSNjVGfEhE9/+tPYu3dvXMM3MJamVdnJjrdYXorThqkrwkvlNbGK8OgsiYjKGiT0dtmdMrd+iSxIVVeEsNgGo6ow7acTQyLbZmglManzAdgFtNpk18j2mcaOEErWEd1AVkJKss8mbmRCBLHGVcfEWUUmArGJEdkyxeMCUnlVWuxsgOmgi2QtGhNTskZRNN7dpMwqIl53hnapyANWeZVVVUKk9JZkbhkdEaJjHYnToKcbwKqz3cJKQjBJ0QXMAlp5+3XHYedkax3h1h0hyESIqauGR0qzncPhKBGbGBl3DFGPaZOOPEsIvU0SMyIy16pcNCnONgrWl65jFZG5Z0RpvLyxdYRIoL+OW4a9UOtaR3TFR3/IPxmmrhrVe1VYSaIQJKq2Nu6a8nhiEWFrHSkhEyG2rhob98y4yKiJ2qUiGs9ZRSYKbm2aqmKRWSOqukoQWUs0AldFVhG67Ls6TsDbRrtniuBf6HSFSGmfrluG99x0G6AWDraQcXsQ1KF91HPTrJohxWtOxg2dbUMybXSybFRZMbJ1Z3j7RIvpscjG4M1DBHdFX4Is0yYF8Y2H7po2jbr8SyTwznVOQNAMoBv70GndfwRtEc6mtjjLSFWQxYloxpDouGg0AldlVpEwQas0shLxvP0yIaLtluFZC3SsI7oWDN7YYQNYRcfVddUYWkmisJCYVGiV7eNZWXjzESFrUxeuGt1xx4WVpBY4UTPecGKk6pCTuUyECKquAnIXDaAMXAUqT9KmQauitWe850FfP3uxMRUiJVRuGd5zuq1IhPAIKzR4/WTXV5Egkokqk9cRCRKCbkBrGHcNi05V1lhdNaZjpizGCZVRozNonIQJXrVx0TjGG8ZihE7fZfn6179een7IIYegrW38mJDCo2sdoUzOUbloSFuKZFJcW0Kn0qZOvIcotkTm7hEKEYLKMiDaR1Bd9E0sGzZ9sop+utYSGyuJj60gMQloZcfRDWZl5yLPutGwoNjWHNFpEyZDxllEQuBEynjEWIz8wz/8Ay6//HLk8/nStjfffBMLFy7EVVddVdr2/PPPY/r06dHMshHgZtKwGTUEmXVEsPpoWBcNFCdkmFlFaGT1KsptKoVIRdbNCOfiohsfooodkYkQFTpWEltLio0osX0dQpDw9vPambpryvuGA214yKwjFXOyrTnCktJooxoztHEi7sXQ4rCehI0XcYJjomFlGbn//vtxwgkn4IUXXsDDDz+Mo48+GplMBrt27YphinVGP/hpvVroWEckJx7iohHB7uOcGGUumoq2mlYRfl95bImoDglAXSijECImIiRsDAgPdhzReKaWHNvXhoKktN/CXaOzT0tYaK2HZOCq0SGuuJG6xeRNRfkB2CyVMX7Ileof2/1NyFV7CXPnzsWuXbtw9NFH473vfS8++tGP4rLLLsOOHTtwyCGHxDHH+iDURUl2wuUIFJW/2CT4jidImLtGkYuGRe6vF18MTFJ4jYSIzE2ja1ngjaOLrWCR9ZOJErad7WsNQUJQWUfkxevMrCOy44RpQ5AWQAtLmDHrxmVjKzKiLnZmKzjss1IctccqgPWll17Czp07cfDBB6O1tRUvvvgistmI7kgmDIo7Qtt4EdKXbcvApu2aVls1tYoYx4kQTAJVCboZKnG7aXQwESWswDIVJAaorCOmgag61g3ecUSYuGqkWTWVA4upG9EQJ1FYPeJK6ZWNMX4sBBMVYzFy8803Y86cOZg/fz6ef/55PPXUU3juuedw7LHH4sknn4xjjuMMkatG8EOziRdB5TZRvIht4Cq9XZpmCdYKYxAnEoUQkV3seUTppqHHUF1fZfNhsRUklu4alZAIax0xcdXoCBUrV42t0DANYm2IsvDV8kNFJVqcEBkPGIuR2267DQ888ABuv/12dHZ24uijj8ZTTz2Ff/zHf8S8efNimOJEIsJ4EUC70FmgjUbgqjzIcFjYXnZR47pnCDZCxMTiEEZ4mPahs2l4Y5lYSURziFCQEGRrzvDa8TCxjsiOIWpjM34sQaxh+9UFIkHCbo9rPRqW8RUrQsghgZxf5cnub/wIMWMx8utf/xqnn356YFtbWxu++MUv4mc/+1lkExt/iO7+DH9kpsWaBC4aGvbO1MYqInPDiLZruWdshQhvPFs3Dd2WZzWxDXy1ESU0MQoSQhRrzugIiu5S1V91Vo3smNpMCJcLIHcHqz6/qCwkcQqJ8XMxnugYi5EDDjhAuO8DH/hAqMk0Pry03r+aDaFT7MgkXgR8F42s4mrlkHpWEVl7K/dMFEIkCjeNratG101jasWhiUKQcLAJZpW1qxjfQKiw45q4Ydi4EVV6e4Bxl1FDn4sGmEcRpm9Ut72Ji0a03QmR8YSrwBoFpRojtjA/NpPfs+bS6KIgPt3AVV2rCLnD1b3IVLhndISDjhCplpvGFFs3jey1zD2l855itI6wY9gEstq4aiJL8Z0QxGkhCWsVGZ/uGUclToyExdpSzP7AGZXPKeMuJAXtlF7VSVpV9EoEMa8Ht8mtIlpxIrZChDdeHG6aMJYUE6uNSmTIUn91nnMESRTWER1RIApy1iGUq4aF/GbCZtTEUvgsbgYgP5n1ILo3EUXgqrOKjDecGKkKIleNxg+wC/aWT87CeIC6YqpO4KpprIjUPUNgBYjM0qErRGrlpjHpb2sliVqQcCD/ryisI4QorBeRWD66mUcHzO6u4g5eHf/umZxfZMH2zzSA9bHHHsPChQsxbdo0NDU14YEHHtDu+9///d9obW3FcccdJ2xz8803o6mpCStWrDCaF+DESDhES49LkQgQWbxICtZ3ZTwXTRJZgwqafGuJtIiZwiyv5Z6BYJ+OEKk3Nw0tsMK4aNhttoKEh6a7xnSl3SjiQ0JlzJAxbeJGCCmNNuNG1Nham9i7Id65Lq4aJIhwccHxy9DQEGbNmoU777zTqF86ncbixYvxwQ9+UNjm6aefxte//nUce+yxVnNzYsQWIyEiu4OwyKZR1SrQqLrKQ+aiUaV0lmNFxBcqafYMQeSeIegKEd64JhYSEf2KP13CuGjYdipBIjo+7znne62b6kvokXwXKsZmREZ3hG6X0tg669Q4GCJ0f1lhaBVxQkSL008/HTfccAM++tGPGvX7zGc+g/POOw9z5szh7h8cHMT555+Pb3zjG9hvv/2s5ubEiA2h76Alfpcwweg0Gim9om3lQ4RbN0R5MTK1ioQRIqqxediIDVNhEtV8baxHCqSikYON5SMOIh27IYqURUkP9OJDbFw0ulaR8e+eiZpMJhP4GxkZiWzsu+++G3/84x+xZs0aYZtly5bhjDPOwKmnnmp9HKcnTYnlhsHyR6YwC+vEi1TORF3mXdWOK3pksSIEWcyIqo6IakzVNvYYUUCPN0nSbgiVFz3eNnY720bUR+e49PNBCL9bSWSRRTKwLYEscsw2Ebz+piSRQ5bzm4li7BI9qL1xoER8wi2+oNQqZsE08FUsiwTaQoisPIoAgOnTpwe2r1mzBmvXrg0zNQDA73//e1x11VX4r//6L7S28j/o++67D88++yyefvrpUMdq4H/jOMbE95yC8d2bSfaCzt1uDwZQQLtWf6NYER1Uwauy9jRRCxGWDPWoEhn0Ngjay/oR+gH0abTTIDGSRa7D/kJvIlhEfVmxQbb3YAAD9Z6uEquwoQfPCLbzaoxUk5isIu4KBgDYvXs3envLn11HR0foMcfGxnDeeefhuuuuw9/+7d8Kj3vppZdi27Zt6OwMt1Ch+1fqEvnFSnHnYBoMJzgX69QXqehjEcxqMgYXWayIjXsmbiHCjmVyoSfH62O261hDVK91RIeoPadvV6aAoV6+N1dkoQBQEgiyNiLC9A1NNywD0xuVAZhbR6pVAp7g3DMqent7A2IkCgYGBrBz504899xzWL58OQCgUCigWCyitbUVP/vZz5DJZPDGG2/gve99b6nf2NgYHnvsMdxxxx0YGRlBS0uL1vGcGIkVg+qrvP+E6Z2syLRuGLzKYpJG2Slx0UjrirCQi72Ne0ZXiJgGnOru1/2/0ZYLehwbQSJqa2sdMXTV6OwTkUAWI7W2bDScCBEtuFkLVC6asFYRAe7qFSu9vb349a9/Hdj2ta99DT//+c/xgx/8ADNnzkShUKhoc9FFF+Hwww/HlVdeqS1EAPfvjBHWRGp5N2F6jhZcQMIGr6riRZLI4a2KbQoRo8qc0ekrey7bphIiYdxHJsIkjCDR3ac6hqZIkblqbNwwNbF8sNSDCOEdf7SaE7CxjlQL556JksHBQfzhD38ovX755Zexa9cuTJo0CTNmzMCqVavw6quv4tvf/jaam5tx9NFHB/pPnjy5tEAugW3T1dWF/fffv2K7CvcvjQXduxbNO4GU4eGFd7S8tFu9EvAieO4e3nG0MjNYcWLqngkjROKqMULGZcUAjYkg0W1jI2gE+21dNSIiDTStJ2IXNfVgDWFvquK0iox/ITKMBEZD/BZG/QBWXXbu3IlTTjml9HrlypUAgCVLluCee+7B66+/jldeecV6PmEYR//WRiQDbUFiUh4+BFHEi9BUZNFEFbiqwiQ+JO65AOWF8rIAN9ZXV5CEjRdRjU9j6aohsLEfYYJYo6a5bwiF/gmXt4s6ShGiiCDWoQdAuPjJCcG8efNQLIoFzD333CPtv3btWmWWzo4dO8wnBldnJAbYH7vBj1/HUqpZHl5UZVJWqp2HzdoiqjEDyNJ52TY2z2miEiJDgj/T/iy689OtN2KSGq1Zi0SWom1TaZWFiFyTwmlG4wsCuscHOpk0IqolUnSFx/i3ijiCODESKVU0o3ZD6b6xyaRR7RetW1M6JmfBPKt0XnLxDCM+6LFshYiJ6DAVJlG5kHTFic1+H5mbLQrB0KlZ/0a0Om9ZvPD3j98qrNW0dEThomGJwD1Tr+EuDiMaSoyEWYSnOpiYGyMSLgpLiegkLFscr2IM5kLBu/jIsmiE6FhFTGHHEAWqio5la+0QjaPTjkVHkNgIEJugXUkchI0FJMq1ZuqOhl+bRiZsokjnDeGOcUJk3NMwRq+wi/CERhaAGCW2JzSLfibFzkQXHm0XjUlwn61VREeIyERIHJBx2xRt2PAFnRgS3XgRVeyJTmwKhU4BNBJTUtOaIY0GVwvUq1irslVEZwoNyCB60BKiGuFYY9kTpDTEO4liEZ6JjG6dkKrcpVYjWNSUephT2DooOuNVAZOaNBOSSP4vtq4ZUaXWahCxVWQcCBFHkIYQI1EswjMhiNhMHOuFJUrXDI9aZc2oCDuHsP1D1FcxXThvwlHreiVcDAovAtATOqZWkRCpvA1ju3eEpe7/1aaL8IyMjARWLMxkvDuAfD6PfD5vPY9Eq9c30Z4HOuAVJaL/Sk+S8M74JH2qyX8s+n8FAGP+NtIn7/0nOlFOT+uAlwLaDs/E3wqgxf9r9oflDQ1v+ObRUSRGR9GZH0OHf7wOFNCOAtpRRBvKnoNWNKEVTWjxtWkzmtGMFjT5Xw/vkbRuQ9EfzxvNf573HsfynRj1Tyqjo0UkMwXkQb3lAvNIPibeRbCJ2keeZ1GW0Fn/8yBt6GJ/vG82z1UyJNiugp2vwtKab0uUH/cJ2vPmPITK8zb9PunbiRwQyJxtAp8i8wiU/x/0c/prSp6OegccbU74TTr9x/JaGAX/OVmvqFjKZW4LPNLfr6a896aa8y1o9t8U+T62+m+EPLYxo435b6Tdn/hY6XHMP77/ODoaeCyM5YPvl/e5eBMMPpLPvIV5JP8/+jvVzjyOUm06mW30Y+k7Wf7wE4mxwGNw8qyI4KVv0l+IZoi/ICyTmPHYsVmROsa8Ziu49XK2dQJgzs+iqxPvpqsbSHR4/cOc56Po77CjqShLOq4xu3fvxuzZs7Ft27ZSrMi8efNw3HHHYcOGDdw+a9euxXXXXVexfcuWLUgm66PGgcPhcDjqk2w2i/POOw/9/f2Rr/dCyGQy6Ovrw9/2P4aWXnuT9lhmEC/1vT/WuVaLuhYjDzzwAD760Y8G6tuPjY2hqakJzc3N3EV4eJaR6dOn48033wz1z5p6YR6bP74NSzfMRy7d5t3ZDPp/owAwDM8POwDgdf/xLZQrR02CZ77s8V8TU2YvgE7vLoB8J7vh3Tkn/WZ9/mvyR/an/Dbd1DZ4j829WSR7ckgkcuhEtpRym0AWPRhEJ3JIIocElQKZ8Nt0YwBJ5ErplqQdcdskMIxupg5Ee34Mf9x2PY6afwW629722u3LIklM+8SSQDw/otcZZju9j90uasNzh/M8TqYuD532AgtJvi2BbUs2Y/63lqItnxO35ell9mvL9ksKnncJtvdy2vD6cbZl/UqsuXZvZ44yrWf9W/1hvyMJWB30v+s5fz8pfEb6ZpFAU74FM7Z9EL+f/wQG29q1+3nH8x4H/B9QrnT84HxyuQSyA17bQsZ/c6y7cJB5pL83aUFbUVYYcYnRRgv2+067dsjzgNGApMpnkEiMYfPm57B06buQy7UwnQcEz+lFGtj2osAL9uI4iXrO9mFfs311XDZMtTITiwi1PdGRx+YbtmH+/Ploa7Mxd3pkMhkccMABToxUmbp203zwgx80XoSno6ODu3xyW1tbqC9ozj9B5DrakBtp884Rw/BM4yU3DfGl9KH8w6fdNU3wzKMtKH/0vgukldlE/vbBs16OwrN+jsGzihYFQ8Mbvrm1FU2trWhqa0ETWkom7xY0Yx+a0OIP2+YPMooixlBADwZQAFDAGIr+WbFIXEm+GbUJ+9CMfQCAZoz4j55ptqVtGK1tOS+ttxVoay2U5uQ3DD6yJnCCyCpMW4THBM9563rwLK+61liTWIB9kMbutOVzZTGSBj+Lht32FoKZNRmmDf2Z0J/VIPgZM/Rnzboe6Occt1drq+86aWvym5QHI8+b/Y7kscn/rpQHzPtTbfMfy/+wQtsYCm1Bd8so9R0t9y4/7vOPu8+f+Ejp0TvesP+Yy7ci1+q9kUJLW/C9qr6Haf+R/i6MMY/kbZQnFnz0JgV/UsFHWj+UPo4cgicFf2uuxRcjZPIZ8P+pLOwPSec+lE7pDRsrEiJOpAfglDDyfmvM9rDn+jB9HfbUtRjp6emJbBGe2tHYYd86GTYJZAMxA0Jsq52aPI+iiinBNiBRUka9bjFM7yU0yjoz2WzEc7RJaKmHAOqqYXmXbpI502i/MYeUhsimcYwz6v2kTNxvYceoBSbVbScAsa+Fw3O3qJC15a7Wy/M7itRQ2Iqs9JVfVujMtK6IhlXECZEJTV1bRnjYLsJTfXpgnlYXL2HWDCmPYVGLRHTyjTu9VzS26HhRCwgdC4muRYJXBE3W3lEbohIlkaA6/xgs1Glk4bV0z+gi+k014G8ghwSaQ3wWhYrMpcbFWUZsqOcvfY3vGBJhCqfV8o49rgtDXOPWu3WpxtS82ms67AC83xHP6iFaHM8GXcFhU22VxiBORBdnKWl4nBixxejLH8W6DvVHFJYWLjyLiU28SL1csFWCxDamRYewcToClOsO1YhGiF+poCpr3ZkeRBa4KkPHKsIQ1j3jhMi4wImRMCh/BI0hQuKqtFqXFTsNF4OL9bim6FqOTI5VL4JtvGKiAbTjRXS+sDouYpsg0xisIixOiExIGi5mpO7oAfB2rScRLTYWjyQ3766GuItsJeM4tmSg3rPWYv8+mqgensARBa5GaRXRcM9MMCHixYzYW/NczIgjiFLS8e5SYlqoSnLTJDJhsxkHNqbuLFu4qNaM04tuKMbRZ0JiQmLPlgEiiP+g0LLC8eJFeOcLeht7FVdlwsRtFbF0z/AYp0LEEcSJEV2UJ/IIAuaq4juuHkO9DfL1Gm8nNhPRwWur0T/XUd3YjHJV1QiPy1ZbJY/kd8haM0T72f4mx5YS5Q2LTDjYWEV0KqvSWKbx6goRUrHa0bA0yNWijhClVwIQ/yDZuxSO6uD6iw2RWkWizy6IPVgwjjv5WlkHZMfVFQTS757FcSMgru8AcbtE8b0lBc9KpeD7Y/5Q0v6jzs0FaWP8+2ezaHgHY887vKu9bQaNbJ/CPROHEHE0PE6M2CD9/aruECK820nDuzsbgFSIyEzZ7MWE9b2zFwPVxSEX5uJBLraqi7Pouc3FulEJcwLW/ZwMjsG6TXRFCru+TCTio5ouHB2MCqPpumh0MQmiF1lFwrhnLANWnRCZcLgAVlu6IDm59EB8AuGJlRy0frTs8TTcC9lsEslkfaZhluhCPAF+cY1rStxuoChOyLIxQsyftXDE4W4pHyMG8ZH2H1kXTRisXTS8jqYT6qH62AT9mrpnGHQCVieQEMllk2hqtf/eFrMugNUBKE7SuncTirueAXgnxFqVF48C9nPqYh7rgbgEg824YVw09fSZxkSklg/deBEWUbxImhlHhpaLxqQUvAgd64iuVQSSfYbumQkuRBxBnBgJi/Ri0wOrWiNkbZSQd/XERy7cr3DBsCd70clf+6JQLycQ0TxqEcharc8kguPUOiA5CstHKV6kWuLe6Dcsq17MEyC2ZpoeRGMVkaERJ8ISVojUeXa3Q44TI7rITubGPwJJh4gyalRBemHvKlV1HaqdbSHE9CIcpSCpdZaO6r3XeSZNGAtIyR3EBK/WDKOMmzjS/mU3RTpWEZV7RmJB0QlYdUJkwuNiRqKiFRoR8ZbLakfMAHrQM97yiKOkG/XtFqsTC1O2FP8R7YU+TAGzWNakMXXh6KD189MVJXQdI5XoiGLxToviZjROiJTIDnaiqcn+O1sczEc4m9riLCOmSC8Eoi+VZvR5FOm9gNWFVFX4rGZrfuhk0cSRURPWqmHb3zRexFSY8MaytJCwsMGpquDVKGI/IgleFYmNtGY/UbyIjmgp/eajctH8VfHHYmMVkWERJ8IyQYSII4gTIzakZDt1rR8xWUkUQqTaoqLWcQZCdC62toJCt1/UFo46sZhUg1BuRpsiZWEwOo7MGsITIGEsHTZrZ4Vwz+gErDohMmGp0ytFA2BdayTGX5BSiDRA4TNZrRFbwoxV67gPW6L6/Pz3X2tRGcYlVLViZyqM0oNjWi5CiI5VJKR7hiVqITKR6gyNQ5wYCUMXJBerHng/VvKrkQkU/0dsG8FDCp9JqGbhMyOBUo9pviwmgiRq8RLFCVbXDTNOg1dDEWUJeJbIXTQm2KTy0oRM47UVIqJEoBRnm6OhcGJEF1VRKCODRy/zyEAqqpL0XrrWCL1tEFqLeMlOyqbpvcFplt/0MKdd4MJFTjY24iOKuJGwcRHdnD/efh26NOejeh+i5yIi/Fx04z5040XI60GDHxIbL2JkNVHVF0nrDxVoH0q0yKwhvH1RBKPK0BUpVRQiPFKC7Q1AMdOFQr/9XzFjdhf32GOPYeHChZg2bRqamprwwAMPKPvs2LED733ve9HR0YF3vetduOeeewL7x8bGsHr1asycOROJRALvfOc7cf3116NYLBrNzYkRU2T/e6Flg/yQeb+mkFYRCYX+LmlKo24dEZM2kazeG4WrJsZAzRIiYSJDdDxTIaJ7jDBWEQsXDRu8GgVhAl4rXDS1zpKqKHQWRfn3MEXQwrpnaihEUmhoIVILhoaGMGvWLNx5551a7V9++WWcccYZOOWUU7Br1y6sWLECn/rUp/DTn/601Gb9+vXYuHEj7rjjDvz2t7/F+vXrccstt+D22283mptL7bWB/IC45wDy41SVg2d+4OTkFMV/hJxwBRdJcnJXpfdmkUQSWe7rLBJI+ifSLJLoxJiw71BvM7oyhXLKLCnTLnrkQe/TeQ54F/R+yTj0NkiOHZYuAG2K/TpjiF7rWEgiEGfE0hVXSi/BSnSwlhYbFw1rFdF10aQV7bTIMI/0gDxYq4ioLXsVj9o9Q1FtIeIw5vTTT8fpp5+u3X7Tpk2YOXMmvvzlLwMAjjjiCDz++OP4yle+ggULFgAAnnjiCZx55pk444wzAACHHnoovvOd7+Cpp54ympuzjIShB5KTNx0vAmj7ccKk92osmCe6gJjGjYjaVRwzbIyBqYsmjIWhi/oLi+5YOqm8MiFiOrahVUSHsCm9JjFGuYpjGHy/WBER5XozOseNZIVeFtXkbd6cSKRI4kRMhQiPMEKkW9DfEYonn3wSp556amDbggUL8OSTT5Zez507F9u3b8dLL70EAPjVr36Fxx9/3Ej0AM4yoo/sxyS1lPDuJMg2i8XxZEh+jLIF82grB+91DkkkKAsJ+5r0EfUXzlVmHeFZNWzRtZCw+1l0LCe2sTAElRCRjRFDDA1x0bBWkbgRCRgd2KqrVlk0VbGKkN+IrlUkrGoK454xyJyxKWrG/otM4kMaNeMtQjKZoGDt6OhAR0dH6HH37NmDKVOmBLZNmTIFmUwGuVwOiUQCV111FTKZDA4//HC0tLRgbGwMN954I84//3yjYznLiCmyDBqhkUCUUZMD12c8CqWVQ5ewcSOy4mcmWTWluIOwC8fZWEeAcDEYdHuRxcPEoiJqqyNEwgSwRmgV0bVu6FpFSOBqziDmKFTxtKgCV02PowXdmGcVectgLN5JycY9QyOJE4lLiKQwPoXIYAR/AKZPn46+vr7S37p166r2Fr73ve/h3nvvxZYtW/Dss8/iW9/6Fr70pS/hW9/6ltE4zjJii8ga0gpgNAGzIDQiSCL0wVvEjbAxImzZeJHFYwA96MIIAC+rhjwn5DqSSIzwrTIBorSO8CwfIgsJOG11j2FKEnwzvU4Kr417JkKriA6siyYKWLFhEhxbEbha6wDWisBV3fOEjaWE/px0CpzpWEUMFsCLUojojD2B2b17N3p7y/+nKKwiADB16lTs3bs3sG3v3r3o7e1FIuF9Fy6//HJcddVVOOeccwAAxxxzDP785z9j3bp1WLJkifaxnGUkLMLzYi/4P2jZnYjAUmKCZdxIVCv4ivoDHOuISZqvjUVE10KiOwdbugDpx6UjGMIGsEZgFYmyjLuMMLEkVgvjxRW4alXkTGUVsSFK9wyFaan3OIXIBI8Z6e3tDfxFJUbmzJmD7du3B7Zt27YNc+bMKb3OZrNobg5KiZaWFhQKBaNjOTGiC/miiyLApcXPaDLUnwVpiOuNSO7uswMJYYaBTYpvadwoC6DxoC/U1RAk1Q5gDStEwo4t2BYmndc0cNXE0hFLbZFqEalVhIZ3TlF9pjZrz4QIWI1biDi0GBwcxK5du7Br1y4AXururl278MorrwAAVq1ahcWLF5faf+Yzn8Ef//hHXHHFFfjd736Hr33ta/je976Hyy67rNRm4cKFuPHGG/Hwww/jT3/6E+6//37ceuut+OhHP2o0N+emsaELfOsD+VG8ze4Q3V1kBPtznquHdw4yLd8wCBTQheY+vlKpdMUEXTW8bbw2dLEqXoArcdVEkuZriq7Lhu1DozsXEyETVRwL77lIxMm2cU7q1UrnZal5bZGorCJa6FpF3gKwH6ctgXeeEVlFRGi6Z2icELEn7DnOsP/OnTtxyimnlF6vXLkSALBkyRLcc889eP3110vCBABmzpyJhx9+GJdddhluu+02HHzwwfjXf/3XUlovANx+++1YvXo1PvvZz+KNN97AtGnT8OlPfxrXXnut0dycGLGF/AjSnH2tYOICMpC7Z0SiJASKmBGALypYZHEjoqyabj9mRCurRgdaONDCQuc5JNtMYlJU2TVhRYhoDF33jA4RWkVMXSkqqwgJXPX261VuDFVbRBS4GnVMiVaRMxbdWiN0W5WlI4x7JkTAqhMidcW8efOklVHZ6qqkz3PPPSfs09PTgw0bNmDDhg2h5ubcNLqkJNu5NxwJBH/QOq6ZEO4bguY6NWHjRmyzagKIYkd4sSSqO30WXWtAH/SCR0XHMHXriPSmqRCRtY3JKiJD5aIJQyy1RUyIJeiVTedViQ66yJlsQqqgVd0qqwJMAlajECImSy9M4JiR8YCzjJjSBf7dtMh1g14EBUaG2aeByckwxd9c6O8q2TDYeiPkJE+sHORkn1C4ZgC+dYTeHrCksK4aHVTuGplFhJcpI8qeIRfwqGqbsOO2CPbrigVdC4lOjI1km8wqEkXMB69fFonAicjEJSQKXNWqLaIbuApmf1rQThSTUlWrCMHUPUMjsIrI4kTCCpEUZxq61hAnQsYFzjJig6jyKlHs2r99UTBrBl7cCMIF2TG56Dwq64hUWkcGmHgQ0obuS9eHINt5lhRyt63MrOFdSGkrhkk2Cc96IbJo9Cn+VJi01xUdUQoR3ufLWYNGFCsiX/1ZL3BVB1E6r45QEabz2gSuxpJBI7KKiIQIb0G8KrtnbIUI7/vrhIiDg7OMhEEZN0KfMHVcNIQQsSP0XASiKJtNllJNTSqtitam8drlAXiBrL34q7AtYBHMCuY5ifWgrRxsW0AvbkTUVoStS4c+3phgu+k2WyFCY+meiSqDht2f07CGiGJFtNJ5VbEiIquILVYZNCIhMohyACt7jojCPRODEGFRCRGT2JDxIESGoBsixUejfFOj4MSILrKbuhT1XGiFoN015GQjMqFQJyxRVo0I0Q/Xz6rJAkj2BM3FbAE0crKnxQS9nxYpqiBYk7ZcVIKE3s4KCxM3DX1iizKLhxCnm8Y2/Zn6rsgKnEXlntFFZRWRobSK6BBVXRHpGjQyqwiBFiIDAJoEY4mEiImlJAbXjKgfIaXR32QxPWfnb2jcv8+UFOSuGG5wlcjSMcD8RQypQSI4EfMCWU3Kw9N96Xa8iwfr2tF218ie68ZHmLhp6H2iPxG67cLOswpCJA73jMoqIkM4po5VRFX6Pfa6I6I1aHSrqupOTFXczCJzhsY2RsRGiJgEqcajiR1VxokRXWTFznj7euDfVdA/bt5JgSYCUaIogEbWqmHTILNIBu50s0hU3ImysSN03/L2TmHbqguSsKJEhKlIoUlK5sE7Du+4hLAF4cAPWK2le8ak9LswaNXEGhJXtVWroFWee0Z2PuiB3dozFpkzUQmRFPSECIvoN+aEyLjBuWlMIWUQ2BNdyn8UmviJm4bNruHBnIBMXDW8Hyc9V84PXWeVXqAcXyKLB2G36bpmlPEjkDwX1SHhvSbbwGlXMSnltOXQY4pkv62bJowQ0QhY9Z5X1z1TmosiaFUrEFYkEuJaEE+JTtAqgRewSsO7WpvEiYjaCNwzUQoRFl0hwsJ7O40YQzIAfgyZLuMoZsRZRmwRuWq41hKe6ZPuHEF9EZY0lDVHstlkhYWDvdDoWkdynAuFiXVEShgLCXmte9Hn9dO1gphYSsJYbmISIjQ69UKisopEsQaNUbXVNIJtY7eKsEKEB+nMxonoEGOcSFxCRFQ/hB1PV4i4AmgNjxMjusgiukX7Sq4aArtYXg/zOqQoodeq4eHHjxBXDQ0vdoSNCaFfq4qe8dpau2t0n7MX6bjcNHQ/WzcNbzzVvNh0YUshQiOLE+FhKyh03TrD/qPVGjSqVF4TS1es7hnWKqITJ9IDtUXExD1TYyHCoqpLQsblVXx1QmRc4MSIKWR1yBRnewoSHyb5weus3puBdzIi4oSqOcIukpemtvFQCBTWOgJUmuF1rCOqYNaK41ZDkJB9JqLERmDwYMcRXat140d478vkuWHAKqC2bPCIowKrKmg10gJnkVdbNam0yosTEcWFAGar8YYQIixRChGemHDWkAmJEyNhULlqCKUfucxdE+G6NPRKvgJE1pFgMKqedYTnriHjydoqiUqQkP02VhFdV42piDFxycQgRGh4gkFHTETlnmHFTaSpvNUscGZUU4QVJ2yciEyETAI/RgRQx4lopvDSdAueA+GFCAv7/de1hqQ4x3M0FE6M6EJ+JCZZNcIUYJG7xgLWOiIzRUsqsoaxjvCQpfpqu2tobASJ7qq4UbhqdNqKjmUiQiISIjoBqzRxrj0TPE53YNyGKnAmRCdolUYmQnTbW6w7w+taLSHC+w3oWkPYYzUSWXjfP9u/cRTA6rJpbKB/JMZZNToIbud4WTWqC2Gaes75cZOTOlmvhi14xmbSkDZJ5EoXqCSyJV+/t78y24as/kv2sWvXlLaLqrOS92q6Yi9vzRnymckKn9GY/i9NhI2pS4a3LSYhYlIXJKxVxPv+7OO8UeoYuqm8MldL1YNWZYisIizECsKW6mR/0Jb1RHTiRGSVVWVCxCY+ZCKIEEcFTozoIrrAkB+J6sL2tmzwiKot8U7C7I+YalNAF5r7vImTEvHsYnlsVVYAFQvgZZFEN0ZK7RK+6GDbhhIkZO60kFA9J6+B4AWeTgNm2/EIEzvCIjIkmFhxVM+BqgiRqNwzOSTRTl1odVJ5td0zJqm8VRUiNPujUpCI3DCAd7Vmq7E2gBDRdcuw6AqRaI10jirj3DSmsPEg7HZRUBVX9kWYzpsWbKeLoHE0Dxs7wsaFDKBHWpWVF9TID4LkZ0nouGwqgloB/YuyqQskiiBW3hgyEWITdCuzhlDpu3SwKm/xOxMhQhOlEPHadzPtqlBpNbagVRrZb9zUPatTK4S3rQpCJAUzIcJ+n8PEhtj+Th11hbOM2KLjqhHtj1KEkBMqmQ97LPYHzgoS/8eu664h22j3zQB60OdPZBgJdGAg0J+1gJhaSADYWUnoz0fXWsIjyhNdL/hLjKisILxthtYQQC9rBuALkagDVuniZu0oUHPUzJ6RuWVMhUhVa4qoYK0i5EfMumlEi+XVQIjQyOJMVGOJ+vCOwxur0egHfKOyHcNRTaT2OMuIJs29fqQQ78tPAlVlrpxAefiIMmfokzAJZGWhM2t4rojBYGqkaSE0cuEYVJSKD2Mh4Qa26lhJyGtZ5guhT/JnCm8M0b9cN6BVZhkRWEOA6gsRFlXmDEvWX07AWIjIsmfqToiY/P5Fwak1EiKsBSPFTEMV8GojRFKc4zhryLjDWUZsoH8w7AWe/TEZB7Jqxo8MojK4E6gUJOwPVjDfQn9wRV9e4Cq9jY0J6aFiRjoxWNE2jIUEQCCOBICelYR+v6L4EFEwK42NIBHRBfHiqzrWEUGAKqC2hnjPxW4ZILwQ0alFQvcl7b3XbwXaWAuRqsaJyIh65b0e8IVOFYUITUrQh/faJjYkxWnDG4uMV4Tm/8VRjzgxogm5SFegutiz+wfgZcUACJ5YNM26pPhZD8qChD4u+yNn55NiXpOTrMJd4w1dDmYlIoUICLJAnre/MpsmjCABUHLbAAgGt5K58zJuyGv2c1CJFZYosml46xmJ2upsk9QOsREi7CKJpbFCCBEd9wzZ30G5aSJdeyb2OBGTFXlNYYNRC5L9NA0mRHQDVEUixDEucGLEkObeLNDi3yrrXFyk5ySdRfNociidXGhBApR/0IKYkBJpwf5BeXaNN7T3yydiASjHhPT4J+VB9CCJ/grLSRhBAgQFjraVBIjGMhJ1Nk2YRfMsrCHe6+iFCE0YIeI9emJ2WNc9Ezy4R93EiYh+9FHFinUDaGG28So7xyBEUpypiF7riAcda4gTIRMCJ0Y0SSQ4lhFJ2mwJ3o9mABbmRLLib86zrLSiLEjoY7M/XHZOojkr3DVAMJiVTgGmA1oBlMQF3TaMIPGOXWklqQhuJe+Dfs/1YhkpIJxlRFOEAHxrCCC3ZtBtddJ36X3hhUgCHdTSpY1ZT8S0sBkLL71XlwYQIjbWENFvUuTiKaIGqzGHZBDhXEvjKIDViRFDkj055FpbUUCX/oWeC8+nTp/A2LsrYkXxTzijKP/3aHcN25X3o09Tz1kxI3DXAOWLRw8GKmJCektum060IxsoiBZWkHjHrrSSsG4bgLGS0O+rXiwjXTCzjEhECKBnDQHqW4gAZYtILucJ7fqOE5EtgmcCz4LCqy8iCngVba+yEAnrlmHH5o3BG4c3lqNhcWJEk05w6u7qukBYlO7kv6LypEQJEeKuYeNHCCbWkYHKbYX+srvGO1owmJW2fBCRwK5JUyEcQggSgHUNaVpJ2PcqEyZRWkZ447HuflE7QPi9CWMNYdvIAlXptnEKERGhhUjV6omYWEWiDmZlhQh53eBCxImQCYsTI4YkEjkkW1uRBVAYZH45Keq57ALWAyaQlUa0nDgdSe+7a9j4EdGxZRabLs52jdojbPxIHxXQ2oahUluteiIKQULGNbWSAOALE5UbhyXqFEJZCjgDb50eVYBqqZ2FEAlbmVU3hZft20m5aaSEFSJVc89ELT5kVEmImASqRuWW0REi5HUBjeemcZRwYkSTJM85J0vjlV3ApHdo7K+PtohwthF3jcw1o1sITTOYlScS2Asbaatd4EwiSIBgoTWelYQNbiVwhYnKWsIjbNwIudayRgENKwjB1CXDttUNVKX3m2TN8FC5Z8j+XM6vLzPguWm0Cpux1I0QsYG1hvLSdbuZffT+GgqRWlpDGt1CMgggH6J/mIJpdYYTI4Z0Iotk0otkH+xnfkX0S9mNEZ35IrSO0Ccj9sTUi9LJh/0P8k7aKeo576LahUox4wsSEsxKBAkQdJOQ+JG2kjhJoA2VFVptBQk5hspKwksBJtAunNJblgkTHlFZR7rA/dVxVypGpTsG0HPJsO1M40Po/Sbpu7x9KiGSzSaRoKL4IokTiVyImED/mHqgZyVRCZEeoMJ6ZCFEbKqq2rplbGJDwogQVwStoXFiRBM6xZXQ3DfkBbIS2HOQCuE5SlbESCJEVO4aQHzx7WK2aRRDowUJWXU1iwTawC8ZrytIaGysJOW+ZWsJQVuYqOAJF1l/zkVNZf2gsbGEAHbWEN5+k2Jo9D4dIQJQcSKZpJe1GiZOJDaLiC2ikwHvBCASIuw4EQqRKOJD4rCG6IoQsk3T0+eoT5wYMSSJYYygBUiictEu3jnHOnaEbqQhRMiJ08Zdk2a20e26y4IE8OJHeIJkf8pEzdYgIW3ZsvIidKwk7PisKPHmYS5MWAIxJzSawoWMPTrqPWZ7m9HaKs6IIYiCPKMSIXR71X6blXuD/dRChLaMhA5Y5fVNM22shUgUa88QdIqW0eJklNkfoxCJyi2TQiU2ab/j0UXjCFDXYmTdunX493//d/zud79DIpHA3LlzsX79ehx22GFVn0sP51Y42ZNDFihbR3TrSLC8LT6qUogETprUycnEXdPNbNNI92UFibfQ2VsYRgItyHEFCWlv47ah+3pTVIsS0gfQFyYsMqFii671w9tWKVRtRAjdz6YOSRi3DLc/zyICeN/BZoTLnFEJEStE4sMkcFWnyKFIiLAohEhU8SFRumWidMlwtpXWD3M0JHW9UN6jjz6KZcuW4Re/+AW2bduGfD6P0047DUNDNrmW0ZBAFknkuG4bdKO8YF6Keq2i1RvZO+mQGBFWiNBtfSru3nLldF+aIX9bGt4JmhYq9EJ6dFv6hD5Y9uNnBxKlC0kOSc6FqJu6YCVKFyR6m9cueMFi99Pbg+OV+9Nj8NrTfei+pH2gn78gn+pPhbBvu3+c9kohQc+DnXNpXP990WKA7hf8PPh96c+VbsPbz45H2snGIP1UbYRFzQhhU3jpfWmmDd3OOGAVgtcm9Cr+CDJXbXnphdL5gJwygHiECLswHX08ti07pu4CeTxrjMa25r6hQCmChiIL7/to+2epv+68804ceuih6OzsxEknnYSnnnpK2HbevHloamqq+DvjjDMAAPl8HldeeSWOOeYYdHV1Ydq0aVi8eDFee+01oznVtWVk69atgdf33HMPJk+ejGeeeQbvf//7qzqXTuTAlmAup7xGYB0puWvo9F36MaEQIkyFVpG7Ji2YG5l3ipoP3ZeTYUNbSDqoK4GsbLwsY0aEzB2jspQQ6IDX8riVVhP6mDx0BAlLFkmMlVajTaCFs1KejgWEjEWjYwlh+5laQ3jtbOJDAIkQIV+fIXgLCYZJ4ZUFIocSIjYihA3k0kEkRJjAo1amORCfEOFNj23Hjqfqy+tvsK1hBUiN+e53v4uVK1di06ZNOOmkk7BhwwYsWLAAL774IiZPnlzR/t///d+xb9++0uu//vWvmDVrFs466ywAQDabxbPPPovVq1dj1qxZePvtt3HppZfiIx/5CHbu3Kk9r7oWIyz9/f0AgEmTJgnbjIyMYGSknO+UyXgnkHw+j3zePoeqze86Ke+dtTpQQBEdKI6Oojg6isJY3stz74JXlhjwTqpN8OxPLf7fCIB2/2+U84ck9YKcgDqBVn8C5HzUBpRrAZOT5Ft+n7z3n2XHHoF3MmiDt3+Ymlc3PFVF5pyEdx4t+n9d/mHG2lEcHUWyJ4diph2JRA5FdGDM/2wL+U60Yxgj6EI7iuhEDnkkStlrrf6FeJ//mPWNc90YwDC6kUQOI+hBJ3LIoQdeQTXvpJxADjl/CV1PlLT5z4eR8bMRujGAQbSX/m9EcAyho7SNFLAboLaxqdsD9N1nBIzlOwOPWcH4w1wBUilUBqmzeo4Zi774k2J0TdQ4TYAfcOx9NbNI+J4Rb8wWf8xWaqxWkEwpr30bNUY7ihhAN9pRQA7eondZdKIDYxiGV0OEpO4mMFqKEfFcM3lgCEgUvG9IopivFArke5n2H2lXTov/mjy2wv9tAOiH9zsb8B+zADr8dp1+f/KYgPcb4f6ueK4XVvEUmce3AExittHxR7yiN2T7GLwfKh0jMopEwrsZSiTy5TO3joCgv1Lka8OuRN2l8VzlskkJjkn3LTLb6NdkvAJnGxWcWnLHUNos2ZND56i3Icx5Por+9c6tt96Kiy++GBdddBEAYNOmTXj44YexefNmXHXVVRXt2evtfffdh2QyWRIjfX192LZtW6DNHXfcgRNPPBGvvPIKZsyYoTWvpmKxWFQ3qz2FQgEf+chHkE6n8fjjjwvbrV27Ftddd13F9i1btiCZNL+rdTgcDsfEIZvN4rzzzkN/fz96e0Ul98ORyWTQ19cHfLIfaA9xjH0Z4Jt92nPdt28fkskkfvCDH2DRokWl7UuWLEE6ncaDDz6oHOOYY47BnDlzcNdddwnb/Od//idOO+00pNNp7c+wYSwjy5Ytw/PPPy8VIgCwatUqrFy5svQ6k8lg+vTpOO2000J9sZbmv4ePbevG/50/hoG2RCk24i1MQi6XQHYg4d3pEV8eMRdn4d3RkW1k/2vwbriyTPtRoHxnxrt7Ft21DaIcpEL5lltR9rN2oXzH0gPv7qgLQZ9uN/U85bfvZvb5+5t7s6WU3/1aB3HLtt9g9fy/QUubZ9IjrpEeDPpurrKlIlF6zPqPw/4hynehpG0nchXb6DHoceixCN3cO9vgWDTc0v8hKeQ78Mdt12Pa/HVobqusVCSqWDrIMfHTlhDWlZNjxqHHJZYMepvIwlIqRsbpQ8YZ8L8YQXdQcPXdQDEzMIGqhCHPIrK5fxuWDs9HDm1BX3gawT6DzGv2sd9/JP/2LLOftbyU7rBlvy0WUaVkwLOKAGXLCP0/5AV2gLMfYGNEEok8Nm/ehqUXz0euta3cjGcR4VlDgKBFRGQBYV/LXDYp5jV9XB13i+y4PqLAVHLuIXSOjuL2/3oG8+fPR1tbG7ePDsSa3kiwc+7o6EBHR0dFuzfffBNjY2OYMmVKYPuUKVPwu9/9Tnmcp556Cs8//zy++c1vCtsMDw/jyiuvxLnnnmt0zW0IMbJ8+XI89NBDeOyxx3DwwQdL24r+CW1tbaG+oG3IAehGvg3Y19aEfWjGCJrRhBE0tbWgqbUVI637UGjp8kzIxO9N3BwFeKbGMXgnvySAff5fK/WXAzAqmmfOb5ShHlvgnQQ9g7v3uhXeiaytPOaw/zcC7we/D17lv7Q/nzF/e78/V2JCpTNsmvxDNPv7h/qQa/W/Qv61qr+tG01tI767pRtJZPFX9KIHTUggi34/XiTvdxj17bTkccy30fZgAIPoQBJZZH23SwJZDPk2eC/rhjzPehcwv02Oiu1JIhdw0ZCxAWA40K58wttXsTy7mAQnPoVHwR+zuW2kJEboOI1meAKOTX9u8rcHxUqeOmaeGWuUaTtKtR3z2xWoYxWo9kWqbVEa+yGKD6HbZbPJ8qJ3rX58CKkhQmfMDKFkrs+hDbkh6vuf9h9lMSL0Y1qzHTdGRPbbIpCTPhv3M8DZRl4PoqwIyFj0d4x3whan7+Za25Ab9j+jbni/a3KRp3/f8A9LnqdQrvbZjfJ7J54hoNIt4n1Fyu2IHT3lP5LXprEhGkKlHBMSvEyVax6VtyeTWTTlvUmHPteH6GvMIEB5lc3x/7fTp08PbF6zZg3Wrl0bYmA+3/zmN3HMMcfgxBNP5O7P5/P4+Mc/jmKxiI0bNxqNXddipFgs4nOf+xzuv/9+7NixAzNnzqz1lPwLaSXcIEzduDVlHBZ9ByArRd2Liju2USrwVZbuS/ax1VhTzPy6qbbd5UX16JorbNovvZYNTZTl4HltAH7AKj2X8oJ/6iBaHjIhQo9Z8M84XnxGUOyI6q+YpvWark1D91EFu7JBqnQ7o0BVUQ0REr9BPuY09UaqIkQA8zLvqlxhtsQ721dVZ0SQvkuIooaITl+2XYrZZxLgytumGZzKWkKA4KriE5ndu3cHrBC8G3IAOOCAA9DS0oK9e/cGtu/duxdTp06VHmNoaAj33XcfvvCFL3D3EyHy5z//GT//+c+NPRF1LUaWLVuGLVu24MEHH0RPTw/27NkDwAuYSSRkhcKip+xO8MRIRaVQXmYNoFfbgP0xD0DjREkPTu+jvwD+dpJdI6vOSosMnQwbRpCgy7ON53IJNLXtEwoSUj7ephy8N56ZKCHtALUwIcfioRIrugyip2TtqDwG/zstEyC8fiYiRNReJ1uGbhdKiLAfeZp6XndCRPSD/qtguwn0b5cjRHgiIm4hokrZpTG1hkQsQuJwrzYKvb29Whf/9vZ2HH/88di+fXspZqRQKGD79u1Yvny5tO/3v/99jIyM4BOf+ETFPiJEfv/73+ORRx7B/vuLRLiYuhYjxMwzb968wPa7774bF154YfUnRMGtM+KfjwNr1phm9RECgkTUAAiePOmD8RbY8yHhJfQwQFBkAJXVWGmrCStIMuULUFNrayDtl8AKEh3CiBK2HYEtjEb6AWIrBQ9auKj6FQW2WN2UXq+tWIREVY2VbmPllgECVjJtIZKFZxlJC/YDNRYiNiKEnJBlJd45tYTIZ8srZgZUumYAvphIMX14z3WtGimmnUkpeCOXjAdPgAB8EcI9FzuErFy5EkuWLMHs2bNx4oknYsOGDRgaGipl1yxevBgHHXQQ1q1bF+j3zW9+E4sWLaoQGvl8Hh/72Mfw7LPP4qGHHsLY2FjJcDBp0iS0t+v5oepajNRTog8JikwgW4pvILAXiYo1a3iETpEnteRJVUf2jCBRyeREMAThInmBw+gIEiodj63Uyrv4e0PrWUkAO1ECoEKYAJUXbtH8eNBBrzIBUjleWylllq1Xoys+eOOKBAU7v6hEiKidtjWEfk6LhybFftFYac22VRUiskXveAGqmkKEJyjq2RpiIUKASiEicsWMCxEyBAgMpXpYZCGfffbZ+Mtf/oJrr70We/bswXHHHYetW7eWglpfeeUVNDcH66G++OKLePzxx/Gzn/2sYrxXX30VP/rRjwAAxx13XGDfI488UmFMEFHXYqSeEGVkAEw8gX9+zgIoDEoEiVEhtBzKokOnpDRBw2cXVpAQ/PddyCSBSfuC6/YkUSE4TAhbEp70Y48rEifssWl0RQtLDkl0l45VeQbRWYuGHkvUlxVIYUUI3TZyawgQLGY2iXpt65aRHa+uhYhBefculANV2XaAWojUwhoSkQgRCZAkcugIFChxyFi+fLnQLbNjx46KbYcddpjQOHDooYdGYjhwYsSQBIYrLCM0gQuFKHvPhIAgsSFR+V/mBbLaChKynXwkQ/AyiuAJsmRPzrtgMdda3ViSKNAVJ9724MlO151kShjxwesfphKrSoSI2iqtIfRzkbUj7T/SlpHY40MAtRCxWciGdcsAlUKEV96dI0R4AkGUissTIlEGqZoEqFZRhIhS8x2NiRMjmpAvfjcGSumnvAtKAtmgdYT9JVu7Z8gJi7aOsK4aiSWEnEB4BR9FmTQqQcLbTqpjdgdX+wXAjSNRIcuWUVlL2D4EWcxIVAKIpQmtvmUkgaKfhqwbsEqQCRB2PJ2sGxMRQre3soZ4B/HglXZvZtpXLT6EbqwrRHhWkZBCRBYfQj8C5bIAcbhl6HFkfWTH4b2GOi4kjAhJIusv1uloVJwYCYEo3dPfGXRVEHTcMyzKYFYaQRyD7PxqK0hSCIorUjNLkfrLxpKIrCQ0pqLEm2rluje64kSG6o6MN1aT/1PLIoEi88/UsXwQZAKEHUsU8BpWhAAcawigdsvQ35W0oO0QyjUuqh6oqmMN0REiEQSqqmIvbIVIHVhDdLJjTESIY3zgxIgmdAXRAkeB8y4oyZ4c3zpiQ8ldA/BjRnh3XZrjAuEtJHQ6MLOfLLBXIc4URgiTrBsWVpiQ8XTECUsUcSOkaukwEijQC22UxtQTH7zj66b+2ooQwNAaQj+XWUNAtaFjelUihB5vvAgR0cWedtG0U21J0GNK0of0Y6fDtqHHMenjREh4+hHuKqx9k1r/ODGiiepuWJbqqwxm1YF4ZEbpCxHtqmGhTnyqL2wYQdKN8gJmZNGxpsp+RJARUUJiSURWEgQONSC0hJgEsOqIk3L78v8zirgROjZd5grSLYBmW3skFhFCPzexhvDEBSsy0kxbdj9vvJoLEcNAVR0hwpJS9GH76bhlbK0hNRYhJMW+XRLL56h/nBgxpBM57p0tS8Xdc9hgVl6shlZmjSrIK+GdvG0ECb09DS8bgpwreNaT7vKFrOKUoghw9YY0EyXld6gvTghRx440+3LEmwddjr2SOIqf2YoQQNMlQz8XWUNkbYY5bdKCtrLsnKoIkQgzZlTxITxB0YdgWXe2v6gf3SaFIBHFhpjGhUQhQhzjAydGNCGLsiWRQxGjyotV6QclC2a1geuu4VlHVCKFnCBzsBYk9D761l9W3dV/TceSlDJuAKXrxgRe6q5JNVYZohOhaIwW/wMaRE8pAJoQJpBVZTGJTYTQz1Upu6o2gyifiYYQFC81z5gRCZEIM2ZM4zzYttWwhtS5CEmArFTVUrHP0Rg4MWKJic8y9F02t1w8664BzMq9smIlpCAhq4Gm/eds/RKOVYWIM/JJilw3QKX1w8RaQggTvMqeKE2ESxYJtPq+qxw6uanhumvckPFkfSuDXO1ECKDpkvEO6mFiDaH3iVbcjaR+CN24WhkzMRQy66P2k/cclRDRtYaEFCFApRAJK0IamkGE009qI33D4MSIJvTaNGw2BCC/mAjXrdGFd95sBSNIaGyXwA7hsiE/KGoFVmk/6pGXcQOgZCUJI0rKQ+mJExr6RGdb7IyHifCQHV/khuGNYy1CAD2XDKAOUqXbsGO2GbSl20RayIxQ5YwZlaBICaapEi81tIbUQoR440VR3MlRC5wY0SSBYWnVXqVCj8NdY7ZDQA+CJ2oDQQKqDX3hKKAcR6KJKMAVQGjXjW3RsygLnpUtI0kQtSazmOm6bupKhABit4zMwpFGOVPE2C0DmMWH9EAc9E2gY0L+ilgDVVVumRRnfxfKd8RRWUMiDFBVuWR0RIiJFcQVPxsfODFiiPeDaFO24+Kf/wML6YkwKhdPkGXXyAZRCBJ2XmR7CuWLBP1NYvulEHTbiIqnQey6AYJF03SsJQA/PTjKomdJZLX6kG9MDolAMXiZxUUkhkRxILzxKoJeqyVC6LYma8tk4ZU6D5UtQzcWLSQpcrOxX979oeeWoZ9HvL6M6GafJ16idMkwr6MWIeGtII7xhBMjmnRjAG+BLJhXubaISQyB1kJ6vAu6qF0sgsQflyceeBYSMj+6aJVEdAT2a8STVBBPoVRteOJFJizaqDbst0dmgeHXGYlRgIiehxEh9H5Rpky7pG1oawiPXqYdQfQ7jrB+iIk1hNeWtoxEZQ1pYBFCxvOsj85N06g4MRIR2mlmtu4aTppsicgECU0vgJxYkLCQq+0Q7AvxsPEkqHTfAHrWEkAcJ8JL6dV1yfCycXiw442hCGAfhpHAvkAhFg/5CsC8Cq36AgQIIUK8yXlEKULY9qPUc6PYEPq5TbaMqEBghtPG0i0T1hrCu77WwCVTDRFiYgUZF4XPhuACWH2cGNEkiRzegmchaUJH2MEAhIwfoU/4xgXRZHDaE0HCg1hJyJ3tEIAOVAoXlZjhwRElQO2sJTYVYYnI8NbNeAsD6Ma+QB50GdMYEmsBAsQjQui2vDFJX964bL9QKbvspGSQqzRPjPPaxZQtk+K0Y/u0UNsKnLb0GKbWkAYRIbyxEgHLiKNRcWJEk07/C59EDs3SUFaPUMGPpvqEjtvgpvyaQk4UVFG1UX/1X1GqLhEj/QiKAjq4Fais3GqBjjDhLcpnEsgabOMdxcQVJyKHJEYEYgQwiyGRiQ8gpAABwllC6Oekr0y00Na0mlhDdP63MQep6hQw4+1PUc9jdMmEFSFRuGJUfR2NixMjMREqu0YUOKpiAIKUX5tKhQJBwhuuG2VVMABoaLVg3zQqF91j2wzyn5MLrsiNQ9AVJ4E+lkGtPMZ8ywjBJnAViFCAsK/jFCG8Pmx7epFFI2sIbxudAaOLKIaE3kc/r5I1hO7TTG0n6fP0GCGsIbUWISbrz4gETItE5DvqHydGNEn69ao7kUVzVFX+TNw1bK0OWTupICEnW53F9HgiRpBpQy4gWZSXN09rHIIlBbEokaDlxmGRaAzTlXxVjPnO3Sw6MeJ/f0xER2k7ZyVoqQBhX5sIEFH7MCKEbke7ZMj3J1QBM159EFNEv4uYglR1rCE8o02Kel4ll0wtRci4zagZAkJpqMo1WxsWJ0Y0KZeDH0aLpW+SXHwCPzab+BFyEk8J9pfOATxBQiwdvAA90WCCTBvabUOb2VkxkYIwa0ZIijOOJuznyCuoxrOeAOWTb5Q1RgCg6IuRYf8bRBCJDoAvPAAN8cFuY/fHJULoMWQuGWmA6rD/QuaS4W2zESK8qzz9W2CFewRBqilOO1Ufsp+cdlIoW0YisoaYxIWYipCoXTG8vj0YKK3/5GhMnBipIuSHJY09MMlMIyd13nClu02ZICGohIkk04bsJmb2IQQXPeNBZ82oBEeKakOnFNMBsSI3jg9P5PGsJyKBEgXFUe9qm8slkMsHf3Yi0QFwhAdBZv3gvRYJECAeSwjdT6dmSKB0T1hrCM9FYxPvE0GQqiplV8caQtrQ/WvgkjENTLUVISZWELdY3vjBiRFNEvvK5eBtLSNCaOsI7+KjMpikBe2MBAlBJkwkbhv6rpeIER2xYdLOBp41prvyc+aWo5fAWxZd1p+IkexAArnWyp+dUHQAasuHaBv77zKxgrDtoxYhAOOSIS8y8E5LOpkyUbhlRHD+l3G5ZUyrqBK9bJGuG4VLxiY7RkfImFhBKttF6D531AQnRjRJZjznXGJfFq1tMaSQ6QS0phRjDCAiQULgCRPBncio/1UahPetMk3jBcqihFdUjc6+SUFf5GjOwyTFWle40AKjMOaVOitkkii0CCr4yuZqIz4AsQBh+9dMhBBo8dHCdIgjNoRGET8VdZCqyhqiEiF0W9FcmOfViguphQhxjA+cGNElgjv30OvXkDmoAlhDCxJVcCsvjsSf/CjAtajKhIYInbYp8N04IgSWEi3R4n/uUiuGiCHqUebaNhUkMusHYCdA6Ha8eBBZX624EKAyQJUeUMcaQr6DLKyLhm3DK2DG7qOoRpCqyhrCft14x7cIUI0zLsRGhIQRIAlk0dSIl7MhIJShvXIB8IalAf97tSWZKWBf6whs6p6JsjOMA1rTiEGQiBAFpIjSaYaB0bZyM1GxMxNRYkPU45sE36pQjSPbrxIfgL4AYfurglLZ/jJLCN1WKkII7LoxIqG8PzxB0kO9ZlEJjZjXlUlx2tlaQ+i23fA+SzqYVbNmSBQixCYw1TYexESEOMYHTozoQr7zQ0BXawHozVoJEh68gNZkT04tSAD5el88QQJw0n514NVhIM/pSq/E5w955VZC3KJEdSybyrA2kDsYcidkIjhoTMUHbzwdAUKPa+LCodsrRYgszZwVJPR3TEeAyAqbSUQIYG8NSXHa08/DBqiyd8GGLpm4REi1rSC88URjOhoHJ0Z00XDTROa/TJZTPpWL6g3CXJBw65DYQi4oJOGdHICTcdOoRCFYyIVkEOW7WlPRQUgzr03dNzoChB1D1xUDaBYtA7OPjsokAaw8QcIejLdeDAT76TY1qhti4pJh2xbh/X+6UHb1xShCvCmIs2PisoLYCxB3OWtk3H/PlCyAFqALldYRUW0KK8VO3DUDiXCCRMQALAQJzxJCttNXW05Uu0qUyDJqUqiO9SJuOhF8HyaCg8YmdiQqAcI+txIhPDHBSx/nCRK6D92WHU+0n35dpy4ZkWDpQvl/FTI4tdYiJKwbhndOTTjLSEPjxIgulJuG3JXwBAm/a1mkGFlPTAWJN6kgaVSen4l40RYkoh85fYEYoB4lEZphLSUpSGuK1C1N8MRIFmXdlpa0N3XjyMQH7zV9bFMrCleAAGYihN4mUtI8QcKOKQpI5VlJLIuXAdGn66aobaq25JEYH/32uvVCaiVCbF0xJgIk2FeQpVbPDMAFsPo4MaIL+d1nEbjW6goSQi4gTDSUvIkgEZGG3JUjY1Sxnzugxq8ravdNXMKETikOQxOASf5Y9AkkytgRWwGia0WJVISA2Z+knu9H7eO5aFQr6kLwGojUJWPSx9YaIrCy6Lhk6kWEhLGChF2zxtE4ODESAV0Zu4BWNnBVKE4oQQJAnkmj67IhFVDlE/RJoHzBEdUmoVN9RRcfTl9WlFQroLTaEAE7hOB6EqrrtMiVo1N3JM281hUg7GtpQCogrubLS9Hl0cscMIPKIFORi1AWlErgWELormFcMqo+OlkyKqsMee6tKIDm3iyAVi0RAsgLltWjCBGdB9V9G9Ay4ijhxIgu5OaMlxcuuaiblCtmV4cVpvz2d5kLkkGoi6bRbbXhiRO2KFUvlFfdKC0lKdSfoCFhNIMoXVQAqAOjRe+Dtz3N2WZjAWH7xSZC6L7sF5onSMh2g6BUQhgRAoQLUE1x2onaSjJkmv0qvsmeHJr8Kr42IiRMdoxNPEjUVhCRBSSBYaMFwx31hRMjumRRvvnneCFE7hpaYJiaEQfQE60gYRlCcJ0YUM/JxakV1MVIdodKQ2pBWDBKmdGjzMBJwbzYmSyo1hQiRoYQFCNQzEG2P83ZFiaQNRIBIttOENWuIZPqo8bhuV54MSOKdWTYw9ZTloxkTmGDU6OyhNjEg9hm19gGshbRzm1T17DnggmMEyM29PM3q+JH6KJnuhk2SkECqN0tBB3XTOTwRImBpQTqpg0B+aUNoTIORyVG0oLtOoGs7PjaAgSwFyE6MSI0rDihg1pFgoT3WlErBNDPeElZ9IkiLkQmRPz/TyKRQ1Obp25rLUJsA1LjWLW3gHa8VbHHwePOO+/EF7/4RezZswezZs3C7bffjhNPPJHb9t///d9x00034Q9/+APy+Tze/e5341/+5V9wwQUXBNr99re/xZVXXolHH30Uo6OjOPLII/HDH/4QM2bM0JqTEyO60AGsvAxef38cGTYktqT0I2SrtIpERlqwPTh4+WRJW0oAufEjQC+ECg1A6LVEaiFMUojW1UPc2UMA8pz9aUV/0ftWxX6w20SFc0tEbQXRYQCVX1SVICEYFi2jn0cVF0Lvp/vbxIVAbA1JJKjnEYuQsPEgtrEgYQQIbx4ONd/97nexcuVKbNq0CSeddBI2bNiABQsW4MUXX8TkyZMr2k+aNAmf//zncfjhh6O9vR0PPfQQLrroIkyePBkLFiwAAPzP//wP3ve+9+GTn/wkrrvuOvT29uI3v/kNOjs7K8YT4cSILnRqLy+dipPkopvGq5thk0XSXJCwEMHBovxN84JYdV01ojbs+jYaNKrFhFiQ+wGuYzvOQFZrAcK+1hEhpsKTVFOlU8P7qOciQaJwyzRCqi7zXJkh44vYTmQxghbtqqnVFCG2VpCwAiSJLAoYc5YRDW699VZcfPHFuOiiiwAAmzZtwsMPP4zNmzfjqquuqmg/b968wOtLL70U3/rWt/D444+XxMjnP/95fOhDH8Itt9xSavfOd77TaF5OjOiSpR4Vud3EOpLrMI8XIVYQUftIBIkppWwKIgTIhYEWJD3U6yZ/G69sNzuwKLCFvvgIBEtcwiSOEvVEjAyAL0YAu2BWnQJogCQGBJALEN6gMiGi+p+LoL8/9DZg3IgQ+rlh5dQEsujwAwySGEYLVV+gHkVIHG4YnfEmKplM8Dfb0dGBjo5K8/y+ffvwzDPPYNWqVaVtzc3NOPXUU/Hkk08qj1MsFvHzn/8cL774ItavXw8AKBQKePjhh3HFFVdgwYIFeO655zBz5kysWrUKixYt0n4PToyEQXLxYN01JF5E9weURcJOkMTFABRl5EkkVg+CRc9oUUJfrNgLjaisNxusSK9nQk5aifLFlhf4yksXlgWvxiHqyPyyAEYkx5VhJTxMEVmqaCuYKJCZFSImZYG7mTHJccB5HkHlVKBug1NlGTI0cYuQKFwx1RIgCWQx1pDRoDmES0n2Prfp06cHtq5ZswZr166taP3mm29ibGwMU6ZMCWyfMmUKfve73wmP0t/fj4MOOggjIyNoaWnB1772NcyfPx8A8MYbb2BwcBA333wzbrjhBqxfvx5bt27FP/7jP+KRRx7BBz7wAa134sSILnTMCFMJkdsO6pLxqiBWG0ECMKLEplS8DGHVVnIF7Aa3HHxJlOiKEB60EFGIEqD87eaVpah26i+Z1xCAYUEb1Zy0hQf7vZJV2dWpAULa8QqPsdtVXzbR/5j8LrrBFyER1QoBoitaFqMIASrjQjr8E08C2ZJlRCRCogxKjcIKEkaA8MYTjTlR2b17N3p7y78bnlUkDD09Pdi1axcGBwexfft2rFy5En/zN3+DefPmoVDwvpdnnnkmLrvsMgDAcccdhyeeeAKbNm1yYiRyyDmbl5pJQweDQh7QqnLJABaCxKZSqyiORESF2wYoXxV7UM6D5iETISKLCH1g3TiTROWF2iRdWOe6agKZyyD4YkRnXlrCA6iMr9Bds0P2uZqIEpWopCF9aMuaoF5II4oQ6rmNJYS+YCc0LCG1FCG1Xq13bDzVRjekt7c3IEZEHHDAAWhpacHevXsD2/fu3YupU6cK+zU3N+Nd73oXAE9o/Pa3v8W6deswb948HHDAAWhtbcWRRx4Z6HPEEUfg8ccf134PTozoQn4DGejlhpMT1qBeyq+tIAlAZflI3TZDEBdAo10UacgvyAFRQlJEelH+WvEKVAGVFy2Tixdr5lBZSwgccWJyqLDQYoSnDbTmJhIePNjtvM9YR9TpVkMVlWjXoRflD0BjETsgHncM3a+OREhlhswg9qGpKiIkLitIWAEimotDTnt7O44//nhs3769FM9RKBSwfft2LF++XHucQqGAkZGR0pgnnHACXnzxxUCbl156CYcccoj2mE6MhEUVdOifwFQl420FiagOidcn5jiS8iSob1InKr9WrChhRQi5+81R7XkHEW0TiRL6eBYnrlF6biEZpR61LRwsvM9FR1CICoXZIrKSEExcb6QN+VA6AbSFFyEpRR9RvxqKEFV2TLt/59+JXMkRqhsP0mhWEJvF8kYb0jIS9o7HvP/KlSuxZMkSzJ49GyeeeCI2bNiAoaGhUnbN4sWLcdBBB2HdunUAgHXr1mH27Nl45zvfiZGREfz4xz/G//2//xcbN24sjXn55Zfj7LPPxvvf/36ccsop2Lp1K/7jP/4DO3bs0J6XEyO60DEj9MWEccsI8U9QiZH4LSQ5JEsnvCw0qrVGAUknbwXUF3BOHIAR7IWVJ0ro4ximD8cC+dIMg69GdK0ULLKTEZ0WSxCtLSQ7vqjOh06JdvY5D8qy1grzRezotilOH51+MRUsi0KEEDqRA4nHqhcREqUVxCS912XS2HP22WfjL3/5C6699lrs2bMHxx13HLZu3VoKan3llVfQ3FxOQhgaGsJnP/tZ/L//9/+QSCRw+OGH49/+7d9w9tlnl9p89KMfxaZNm7Bu3TpccsklOOyww/DDH/4Q73vf+7Tn5cSILkPUI69olYYo6UIBQ73eP1n0Y9J2yVDkkOQHfskCW6OGrm1TKiEvEhu6IsT2Ag3wY0tsxIms4JYJRIBkwP/Z6cyJJzxkFg+etSIjaMvrJ3O1sG4x0Twk68XQkI+km3kOzvOwIkQVE6LTNmIRYlqsLM/poytCTAWFjRWkmgIkMeJbRkYb0TJSG5YvXy50y7DWjBtuuAE33HCDcsylS5di6dKl1nNyYsQGmRWEnMQUJeNzHUkrQeK5ZQxcDtVy2xBrD7mYlOJJgKAbhokFKBkJbP2/IlcNL783w9leLUig0QDE2UYqVIJFtJ/9PCyKzQnHJceVlWwXBKHSaFzoY6mYyo5hIULCuGN0RUgSOT8BtBsJ5NBGuSSiEiFhV+llj8cbgzeOaCxRf6AsQBzjBydGdCHf/SGUC1jpiBJOm7CCRKc9m2mTzSaR7MmVBUnUtTTIZ0Lf2QKVosTqG6dzoRa5aoDyxbiW7hqSDz6IYB0Wgu7cTMSUyE0jiu+Qja8qyc6zuEhEiMjown5/wmbGsP3qUITIVtENZtPkAhMRtYvbFRPV+jS8sXh9AbH46MoUSs/zowVuG0dj4MSILnTMCKmgqbCCAChXtmZESRhBkhW5ZWTEbSEhYqQL/G8VrRWkU7e9+2cPRA5G9wmzdkrYPF9yJzsA7rLP2nOTlVtni47JPgtAT5joVMgFKoWKIiWXQK6txM3XBa8oXDVESA0CU21ESLlPU6kdCdbUFSH1bgWxFSABolphu6oMQFnSW0q1CybFhxMjutCWEfZT0wli7arcH7WFJGANESGKIwlrKSFFBJOo4reKvYDTF+r9mf2ma6ZEjUqM6M6P535SjcF+FqL4EZUg0omf0RAh7PesG2U3H/39CRuUSrehx6gjEaKb0ptAFq3U96ZWIiT2VXpNBMj4uQ474MSIHSLRIbCClLAUJDxyULdnA1tLwiYOKwl9ARGtvQKUK8KSaQWCXXPgB0by6lnw0kr3R/mCzF6YaxUrQrMfIFzKK4zVhoU1P5DPglhO2ABVUVCrSYaMoQhhhQSxrNHfn5RmX/ZYKleMzrxClG03FSG6NUISfrW8BIYxxoxnIirisIJELUCE1g+RABlC2RPqaEicGNFkmJyvsyjHH8pEh2i/hSAxDlrVgRdHEgbymfSBn20kg1vRlUWUkqrDXxFNOVXWDWJC0f+bhKBlhAgF1fxMXEWiecrEBa+NZlAqYCdARIdNGfQ1iQeh2xtkxgDxixC9zJhmf4oDKPhXXpOMmqgXx9MZQ9gvrABpSJeMQ4YTI5pk/C//cAZoo+MjALFa5wgP0XZbC0koGAsJEEKUEDHSBfuF2rRESa3RDfjURUfgEDGlK4ZUpfhl7SysH+xQPDcMQSQk+qhHug5PleNBgNqJENWidc2ClXpNXDENIUBk1g/Z9oa0jLiYEYITI5qQn2hmCGjNMfeFhpYQ0fZS2XhAWBiNRitGRIcoRAk5T3ZBr1y+CmthooyQjRhda0URZdcSL2aEB+1+MjmWKK5DJT54r2MUIClOmy4Ea9ZEYQVh21fJFaPbR3flXLI9iRwKin5RW0F03DA1ESCOcYMTIxYQK0nFZbIb5oIEwX2kMBpbqVUnRiQUyeBLY9cNub5OgTy7iKCrGeg2QmESxoVTLQooixFeai+LaF0fnQJsupYR3jZN9wsgtjIAZtkwbL8xSX9bEVIjK4isj3l9EM/82Ikcir750cQV0xACxFR80NtdzbOGxokRTcjvZgBeGCIA5PojECScfV3E3ihZy4bAipTQokXHStKNyhMJESNd4JtLSR9eXxl0+8C502Thu3oQK8Rc1A1+0TOCaOE5go51RFYPhEUiPtjDyawfgJ0AIa9pN9+Y5Lj0WA3iimH72NQHSSCLJuqfU00riNbieLUSIPQ2J0YaGidGNCE/30H4580hoFfknrERJCzdlYvr2ZSKt8Y24yYF76RARESUXhNWyAitJiQzp1qEETq0QBBZQ0TtTdtwLEuqYmS61g+2b0pzHDJGC7WvwLRLcdrzjhljaq73Or54EF4f0dhJ5FBUtJUdQ3Uctq+wT9QCJApR4mhYnBjRZIB6LBnZibumD+EFiWB7KY5EI4YkcmjXTU8O2YGEWJQU4VkqkrC/Q0kBSFv2ZQVPhUtHJVB01mzhtdftQ8w4PRD/7HSPbVDrg4Z3WJn44L0WWSMAcwFCj0HHHJHvDz3eBLOCBPeRfq3+/hxIyloYK0hUbpi6ECDENRxF0lxVGUC4yNvxo8icGLEgcB8cpSDhtRsMChLToNVIYk10XDdFeEIiqhLzKdgLE4BvjRHGnJD6JoB8ET/6czddPI+IkV6E/9kpgnpFw/NO1Cbigx0jZTCWTMR0o+zmS6EsRnQECPvaQIToxnZEl5obtjZIG7dt3CvzAjUUICrx4Rg3NIQYufPOO/HFL34Re/bswaxZs3D77bfjxBNPrOoc3oIXKzIATr3KqAQJr3+pudhCYlUe3gaZ66aAshjRTRbRJYWyMBG5fVgXjig2pcKCAkjjJgJxKSaZPdSigADKxVc6QV9UrFD9amXrvqi2ycQHoG/9YMeSWWBSKH9nmEDqWllBvEPH74qxScslj2FESGSBqGEEiK31Q9S+CQ1oGXEQ6l6MfPe738XKlSuxadMmnHTSSdiwYQMWLFiAF198EZMnT67aPAZRrp9JzpuBlT3ispBQbWpSi4RFZCUh8ZldMBcjtnElUcWjyMbQ+YVwA2klwiWKX53spCuyTomyV2TjphRjmIgZnUBUWR8NAQJUtzaIrJ1tmXbx+G3+62E0+WVqI18Ur54EiEp8OMYVdS9Gbr31Vlx88cW46KKLAACbNm3Cww8/jM2bN+Oqq66q2jzoAFaa0IKEpQtSE6RpLRKWyFKEGVHSPOpfkbugl7laDXhizzSbRwR9DdD5FbVSj7IKtbp3djJ3mGgfL9xHJT7Y8UwtKboCpBueqOtWHK+BrSCysXXGL2KsVCnf1t3DbW8biBpGgNhaP2Tbo7bIVoVBhIsZqdFNaQzUtRjZt28fnnnmGaxataq0rbm5GaeeeiqefPJJbp+RkRGMjIyUXmcyXoRHPp9HPm9ap7xMc8K7021KJEq/SVLguwfeb2gMwGgB6N0HdPaSDf7fPngn4H3wbnCG4GUQdAEYhncBb/L/kv6A5ABJlOOcxoD2USDb24y20SLa24sY853sYxhDAWPIIoEujKCIdpSvfHk0obV0R9WMllJFxxY0lxbhIo9tKDsT2lHEALrRjkF0+D+cDoyhs80zhyQSo+j0xUhXF7WYGvmNFZlHQhPnOS1kWgTP6W8t7fFoRyW8bayI6+S0YWFPxDp9KBKdfsDhAXn4S4yoUcXfyBKcWHcHwBc6fZxt7Liq17Iy7ynJvKh+ia480O8/FqlxCsExm7uoi3mCusiOAgnyOg90Bi765Q88EbCCBP+pnQHhkaP6kOdNVP+m0lox/szQXRIIzVT/lsC4QGsgIDUR2NfGtYIAQBHt6MqPeKeR/AgKaC+NU/C/0OQ9j/mvyfse87+s5FijvtUusS8beJ2kBEjpTMkTAez1T6eNrK0oGY0egz5XCARIfth7H2HO81H0d9jRVCwW6zY7+7XXXsNBBx2EJ554AnPmzCltv+KKK/Doo4/il7/8ZUWftWvX4rrrrqvYvmXLFiSTvDO0w+FwOBwe2WwW5513Hvr7+9Hbaxqorkcmk0FfXx+A+8C/c9AlC+CcWOdaLeraMmLDqlWrsHLlytLrTCaD6dOn47TTTgv1z8rn89i2bRvmz5+PtraQAYjjEPf5yHGfjxz3+ahxn5GcqD4fYk13VJe6FiMHHHAAWlpasHfv3sD2vXv3YurUqdw+HR0d6OioDKZoa2uL5Acc1TjjFff5yHGfjxz3+ahxn5GcsJ+P+2xrQ72EGnJpb2/H8ccfj+3bt5e2FQoFbN++PeC2cTgcDoej8XgL3qrctn9vVX/KMVHXlhEAWLlyJZYsWYLZs2fjxBNPxIYNGzA0NFTKrnE4HA6Hw9HY1L0YOfvss/GXv/wF1157Lfbs2YPjjjsOW7duxZQpU2o9NYfD4XA4HBFQ92IEAJYvX47ly5fXehoOh8PhcDhioK5jRhwOh8PhcIx/GsIy4nA4HA7H+GMAgvUkNNFfNLXecZYRh8PhcDgcNcWJEYfD4XA4HDXFiRGHw+FwOBw1xcWMOBwOh8NREwYgX8Zbhe6qm/WPs4w4HA6HwzGBuPPOO3HooYeis7MTJ510Ep566ilp++9///s4/PDD0dnZiWOOOQY//vGPA/uLxSKuvfZaHHjggUgkEjj11FPx+9//3mhOTow4HA6HwzFB+O53v4uVK1dizZo1ePbZZzFr1iwsWLAAb7zxBrf9E088gXPPPRef/OQn8dxzz2HRokVYtGgRnn/++VKbW265BV/96lexadMm/PKXv0RXVxcWLFiA4WF9y40TIw6Hw+FwTBBuvfVWXHzxxbjoootw5JFHYtOmTUgmk9i8eTO3/W233YZ/+Id/wOWXX44jjjgC119/Pd773vfijjvuAOBZRTZs2IBrrrkGZ555Jo499lh8+9vfxmuvvYYHHnhAe17jPmakWCwCCL8sdD6fRzabRSaTcas6cnCfjxz3+chxn48a9xnJierzIdcKcu2Il5FI+rPXN9Hq9fv27cMzzzyDVatWlbY1Nzfj1FNPxZNPPsk9wpNPPomVK1cGti1YsKAkNF5++WXs2bMHp556aml/X18fTjrpJDz55JM455xztN7JuBcjAwMDAIDp06fXeCYOh8PhaBQGBgbQ19cXy9jt7e2YOnUq9uz5Yuixuru7K65va9aswdq1ayvavvnmmxgbG6tY223KlCn43e9+xx1/z5493PZ79uwp7SfbRG10GPdiZNq0adi9ezd6enrQ1NRkPU4mk8H06dOxe/du9Pb2RjjD8YH7fOS4z0eO+3zUuM9ITlSfT7FYxMDAAKZNmxbh7IJ0dnbi5Zdfxr59+0KPVSwWK65tPKtIvTPuxUhzczMOPvjgyMbr7e11JwIJ7vOR4z4fOe7zUeM+IzlRfD5xWURoOjs70dnZGftxaA444AC0tLRg7969ge179+7F1KlTuX2mTp0qbU8e9+7diwMPPDDQ5rjjjtOemwtgdTgcDodjAtDe3o7jjz8e27dvL20rFArYvn075syZw+0zZ86cQHsA2LZtW6n9zJkzMXXq1ECbTCaDX/7yl8IxeYx7y4jD4XA4HA6PlStXYsmSJZg9ezZOPPFEbNiwAUNDQ7jooosAAIsXL8ZBBx2EdevWAQAuvfRSfOADH8CXv/xlnHHGGbjvvvuwc+dO3HXXXQCApqYmrFixAjfccAPe/e53Y+bMmVi9ejWmTZuGRYsWac/LiRFNOjo6sGbNmob0xVUD9/nIcZ+PHPf5qHGfkRz3+ehx9tln4y9/+QuuvfZa7NmzB8cddxy2bt1aCkB95ZVX0NxcdprMnTsXW7ZswTXXXIOrr74a7373u/HAAw/g6KOPLrW54oorMDQ0hH/6p39COp3G+973PmzdutXIDdVUrE7+ksPhcDgcDgcXFzPicDgcDoejpjgx4nA4HA6Ho6Y4MeJwOBwOh6OmODHicDgcDoejpjgxooHpcssTiXXr1uGEE05AT08PJk+ejEWLFuHFF1+s9bTqlptvvrmUCufwePXVV/GJT3wC+++/PxKJBI455hjs3Lmz1tOqC8bGxrB69WrMnDkTiUQC73znO3H99ddXad2U+uOxxx7DwoULMW3aNDQ1NVUsxBbFUvaO2uDEiALT5ZYnGo8++iiWLVuGX/ziF9i2bRvy+TxOO+00DA0N1XpqdcfTTz+Nr3/96zj22GNrPZW64e2338bJJ5+MtrY2/OQnP8ELL7yAL3/5y9hvv/1qPbW6YP369di4cSPuuOMO/Pa3v8X69etxyy234Pbbb6/11GrC0NAQZs2ahTvvvJO7P4ql7B01ouiQcuKJJxaXLVtWej02NlacNm1acd26dTWcVf3yxhtvFAEUH3300VpPpa4YGBgovvvd7y5u27at+IEPfKB46aWX1npKdcGVV15ZfN/73lfradQtZ5xxRnHp0qWBbf/4j/9YPP/882s0o/oBQPH+++8vvS4UCsWpU6cWv/jFL5a2pdPpYkdHR/E73/lODWboMMFZRiSQ5ZbppZFVyy1PdPr7+wEAkyZNqvFM6otly5bhjDPOCHyXHMCPfvQjzJ49G2eddRYmT56M97znPfjGN75R62nVDXPnzsX27dvx0ksvAQB+9atf4fHHH8fpp59e45nVH6ql7B31javAKsFmueWJTKFQwIoVK3DyyScHqvNNdO677z48++yzePrpp2s9lbrjj3/8IzZu3IiVK1fi6quvxtNPP41LLrkE7e3tWLJkSa2nV3OuuuoqZDIZHH744WhpacHY2BhuvPFGnH/++bWeWt0R1VL2jtrgxIgjMpYtW4bnn38ejz/+eK2nUjfs3r0bl156KbZt21b1FTobgUKhgNmzZ+Omm24CALznPe/B888/j02bNjkxAuB73/se7r33XmzZsgVHHXUUdu3ahRUrVmDatGnu83GMK5ybRoLNcssTleXLl+Ohhx7CI488goMPPrjW06kbnnnmGbzxxht473vfi9bWVrS2tuLRRx/FV7/6VbS2tmJsbKzWU6wpBx54II488sjAtiOOOAKvvPJKjWZUX1x++eW46qqrcM455+CYY47BBRdcgMsuu6y0iJmjDL2UPY07XzcGToxIsFlueaJRLBaxfPly3H///fj5z3+OmTNn1npKdcUHP/hB/PrXv8auXbtKf7Nnz8b555+PXbt2oaWlpdZTrCknn3xyRSr4Sy+9hEMOOaRGM6ovstlsYNEyAGhpaUGhUKjRjOqXqJayd9QG56ZRoFpueaKzbNkybNmyBQ8++CB6enpKvtm+vj4kEokaz6729PT0VMTPdHV1Yf/993dxNQAuu+wyzJ07FzfddBM+/vGP46mnnsJdd91VWp58orNw4ULceOONmDFjBo466ig899xzuPXWW7F06dJaT60mDA4O4g9/+EPp9csvv4xdu3Zh0qRJmDFjRiRL2TtqRK3TeRqB22+/vThjxoxie3t78cQTTyz+4he/qPWU6gYA3L+777671lOrW1xqb5D/+I//KB599NHFjo6O4uGHH1686667aj2luiGTyRQvvfTS4owZM4qdnZ3Fv/mbvyl+/vOfL46MjNR6ajXhkUce4Z5vlixZUiwWvfTe1atXF6dMmVLs6OgofvCDHyy++OKLtZ20Q4umYnGClvJzOBwOh8NRF7iYEYfD4XA4HDXFiRGHw+FwOBw1xYkRh8PhcDgcNcWJEYfD4XA4HDXFiRGHw+FwOBw1xYkRh8PhcDgcNcWJEYfD4XA4HDXFiRGHw+FwOBw1xYkRh8MBAHj99ddx3nnn4W//9m/R3NyMFStW1HpKDodjguDEiMPhAACMjIzgHe94B6655hrMmjWr1tNxOBwTCCdGHI5xxF/+8hdMnToVN910U2nbE088gfb29sBqpjwOPfRQ3HbbbVi8eDH6+vrinqrD4XCUcKv2OhzjiHe84x3YvHkzFi1ahNNOOw2HHXYYLrjgAixfvhwf/OAHaz09h8Ph4OLEiMMxzvjQhz6Eiy++GOeffz5mz56Nrq4urFu3rtbTcjgcDiHOTeNwjEO+9KUvYXR0FN///vdx7733oqOjo9ZTcjgcDiFOjDgc45D/+Z//wWuvvYZCoYA//elPtZ6Ow+FwSHFuGodjnLFv3z584hOfwNlnn43DDjsMn/rUp/DrX/8akydPrvXUHA6Hg4sTIw7HOOPzn/88+vv78dWvfhXd3d348Y9/jKVLl+Khhx5S9t21axcAYHBwEH/5y1+wa9cutLe348gjj4x51g6HYyLTVCwWi7WehMPhiIYdO3Zg/vz5eOSRR/C+970PAPCnP/0Js2bNws0334x//ud/lvZvamqq2HbIIYc4V4/D4YgVJ0YcDofD4XDUFBfA6nA4HA6Ho6Y4MeJwTBCOOuoodHd3c//uvffeWk/P4XBMYJybxuGYIPz5z39GPp/n7psyZQp6enqqPCOHw+HwcGLE4XA4HA5HTXFuGofD4XA4HDXFiRGHw+FwOBw1xYkRh8PhcDgcNcWJEYfD4XA4HDXFiRGHw+FwOBw1xYkRh8PhcDgcNcWJEYfD4XA4HDXFiRGHw+FwOBw15f8Dhk3O3b8883YAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def error(D, x):\n", + " return torch.abs(5*torch.exp(-1/20.0 * ((x[:, :1] - 3)**2 + (x[:, 1:] - 3)**2)) - D)\n", + "\n", + "fig = tp.utils.plot(model_D, error, plot_sampler, plot_type='contour_surface')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "anim_sampler = tp.samplers.AnimationSampler(domain_x, domain_t, 200, n_points=760)\n", + "fig, anim = tp.utils.animate(model_u, lambda u: u, anim_sampler, ani_speed=10, ani_type='contour_surface')\n", + "anim.save('inverse-heat-eq-wrapper.gif')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/setup.cfg b/setup.cfg index 7f36c77f..b7912acd 100644 --- a/setup.cfg +++ b/setup.cfg @@ -46,7 +46,7 @@ package_dir = # new major versions. This works if the required packages follow Semantic Versioning. # For more information, check out https://semver.org/. install_requires = - torch>=2.0.0, <2.4 + torch>=2.0.0, <=2.4 pytorch-lightning>=2.0.0 numpy>=1.20.2, <2.0 matplotlib>=3.0.0 diff --git a/src/torchphysics/__init__.py b/src/torchphysics/__init__.py index 542903f6..1cd07388 100644 --- a/src/torchphysics/__init__.py +++ b/src/torchphysics/__init__.py @@ -6,6 +6,7 @@ from .models import * from .utils import * from .solver import Solver, OptimizerSetting +from .wrapper import TPModulusWrapper, ModulusArchitectureWrapper if sys.version_info[:2] >= (3, 8): # TODO: Import directly (no need for conditional) when `python_requires = >= 3.8` diff --git a/src/torchphysics/utils/callbacks.py b/src/torchphysics/utils/callbacks.py index 753540cb..0a0f19e0 100644 --- a/src/torchphysics/utils/callbacks.py +++ b/src/torchphysics/utils/callbacks.py @@ -54,9 +54,7 @@ def on_train_start(self, trainer, pl_module): self.model.state_dict(), self.path + "/" + self.name + "_init.pt" ) - def on_train_batch_start( - self, trainer, pl_module, batch, batch_idx, dataloader_idx - ): + def on_train_batch_start(self, trainer, pl_module, batch, batch_idx): if (self.check_interval > 0 and batch_idx > 0) and ( (batch_idx - 1) % self.check_interval == 0 ): @@ -124,7 +122,7 @@ def on_train_start(self, trainer, pl_module): self.point_sampler.sample_points(device=pl_module.device) def on_train_batch_end( - self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx + self, trainer, pl_module, outputs, batch, batch_idx ): if batch_idx % self.check_interval == 0: fig = plot( @@ -146,7 +144,7 @@ def on_train_end(self, trainer, pl_module): class TrainerStateCheckpoint(Callback): """ - A callback to saves the current state of the trainer (a PyTorch Lightning checkpoint), + A callback to save the current state of the trainer (a PyTorch Lightning checkpoint), if the training has to be resumed at a later point in time. Parameters @@ -162,8 +160,9 @@ class TrainerStateCheckpoint(Callback): Note ---- - To continue from the checkpoint, use `resume_from_checkpoint ="path_to_ckpt_file"` as an - argument in the initialization of the trainer. + To continue from the checkpoint, use ckpt_path="some/path/to/my_checkpoint.ckpt" as + argument in the fit command of the trainer. + The PyTorch Lightning checkpoint would save the current epoch and restart from it. In TorchPhysics we dont use multiple epochs, instead we train with multiple iterations @@ -180,7 +179,7 @@ def __init__(self, path, name, check_interval=200, weights_only=False): self.weights_only = weights_only def on_train_batch_end( - self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx + self, trainer, pl_module, outputs, batch, batch_idx ): if batch_idx % self.check_interval == 0: trainer.save_checkpoint( diff --git a/src/torchphysics/wrapper/TPModulusWrapper.rst b/src/torchphysics/wrapper/TPModulusWrapper.rst new file mode 100644 index 00000000..3fa54a54 --- /dev/null +++ b/src/torchphysics/wrapper/TPModulusWrapper.rst @@ -0,0 +1,109 @@ +=============================== +TorchPhysics to Modulus Wrapper +=============================== + +This folder contains a wrapper module for TorchPhysics to `NVIDIA Modulus`_. +This module serves as a bridge between the two frameworks and allows users to train TorchPhysics models with the Modulus training framework with minimal changes to their existing code. + +Both libraries are based on Pytorch_, but instead of `PyTorch Lightning`_, Modulus uses its own *Distributedmanager*, `Pytorch JIT`_ or TorchScript_ backend and CUDA graphs as a further framework to optimize and accelerate the training process, especially on NVIDIA GPUs. + +Both libraries use ``torch.nn.Module`` as the base class for the model definition, so that any model architecture of Modulus can easily be used as a TorchPhysics model, which is one of the main purposes of this wrapper module. +Modulus offers a wide range of model architectures, including various types of Fourier neural networks. + +As a second purpose, the wrapper module can be used to automatically convert a TorchPhysics problem to Modulus and perform the training in Modulus to benefit from the optimizable Modulus training framework. + +In both libraries, geometries are defined for the spatial domain of the problem (PDE or ODE), but the Modulus geometry provides a signed distance function (SDF) that calculates the distance of any arbitrary point to the boundary of the geometry. +This makes it possible to weight the loss function of the problem with the distance to the boundary, which is recommended in Modulus, e.g. for sharp gradients near the boundary, as it can increase convergence speed and improve accuracy. + +The wrapper module generally provides spatial loss weighting and the loss balancing algorithms of Modulus, e.g. GradNorm or Learning Rate Annealing. + +Each of the two main classes of the wrapper module can be used independently, allowing the user to choose whether to use only the conversion of the problem or only the model architecture, or both. + +Variable conventions +==================== +The spatial and temporal variables in Modulus are always defined as 'x', 'y', 'z' and 't', so TorchPhysics models can only be converted if the spatial variable is defined as 'x', which can be 1D, 2D or 3D, or 'x', 'y' or 'x', 'y','z' and the temporal variable as 't'. + +Contents +======== +The wrapper module consists of two main classes: + +* ``TPModulusWrapper``: can be used to convert a TorchPhysics problem into Modulus and to perform training in Modulus. ``help(TPModulusWrapper)`` provides a detailed description of the class and its parameters. + +* ``ModulusArchitectureWrapper``: can be used to choose any implemented Modulus architecture as TorchPhysics model. ``help(ModulusArchitectureWrapper)`` provides a detailed description of the class and its parameters. + +Usage +===== +To use the wrapper module to run a TorchPhysics model in Modulus, you can add the following lines to your existing TorchPhysics code after the definition +of the ``trainer`` and the ``solver`` object and replace the ``trainer.fit(solver)`` call: + +.. code-block:: python + + torchphysics.wrapper.TPModulusWrapper(trainer,solver).train() + + +To use one of the Modulus model architectures, you can add the following lines to your existing TorchPhysics code and replace the model definition, +e.g. if you want to use the Modulus Fourier architecture as a TorchPhysics model: + +.. code-block:: python + + model=torchphysics.wrapper.ModulusArchitectureWrapper(input_space=X*T, output_space=U,arch_name='fourier',frequencies = ['axis',[0,1,2]]) + +Installation +============ +The wrapper module requires a working installation of TorchPhysics and Modulus Symbolic (Sym), which is a framework providing pythonic APIs, algorithms +and utilities to be used with Modulus core to physics inform model training as well as higher level abstraction for domain experts. + +The installation of Modulus Sym is documented here: `NVIDIA Modulus Github Repository`_ + +We recommend to create a new conda environment and to first install NVIDIA Modulus with the following commands: + +.. code-block:: python + + pip install nvidia-modulus.sym + +Then you can install TorchPhysics as described in the `TorchPhysics documentation`_. + +As Modulus Sym uses TorchScript_ by default to compile the model, it is important to have the correct version of PyTorch_ installed. The current Modulus Sym version requires 2.1.0a0+4136153. +If a different version is installed, a warning will be raised when starting the training and the use of TorchScript is disabled. +The version can be determined by the command + +.. code-block:: python + + torch.__version__ + +To circumvent disabling of TorchScript you can edit the file /modulus/sym/constants.py in your python site packages installation path, and change the constant JIT_PYTORCH_VERSION = "2.1.0a0+4136153" to the version you have installed, e.g. "2.4.0+cu121". + +.. _`PyTorch Lightning`: https://www.pytorchlightning.ai/ +.. _`NVIDIA Modulus`: https://developer.nvidia.com/modulus +.. _`NVIDIA Modulus Github Repository`: https://github.com/NVIDIA/modulus-sym/tree/main +.. _PyTorch: https://pytorch.org/ +.. _TorchScript: https://pytorch.org/docs/stable/jit.html +.. _`Pytorch JIT`: https://pytorch.org/docs/stable/jit.html + + + +Testing +======= +As the wrapper module needs additional installation steps and can not be used without Modulus, it is excluded from the automatic testing with pytest. To test the functionality of the wrapper, there are example notebooks in the folder examples/wrapper and tests in src/torchphysics/wrapper/tests that can be manually invoked by the command (requires the installation of pytest and pytest-cov): + +.. code-block:: python + + pytest src/torchphysics/wrapper/tests + + + +Some notes +========== +* The loss definition in Modulus is based on Monte Carlo integration and therefore the loss is scaled proportional to the corresponding area, i.e. it is usually different from the loss in TorchPhysics, where the loss is the mean value. +* Currently, ``stl``-file support in Modulus is only available for Docker installation, so ``shapely`` and ``Trimesh`` geometries in TorchPhysics can not be converted. +* Cross product domains are generally not supported in Modulus, so must be automatically converted by the wrapper to existing primary geometries, so not all combinations of domain operations are allowed, e.g. product domains only from the union of 1D or 0D domains and no further rotation and translation is allowed (must be done with the entire product). +* Physics-Informed Deep Operator Networks (PIDOns) are currently not supported in the wrapper. The current implementation and documentation is much better in TorchPhysics than in Modulus +* Fourier Neural Operators (FNOs) are currently not supported in the wrapper, but an FNO framework is currently being developed in TorchPhysics. +* Samplers other than random uniformn and Halton sequence are not supported in Modulus. +* The imposition of exact boundary conditions using hard constraints with Approximate Distance Functions (ADFs) is not yet supported in TorchPhysics. +* The Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimizer can be used in Modulus by setting the maximum step size (``max_steps``) to 1 (one single optimization step), but using the maximum number of iterations per optimization step (``max_iter``) as the number of iterations instead. This is very slow, so it is recommended to use Adam instead. In TorchPhysics, ``max_iter`` is decreased and many optimization steps are performed. +* If the combination of the Adam and L-BFGS optimizers is used, then loading the L-BFGS optimizer checkpoint file (optim_checkpoint.0.pth) will result in an error regarding ``max_iter`` as Adam does not use ``max_iter``. This is a known issue for Modulus support and it is recommended to delete or rename the optim_checkpoint.0.pth file. Then it works, but Tensorboard cannot display the loss history correctly! +* If several losses with the same name of the objective variable are used, the losses are summarized in Tensorboard, e.g. initial condition for T and Dirichlet condition for T, then there is only one loss (sum) for T. +* In general, all TorchPhysics callbacks are supported, but for the ``WeightSaveCallback`` the check for minimial loss (parameter ``check_interval``) is not supported by the wrapper, only initial and final model states are saved. +* Modulus automatically provides Tensorboard logging of the losses. The corresponding logging folder is ``outputs`` by default, but can be set by the user with the parameter ``outputdir_name``. +* Modulus automatically provides ``.vtp``-files containing data computed on the collocation points of the conditions that can be found in subfolders of the output directory. These files can be viewed using visualization tools like Paraview. \ No newline at end of file diff --git a/src/torchphysics/wrapper/__init__.py b/src/torchphysics/wrapper/__init__.py new file mode 100644 index 00000000..6a95314f --- /dev/null +++ b/src/torchphysics/wrapper/__init__.py @@ -0,0 +1,7 @@ +""" Wrapper module that serves as a bridge between TorchPhysics and Nvidia Modulus and allows users to train TorchPhysics models with the Modulus training framework with minimal changes to their existing code. +""" +from .wrapper import TPModulusWrapper +from .solver import ModulusSolverWrapper +from .model import ModulusArchitectureWrapper +from .helper import convertDataModulus2TP, convertDataTP2Modulus + \ No newline at end of file diff --git a/src/torchphysics/wrapper/geometry.py b/src/torchphysics/wrapper/geometry.py new file mode 100644 index 00000000..a509970b --- /dev/null +++ b/src/torchphysics/wrapper/geometry.py @@ -0,0 +1,1280 @@ +# This file includes work covered by the following copyright and permission notices: +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# All rights reserved. +# Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import importlib +import torch.nn as nn +from torch import atan2, sqrt +import numpy as np +from functools import reduce +from operator import mul + +from modulus.sym.geometry.geometry import Geometry, csg_curve_naming +from modulus.sym.geometry.curve import Curve, SympyCurve +from modulus.sym.geometry.parameterization import Parameterization, Bounds, Parameter +from modulus.sym.geometry.helper import _sympy_sdf_to_sdf + +from sympy import Symbol, Min, Max, Heaviside, sqrt, Abs, sign, Eq, Or, And, Rational, lambdify +from sympy.vector import CoordSys3D + + + +def GeometryAxischange( + self, + axis1: str="x", + axis2: str="y", + axis3: str="z", + parameterization=Parameterization(), + ): + """ + Exchange two axes of the geometry. Many primitive geometries in Modulus are defined in a specific coordinate system and this function allows to change the coordinate system of the geometry. + It transforms the boundary curves, bounds and computes a new sdf function as wrapper function. + Parameters + ---------- + axis1: str + name of the first axis + axis2: str + name of the second axis + axis3: str + name of the third axis + parameterization: Parameterization, optional + parameterization of the geometry + + Returns + ------- + Geometry + new geometry object with exchanged axes + + """ + # create wrapper function for sdf function + def _axischange_sdf(sdf, dims,axes): + def axischange_sdf(invar, params, compute_sdf_derivatives=False): + changed_invar = {**invar} + _changed_invar = {**changed_invar} + for i, key in enumerate(dims): + _changed_invar[key] = changed_invar[axes[i]] + + # compute sdf + computed_sdf = sdf(_changed_invar, params, compute_sdf_derivatives) + return computed_sdf + + return axischange_sdf + + axes = (axis1,axis2,axis3) + new_sdf = _axischange_sdf(self.sdf, self.dims, axes) + # add parameterization + new_parameterization = self.parameterization.union(parameterization) + + # change bounds + bound_ranges = {**self.bounds.bound_ranges} + _bound_ranges = {**bound_ranges} + + for i, key in enumerate(self.dims): + _bound_ranges[Parameter(key)] = bound_ranges[Parameter(axes[i])] + + new_bounds = self.bounds.copy() + new_bounds.bound_ranges = _bound_ranges + + # change curves + new_curves = [] + for c in self.curves: + new_c = c.axischange(axes, parameterization) + new_curves.append(new_c) + + # return rotated geometry + return Geometry( + new_curves, + new_sdf, + len(self.dims), + new_bounds, + new_parameterization, + interior_epsilon=self.interior_epsilon, + ) + +def CurveAxischange(self, axes, parameterization=Parameterization()): + """ + Exchange two axes of the curves. Many primitive geometry curves + in Modulus are defined in a specific coordinate system and this + function allows to change the coordinate system of the curve. + It computes a new sample function for the curves as wrapper + function. + + Parameters + ---------- + axes: tuple + names of the axes + parameterization: Parameterization, optional + parameterization of the geometry + Returns + ------- + Curve + new curve object with exchanged axes + """ + def _sample(internal_sample, dims, axes): + def sample( + nr_points, parameterization=Parameterization(), quasirandom=False + ): + # sample points + invar, params = internal_sample( + nr_points, parameterization, quasirandom + ) + changed_invar = {**invar} + _changed_invar = {**changed_invar} + + for i, key in enumerate(dims): + _changed_invar[key] = changed_invar[axes[i]] + _changed_invar['normal_'+key] = changed_invar['normal_'+axes[i]] + + return _changed_invar, params + + return sample + return Curve( + _sample(self._sample, self.dims,axes), + len(self.dims), + self.parameterization.union(parameterization), + ) + +# Modulus Curve class gets the additional method CurveAxischange +Curve.axischange=CurveAxischange +# Modulus Geometry class gets the additional method GeometryAxischange +Geometry.axischange=GeometryAxischange + +class TPGeometryWrapper(): + """ + Wrapper to convert dictionary with spatial domain decomposition + into Modulus geometry. The domain is recursively analyzed mainly + concerning domain operations and product domains to identify the + underlying spatial geometries. + A general product of domains as in TorchPhysics can not be + implemented in Modulus, because the domain defines a spatial + geometry that must have a sdf implementation which is not available + in general for an arbitrary product domain. + Therefore each product domain has to be analyzed and mapped on a + spatial geometry resulting on union/intersection/cut of a + translated/rotated primitive geometry of Modulus. + + Not supported types/combinations in TorchPhysics? + + Parameters + ---------- + domain_dict: dictionary + spatial domain decomposition + + + + """ + def __init__(self,domain_dict)-> None: + self.domain_dict = domain_dict + + def getModulusGeometry(self): + return self.RecursiveDomainDictGeoWrapper(self.domain_dict,translate_vec=[0,0,0],rotation_list=[[],[],[],0]) + + + def RecursiveDomainDictGeoWrapper(self,domain_dict,prod_factor=None,translate_vec=[0,0,0],rotation_list=[[],[],[],0]): + """ + Recursive function that analyzes a dictionary of TorchPhysics + (spatial) domains as a result of domain decomposition to map + the domain to a Modulus geometry. + + Parameters + ---------- + domain_dict: dictionary + dictionary with TorchPhysics (spatial) domains. + Keys are domain operations: + 'u' (union), 'ub' (boundary of union),'d' (decomposed), + 'i' (intersection), 'ib' (boundary of intersection), + 'c' (cut), 'cb' (boundary of cut), 't' (tranlation), + 'r' (rotation),'p' (product) + prod_factor: float, optional + domain factor of previous decomposed product (3D domain + can consist of two cross products) + + Returns + ------- + geometry object + Modulus geometry object + is_boundary: bool + True if domain is boundary + cond: sympy expression + condition to restrict on parts of geometry, resulting from + cross products + translate_vec: list + translation vector, containing the translation in x,y,z + rotation_list: list + list containing the rotation angles, rotation axes, rotation points and the priority of the rotation vs. translation + """ + + for key, val in domain_dict.items(): + if (key=='u') or (key=='ub'): + is_boundary1, geometry1, cond1, translate_vec, rotation_list = self.RecursiveDomainDictGeoWrapper(val[0],prod_factor,translate_vec,rotation_list) + is_boundary2, geometry2, cond2, translate_vec, rotation_list = self.RecursiveDomainDictGeoWrapper(val[1],prod_factor,translate_vec,rotation_list) + + assert is_boundary1==is_boundary2 + + if cond1==cond2: + if cond1!=None: + cond = cond1 + else: + cond =None + elif cond1==None: + cond = cond2 + elif cond2==None: + cond=cond1 + else: + cond = self._or_condition(self._geo_condition(geometry1.sdf,cond1,is_boundary1),self._geo_condition(geometry2.sdf,cond2,is_boundary2)) + return is_boundary1,geometry1+geometry2, cond, translate_vec, rotation_list + elif key=='i' or key=='ib': + is_boundary1, geometry1, cond1, translate_vec, rotation_list = self.RecursiveDomainDictGeoWrapper(val[0],prod_factor,translate_vec, rotation_list) + is_boundary2, geometry2, cond2, translate_vec, rotation_list = self.RecursiveDomainDictGeoWrapper(val[1],prod_factor,translate_vec, rotation_list) + assert is_boundary1==is_boundary2 + if cond1==cond2: + if cond1!=None: + cond = cond1 + else: + cond =None + elif cond1==None: + cond = cond2 + elif cond2==None: + cond=cond1 + else: + cond = "And(" + cond1 + "," + cond2 + ")" + return is_boundary1, geometry1&geometry2, cond, translate_vec, rotation_list + + elif key=='c' or key=='cb': + is_boundary1, geometry1, cond1, translate_vec, rotation_list = self.RecursiveDomainDictGeoWrapper(val[0],prod_factor,translate_vec, rotation_list) + is_boundary2, geometry2, cond2, translate_vec, rotation_list = self.RecursiveDomainDictGeoWrapper(val[1],prod_factor,translate_vec, rotation_list) + assert is_boundary1==is_boundary2 + if cond1==cond2: + if cond1!=None: + cond = cond1 + else: + cond =None + elif cond1==None: + cond = cond2 + elif cond2==None: + cond=cond1 + else: + cond = "Or(" + cond1 + "," + cond2 + ")" + return is_boundary1, geometry1-geometry2, cond, translate_vec, rotation_list + + elif key=='p': + if prod_factor is None: + return self.ProductDomainWrapper(val[0],val[1],translate_vec=translate_vec, rotation_list=rotation_list) + else: + return self.ProductDomainWrapper(val[0],val[1],prod_factor,translate_vec=translate_vec, rotation_list=rotation_list) + + + elif key=='t': + assert ((next(iter(val[0].keys()))=='d') | (next(iter(val[0].keys()))=='r') ), "Only translation of primitive or rotated domains allowed" + rot_angles, rot_axes, rot_points, rot_prio = rotation_list + if next(iter(val[0].keys()))=='d': + # translation has to be done first + rotation_list[3] = 1 + dom_space = val[0]['d'].space + else: + # rotation has to be done first + rotation_list[3] = 0 + assert (next(iter(val[0]['r'][0].keys()))=='d'), "Only translation of primitive or rotated domains allowed" + dom_space = val[0]['r'][0]['d'].space + + vec = [float(val_in) for val_in in val[1]] + translate_vec_new = self.compute_3D_translate_vec(dom_space,vec) + + if translate_vec !=[0,0,0]: + translate_vec =[translate_vec[ii]+translate_vec_new[ii] for ii in range(3)] + else: + translate_vec = translate_vec_new + + is_boundary, geometry, cond, translate_vec, rotation_list = self.RecursiveDomainDictGeoWrapper(val[0],prod_factor=prod_factor,translate_vec=translate_vec,rotation_list=rotation_list) + return is_boundary, geometry, cond, translate_vec, rotation_list + elif key=='r': + assert ((next(iter(val[0].keys()))=='d') | (next(iter(val[0].keys()))=='t')), "Only rotation of primitive or translated domains allowed" + rot_angles, rot_axes, rot_points, rot_prio = rotation_list + + + if next(iter(val[0].keys()))=='d': + # rotation has to be done first + rotation_list[3] = 0 + dom_space = val[0]['d'].space + else: + # translation has to be done first + rotation_list[3] = 1 + assert (next(iter(val[0]['t'][0].keys()))=='d'), "Only rotation of primitive or translated domains allowed" + dom_space = val[0]['t'][0]['d'].space + + rot_matrix = val[1] + rot_point = [float(val_in) for val_in in val[2]] + if len(rot_point)==2: + rot_axis, rot_point = self.compute_3D_rotation(dom_space,rot_point) + rot_angles.append(float(atan2(rot_matrix[0, 1, 0], rot_matrix[0, 0, 0]))) + rot_axes.append(rot_axis) + rot_points.append(rot_point) + else: + theta_x = float(atan2(rot_matrix[2, 1], rot_matrix[2, 2])) + theta_y = float(atan2(-rot_matrix[2, 0], sqrt((rot_matrix[2, 1] ** 2) + (rot_matrix[2, 2] ** 2)))) + theta_z = float(atan2(rot_matrix[1, 0], rot_matrix[0, 0])) + rot_angles.extend([theta_x, theta_y, theta_z]) + rot_axes.extend(['x', 'y', 'z']) + rot_points.extend([rot_point, rot_point, rot_point]) + + is_boundary, geometry, cond, translate_vec, rotation_list = self.RecursiveDomainDictGeoWrapper(val[0],prod_factor=prod_factor,translate_vec=translate_vec,rotation_list=rotation_list) + return is_boundary, geometry, cond, translate_vec, rotation_list + + elif key=='d': + if prod_factor is None: + return self.GeometryNameMapper(domain_dict['d'],translate_vec=translate_vec,rotation_list=rotation_list) + else: + return self.ProductDomainWrapper(domain_dict,prod_factor,translate_vec=translate_vec,rotation_list=rotation_list) + + + def _geo_condition(self, sdf_func,sympy_expression,is_boundary): + """ + Wrapper function to convert a general sympy_expression to a + condition that is only valid for points on the geometry + (interior or on the boundary). + It is based on evaluating the sdf function of the geometry and + the sympy expression. + It returns a condition function that can be evaluated for + arbitrary points, but is only true for points on the geometry + that satisfy the sympy expression. + + Parameters + ---------- + sdf_func: function + signed distance function + sympy_expression: sympy expression + sympy expression to restrict the geometry + is_boundary: bool + True if domain is boundary + Returns + ------- + function + condition function + """ + def geo_condition(invar,params): + x=Symbol('x') + y=Symbol('y') + z=Symbol('z') + + f2 = sdf_func(invar,params) + # Get the number of arguments that f1 takes + num_args = len(invar) + + if num_args == 3: + # If f1 takes 3 arguments, call it with 'x', 'y', 'z' + f1 = lambdify([(x,y,z)], sympy_expression) + result = f1((invar['x'], invar['y'], invar['z'])) + elif num_args == 2: + f1 = lambdify([(x,y)], sympy_expression) + result = f1((invar['x'], invar['y'])) + else: + f1 = lambdify([x], sympy_expression) + result = f1(invar['x']) + + if is_boundary: + return np.equal(f2["sdf"],0)&result + else: + return np.greater(f2["sdf"],0)& result + return geo_condition + + def _or_condition(self,f1,f2): + """ + Wrapper function to combine two condition functions with an OR + operation. + """ + def or_condition(invar,params): + return np.logical_or(f1(invar,params),f2(invar,params)) + return or_condition + + def changeVarNames(self,domain,dim): + """ + Change variable names of TorchPhysics variables to Modulus + variable names, if variable 'x' is of higher dimension than 1. + """ + vars = list(domain.space.keys()) + if vars==['x']: + if dim ==2: + return ['x','y'] + elif dim ==3: + return ['x','y','z'] + else: + return ['x'] + else: + return vars + + + def ProductDomainWrapper(self,dom1,dom2,dom3=None,translate_vec = [0,0,0],rotation_list=[[],[],[],0]): + """ + Analyzes the elements of a decomposition of a TorchPhysics + product domain (cross product of domains) and maps the whole + product domain to a Modulus geometry. + + Parameters + ---------- + dom1: domain object + domain object from TorchPhysics + dom2: domain object + domain object from TorchPhysics + dom3: domain object, optional + domain object from TorchPhysics + + Returns + ------- + geometry object + Modulus geometry object + is_boundary: bool + True if domain is boundary + cond: sympy expression + condition to restrict on parts of geometry + """ + imported_module_3d = importlib.import_module("modulus.sym.geometry.primitives_3d") + imported_module_2d = importlib.import_module("modulus.sym.geometry.primitives_2d") + + x, y, z = Symbol("x"), Symbol("y"), Symbol("z") + cond=None + if dom3 == None: + key1 = next(iter(dom1.keys())) + val1 = dom1[key1] + key2 = next(iter(dom2.keys())) + val2 = dom2[key2] + + # sorting by domain dimension: dom1.dim>=dom2.dim ? + # 2D geometries in 3D define boundaries! + if key1 == 'd': + if key2 == 'd': + if val1.dim < val2.dim: + dom1,dom2 = dom2,dom1 + key1 = next(iter(dom1.keys())) + val1 = dom1[key1] + key2 = next(iter(dom2.keys())) + val2 = dom2[key2] + var1 = self.changeVarNames(val1,val1.dim) + var2 = self.changeVarNames(val2,val2.dim) + # Cross product of 2D domain and point / interval boundary + if (val1.dim==2) & (val2.dim==0): + if var2[0] == 'z': + if var1 == ['x','y']: + varchange = False + else: + var1=['x','y'] + varchange = True + elif var2[0] == 'x': + if var1 == ['z','y']: + varchange = False + else: + var1=['z','y'] + varchange = True + else: + if var1 == ['x','z']: + varchange = False + else: + var1=['x','z'] + varchange = True + + is_boundary = True + #only single boundary point + if hasattr(val2,'side'): + point1=float(val2.side()) + point2=point1+1 + cond=Eq(Symbol(var2[0]),Rational(point1)) + elif (type(val2).__name__=='Point'): + point1=float(val2.point()) + point2=point1+1 + cond=Eq(Symbol(var2[0]),Rational(point1)) + else: + point1=float(val2.bounding_box()[0]) + point2=float(val2.bounding_box()[1]) + cond=Or(Eq(Symbol(var2[0]),Rational(point1)),Eq(Symbol(var2[0]),Rational(point2))) + if type(val1).__name__ == 'Parallelogram': + orig=self.varChange(tuple(element.item() for element in val1.origin()),varchange) + c1=self.varChange(tuple(element.item() for element in val1.corner_1()),varchange) + c2=self.varChange(tuple(element.item() for element in val1.corner_2()),varchange) + geometry=ParallelogramCylinder(orig+(point1,),c1+(point1,),c2+(point1,),height=point2-point1).axischange(var1[0],var1[1],var2[0]) + elif type(val1).__name__ == 'Circle': + center = self.varChange(tuple(element.item() for element in val1.center()),varchange) + radius = float(val1.radius()) + height = point2-point1 + geometry=getattr(imported_module_3d,"Cylinder")(center+(point1+height/2,),radius,height).axischange(var1[0],var1[1],var2[0]) + elif type(val1).__name__ == 'Triangle': + #only Isosceles triangle with axis of symmetry parallel to y-axis + assert (val1.origin()[1]==val1.corner_1()[1]), "Symmetry axis of triangle has to be y-axis parallel!" + assert (np.linalg.norm(val1.corner_2()-val1.origin()) == np.linalg.norm(val1.corner_2()-val1.corner_1())), "Triangle not Isosceles!" + base = float(val1.corner_1()[0]-val1.origin()[0]) + height = float(np.sqrt(np.linalg.norm(val1.corner_2()-val1.corner_1())**2-(base/2)**2)) + height_prism = float(point2-point1) + center = self.varChange((float(val1.origin()[0])+base/2,float(val1.origin()[1])),varchange) + geometry=getattr(imported_module_3d,"IsoTriangularPrism")(center+(point1+height_prism/2,),base,height,height_prism).axischange(var1[0],var1[1],var2[0]) + else: + assert False, "Type of product domain not supported: "+type(val1).__name__+" * "+type(val2).__name__ + # Cross product of 2D domain and interval + elif (val1.dim==2) & (val2.dim==1): + if var2[0] == 'z': + if var1 == ['x','y']: + varchange = False + else: + var1=['x','y'] + varchange = True + elif var2[0] == 'x': + if var1 == ['z','y']: + varchange = False + else: + var1=['z','y'] + varchange = True + else: + if var1 == ['x','z']: + varchange = False + else: + var1=['x','z'] + varchange = True + + assert (type(val2).__name__ == 'Interval'), "Type of product domain not supported: Should be 2D domain * Interval" + + is_boundary = False + point1=float(val2.bounding_box()[0]) + point2=float(val2.bounding_box()[1]) + + if type(val1).__name__ == 'Parallelogram': + orig= self.varChange(tuple(element.item() for element in val1.origin()),varchange) + c1= self.varChange(tuple(element.item() for element in val1.corner_1()),varchange) + c2= self.varChange(tuple(element.item() for element in val1.corner_2()) ,varchange) + geometry=ParallelogramCylinder(orig+(point1,),c1+(point1,),c2+(point1,),height=point2-point1).axischange(var1[0],var1[1],var2[0]) + elif type(val1).__name__ == 'Circle': + center = self.varChange(tuple(element.item() for element in val1.center()),varchange) + radius = float(val1.radius()) + height = point2-point1 + geometry=getattr(imported_module_3d,"Cylinder")(center+(point1+height/2,),radius,height).axischange(var1[0],var1[1],var2[0]) + elif type(val1).__name__ == 'Triangle': + #only Isosceles triangle with axis of symmetry parallel to y-axis + assert (val1.origin()[1]==val1.corner_1()[1]),"Symmetry axis of triangle has to be y-axis parallel!" + assert (np.linalg.norm(val1.corner_2()-val1.origin()) == np.linalg.norm(val1.corner_2()-val1.corner_1())), "Triangle not Isosceles!" + base = float(val1.corner_1()[0]-val1.origin()[0]) + height = float(np.sqrt(np.linalg.norm(val1.corner_2()-val1.corner_1())**2-(base/2)**2)) + height_prism = point2-point1 + center = self.varChange((float(val1.origin()[0])+base/2,float(val1.origin()[1])),varchange) + geometry=getattr(imported_module_3d,"IsoTriangularPrism")(center+(point1+height_prism/2,),base,height,height_prism).axischange(var1[0],var1[1],var2[0]) + else: + assert False, "Type of product domain not supported: "+type(val1).__name__+" * "+type(val2).__name__ + # Cross product of 1D domain (boundary of 2D domain) and interval + elif (val1.dim==1) & (val2.dim==1): + assert ((type(val2).__name__ == 'Interval')|(type(val1).__name__ == 'Interval')), "Product domain for these types of domain not allowed: Has to be 1D domain * Interval" + is_boundary = True + if type(val2).__name__ != 'Interval': + val1,val2=val2,val1 + var1 = self.changeVarNames(val1,val1.dim) + var2 = self.changeVarNames(val2,val2.dim) + if var2[0] == 'z': + if var1 == ['x','y']: + varchange = False + else: + var1=['x','y'] + varchange = True + elif var2[0] == 'x': + if var1 == ['z','y']: + varchange = False + else: + var1=['z','y'] + varchange = True + else: + if var1 == ['x','z']: + varchange = False + else: + var1=['x','z'] + varchange = True + + point1=float(val2.bounding_box()[0]) + point2=float(val2.bounding_box()[1]) + cond=And(Symbol(var2[0])Rational(point1)) + if type(val1).__name__ == 'ParallelogramBoundary': + orig= self.varChange(tuple(element.item() for element in val1.domain.origin()),varchange) + c1= self.varChange(tuple(element.item() for element in val1.domain.corner_1()),varchange) + c2= self.varChange(tuple(element.item() for element in val1.domain.corner_2()) ,varchange) + geometry=ParallelogramCylinder(orig+(point1,),c1+(point1,),c2+(point1,),height=point2-point1).axischange(var1[0],var1[1],var2[0]) + elif type(val1).__name__ == 'CircleBoundary': + center = self.varChange(tuple(element.item() for element in val1.domain.center()),varchange) + radius = float(val1.domain.radius()) + height = point2-point1 + geometry=getattr(imported_module_3d,"Cylinder")(center+(point1+height/2,),radius,height).axischange(var1[0],var1[1],var2[0]) + elif type(val1).__name__ == 'TriangleBoundary': + #only Isosceles triangle with axis of symmetry parallel to y-axis + assert (val1.domain.origin()[1]==val1.domain.corner_1()[1]), "Symmetry axis of triangle has to be y-axis parallel!" + assert (np.linalg.norm(val1.domain.corner_2()-val1.domain.origin()) == np.linalg.norm(val1.domain.corner_2()-val1.domain.corner_1())), "Triangle not Isosceles!" + base = float(val1.domain.corner_1()[0]-val1.domain.origin()[0]) + height = float(np.sqrt(np.linalg.norm(val1.domain.corner_2()-val1.domain.corner_1())**2-(base/2)**2)) + height_prism = point2-point1 + center = self.varChange((float(val1.domain.origin()[0])+base/2,float(val1.domain.origin()[1])),varchange) + geometry=getattr(imported_module_3d,"IsoTriangularPrism")((center+(point1+height_prism/2,),base,height,height_prism).axischange(var1[0],var1[1],var2[0])) + elif type(val1).__name__ == 'Interval': + #var1 = list(val1.space.keys()) + #var2 = list(val2.space.keys()) + var1 = self.changeVarNames(val1,val1.dim) + var2 = self.changeVarNames(val2,val2.dim) + assert (((var1[0]=='x')&(var2[0]=='y'))|((var1[0]=='y')&(var2[0]=='x'))), "Only x,y as coordinates for 2D problems allowed" + + point1=float(val1.bounding_box()[0]) + point2=float(val1.bounding_box()[1]) + point3=float(val2.bounding_box()[0]) + point4=float(val2.bounding_box()[1]) + geometry=getattr(imported_module_2d,"Rectangle")((point1,point3),(point2,point4)).axischange(var1[0],var2[0],[]) + cond = None + is_boundary=False + else: + assert False, "Type of product domain not supported: "+type(val1).__name__+" * "+type(val2).__name__ + + # Cross product of 1D domain (boundary of 2D domain or Interval) and point/boundary of interval + elif (val1.dim==1) & (val2.dim==0): + is_boundary = True + if var2[0] == 'z': + if var1 == ['x','y']: + varchange = False + else: + var1=['x','y'] + varchange = True + elif var2[0] == 'x': + if var1 == ['z','y']: + varchange = False + else: + var1=['z','y'] + varchange = True + else: + if var1 == ['x','z']: + varchange = False + else: + var1=['x','z'] + varchange = True + + + if hasattr(val2,'side'): + point1=float(val2.side()) + point2=point1+1 + cond=Eq(Symbol(var2[0]),Rational(point1)) + elif (type(val2).__name__=='Point'): + point1=float(val2.point()) + point2=point1+1 + cond=Eq(Symbol(var2[0]),Rational(point1)) + else: + point1=float(val2.bounding_box()[0]) + point2=float(val2.bounding_box()[1]) + cond=Or(Eq(Symbol(var2[0]),Rational(point1)),Eq(Symbol(var2[0]),Rational(point2))) + + if type(val1).__name__ == 'ParallelogramBoundary': + orig=self.varChange(tuple(element.item() for element in val1.domain.origin()),varchange) + c1=self.varChange(tuple(element.item() for element in val1.domain.corner_1()),varchange) + c2=self.varChange(tuple(element.item() for element in val1.domain.corner_2()) ,varchange) + geometry=ParallelogramCylinder(orig+(point1,),c1+(point1,),c2+(point1,),height=point2-point1).axischange(var1[0],var1[1],var2[0]) + elif type(val1).__name__ == 'CircleBoundary': + center = self.varChange(tuple(element.item() for element in val1.domain.center()),varchange) + radius = float(val1.domain.radius()) + height = point2-point1 + geometry=getattr(imported_module_3d,"Cylinder")(center+(point1+height/2,),radius,height).axischange(var1[0],var1[1],var2[0]) + elif type(val1).__name__ == 'TriangleBoundary': + #only Isosceles triangle with axis of symmetry parallel to y-axis + assert (val1.domain.origin()[1]==val1.domain.corner_1()[1]),"Symmetry axis of triangle has to be y-axis parallel!" + assert (np.linalg.norm(val1.domain.corner_2()-val1.domain.origin()) == np.linalg.norm(val1.domain.corner_2()-val1.domain.corner_1())), "Triangle not Isosceles!" + base = float(val1.domain.corner_1()[0]-val1.domain.origin()[0]) + height = float(np.sqrt(np.linalg.norm(val1.domain.corner_2()-val1.domain.corner_1())**2-(base/2)**2)) + height_prism = point2-point1 + center = self.varChange((float(val1.domain.origin()[0])+base/2,float(val1.domain.origin()[1])),varchange) + geometry=getattr(imported_module_3d,"IsoTriangularPrism")(center+(point1+height_prism/2,),base,height,height_prism).axischange(var1[0],var1[1],var2[0]) + elif type(val1).__name__ == 'Interval': + var1 = list(val1.space.keys()) + var2 = list(val2.space.keys()) + assert ((var1[0]!='z')&(var2[0]!='z')), "Only x,y as coordinates for 2D problems allowed" + point3=float(val1.bounding_box()[0]) + point4=float(val1.bounding_box()[1]) + geometry=getattr(imported_module_2d,"Rectangle")((point3,point1),(point4,point2)).axischange(var1[0],var2[0],[]) + else: + assert False, "Type of product domain not supported: "+type(val1).__name__+" * "+type(val2).__name__ + # Cross product of two 0D domains not allowed + elif (val1.dim==0) & (val2.dim==0): + assert(False), "2D or 3D points are not allowed for sampling due to zero surface" + else: + is_boundary, geometry, cond, translate_vec, rotation_list = self.RecursiveDomainDictGeoWrapper(dom2,dom1,translate_vec=translate_vec,rotation_list=rotation_list) + else: + is_boundary, geometry, cond, translate_vec, rotation_list = self.RecursiveDomainDictGeoWrapper(dom1,dom2,translate_vec=translate_vec,rotation_list=rotation_list) + else: # dom3 != None + key1 = next(iter(dom1.keys())) + val1 = dom1[key1] + key2 = next(iter(dom2.keys())) + val2 = dom2[key2] + key3 = next(iter(dom3.keys())) + val3 = dom3[key3] + + # fully decomposed product domain + if (key1 == 'd')& (key2 == 'd')&(key3=='d'): + # I1xI2xI3 I1xI2xP/IBoundary + # Edges and 3D points are not allowed + # sort by (x,y,z)-order + vals= sorted((val1,val2,val3), key=lambda x: list(x.space.variables)[0]) + assert (np.sum([vals[0].dim,vals[1].dim,vals[2].dim])>1), "3D points or edges are not allowed due to zero surface in 3D" + vars = ['x','y','z'] + points=[] + conds=[] + is_boundary = False + for index, val in enumerate(vals): + if hasattr(val,'side'): + points.append([float(val.side()),float(val.side())+1]) + conds.append(Eq(Symbol(vars[index]),Rational(float(val.side())))) + is_boundary = True + elif (type(val).__name__=='Point'): + points.append([float(val.point()),float(val.point())+1]) + conds.append(Eq(Symbol(vars[index]),Rational(float(val.point())))) + is_boundary = True + elif (type(val).__name__=='Interval'): + points.append([float(val.bounding_box()[0]),float(val.bounding_box()[1])]) + conds.append(True) + else: # IntervalBoundary with both sides + points.append([float(val.bounding_box()[0]),float(val.bounding_box()[1])]) + conds.append(Or(Eq(Symbol(vars[index]),Rational(float(val.bounding_box()[0]))),Eq(Symbol(vars[index]),Rational(float(val.bounding_box()[1]))))) + is_boundary=True + geometry=getattr(imported_module_3d,"Box")((points[0][0],points[1][0],points[2][0]),(points[0][1],points[1][1],points[2][1])) + cond_all = And(conds[0],conds[1],conds[2]) + cond = None if cond_all == True else cond_all + # further decomposition of product domain, but only union or union boundary of 1D or 0D domains allowed, that means no further rotation and translation will be done by call of ProductDomainWrapper + else: + #sort domains: domains with key other than 'd' first + doms,keys = zip(*sorted(list(zip([dom1,dom2,dom3],[key1,key2,key3])), key=lambda x: x[1] == 'd')) + #only union or union boundary of 1D or 0D domains allowed (key='u' or 'ub') + assert((keys[0]=='u')|(keys[0]=='ub')), "Other domain operations than Union or UnionBoundary not allowed" + + is_boundary1, geometry1, cond1, translate_vec, rotation_list = self.ProductDomainWrapper(doms[0][keys[0]][0],doms[1],doms[2],translate_vec, rotation_list) + is_boundary2, geometry2, cond2, translate_vec, rotation_list = self.ProductDomainWrapper(doms[0][keys[0]][1],doms[1],doms[2],translate_vec, rotation_list) + #sampler (interior or boundary) has to be the same for both domains, union of interior and boundary is not allowed + assert is_boundary1==is_boundary2 + + if cond1==cond2: + if cond1!=None: + cond = cond1 + else: + cond =None + + elif cond1==None: + cond = cond2 + elif cond2==None: + cond=cond1 + else: + cond = self._or_condition(self._geo_condition(geometry1.sdf,cond1,is_boundary1),self._geo_condition(geometry2.sdf,cond2,is_boundary2)) + is_boundary = is_boundary1 + geometry = geometry1+geometry2 + + + # check if rotation and translation have to be done + rot_angles, rot_axes, rot_points, rot_prio = rotation_list + if rot_angles != []: + if translate_vec != [0,0,0]: + if rot_prio == 0: + for angle, axis, point in zip(rot_angles,rot_axes,rot_points): + geometry = geometry.rotate(angle,axis,point) + geometry = geometry.translate(translate_vec) + cond = self.translate_condition(cond,translate_vec) + else: + geometry = geometry.translate(translate_vec) + cond = self.translate_condition(cond,translate_vec) + for angle, axis, point in zip(rot_angles,rot_axes,rot_points): + geometry = geometry.rotate(angle,axis,point) + rotation_list = [[],[],[],0] + translate_vec = [0,0,0] + else: + for angle, axis, point in zip(rot_angles,rot_axes,rot_points): + geometry = geometry.rotate(angle,axis,point) + rotation_list = [[],[],[],0] + else: + if translate_vec != [0,0,0]: + geometry = geometry.translate(translate_vec) + cond = self.translate_condition(cond,translate_vec) + translate_vec = [0,0,0] + + return is_boundary, geometry, cond, translate_vec, rotation_list + + + + def varChange(self,val,ischange): + """ + Convert 2D numpy array to tuple of floats and change the order + of the values if ischange is True. + + Parameters + ---------- + val: numpy array + array with 2 values + ischange: bool + change the order of the values + + Returns + ------- + tuple + tuple of floats + """ + if ischange: + return (float(val[1]), float(val[0])) + else: + return (float(val[0]), float(val[1])) + + def GeometryNameMapper(self,domain,translate_vec=[0,0,0],rotation_list=[[],[],[],0]): + """ + Mapping of single TorchPhysics (decomposed) domain to Modulus + geometry + + Parameters + ---------- + domain: domain object + domain object from TorchPhysics + + Returns + ------- + geometry object + Modulus geometry object + is_boundary: bool + True if domain is boundary + cond: sympy expression + condition to restrict on parts of geometry + """ + + if 'Boundary' in type(domain).__name__: + dim = domain.dim+1 + is_boundary = True + else: + dim = domain.dim + is_boundary = False + cond = None + # if variable x with higher dimension than 1, change to x,y (,z) + vars = self.changeVarNames(domain,dim) + if dim ==1: + imported_module = importlib.import_module("modulus.sym.geometry.primitives_1d") + if type(domain).__name__ == 'Interval': # IntervalBoundary + geometry = getattr(imported_module,"Line1D")(float(domain.lower_bound()),float(domain.upper_bound())) + elif type(domain).__name__ == 'IntervalBoundary': + geometry = getattr(imported_module,"Line1D")(float(domain.domain.lower_bound()),float(domain.domain.upper_bound())) + elif type(domain).__name__ == 'IntervalSingleBoundaryPoint': + geometry = getattr(imported_module,"Line1D")(float(domain.domain.lower_bound()),float(domain.domain.upper_bound())) + cond = Eq(Symbol('x'),Rational(float(domain.side()))) + else: + assert False, "Domain type not supported" + elif dim==2: + if vars == ['x','y']: + varchange = False + y_index =1 + else: + varchange = True + y_index = 0 + imported_module = importlib.import_module("modulus.sym.geometry.primitives_2d") + if (type(domain).__name__ == 'Parallelogram'): + origin = self.varChange(domain.origin(),varchange) + corner1 = self.varChange(domain.corner_1(),varchange) + corner2 = self.varChange(domain.corner_2(),varchange) + geometry = getattr(imported_module,"Polygon")([origin,corner1, np.subtract(np.add(corner1,corner2),origin),corner2]) + elif (type(domain).__name__ == 'ParallelogramBoundary'): + origin = self.varChange(domain.domain.origin(),varchange) + corner1 = self.varChange(domain.domain.corner_1(),varchange) + corner2 = self.varChange(domain.domain.corner_2(),varchange) + geometry = getattr(imported_module,"Polygon")([origin,corner1, np.subtract(np.add(corner1,corner2),origin),corner2]) + elif (type(domain).__name__ == 'Circle'): + center = self.varChange(domain.center(),varchange) + geometry = getattr(imported_module,"Circle")(center,float(domain.radius())) + elif (type(domain).__name__ == 'CircleBoundary'): + center = self.varChange(domain.domain.center(),varchange) + geometry = getattr(imported_module,"Circle")(center,float(domain.domain.radius())) + elif (type(domain).__name__ == 'ShapelyPolygon'): + geometry = getattr(imported_module,"Polygon")(list(zip(*domain.polygon.exterior.coords.xy))[:-1]) + elif (type(domain).__name__ == 'ShapelyBoundary'): + geometry = getattr(imported_module,"Polygon")(list(zip(*domain.domain.polygon.exterior.coords.xy))[:-1]) + elif (type(domain).__name__ == 'Triangle'): + #only Isosceles triangle with axis of symmetry parallel to y-axis + assert (domain.origin()[y_index]==domain.corner_1()[y_index]),"Symmetry axis of triangle has to be y-axis parallel!" + assert (np.linalg.norm(domain.corner_2()-domain.origin()) == np.linalg.norm(domain.corner_2()-domain.corner_1())), "Triangle not Isosceles!" + origin = self.varChange(domain.origin(),varchange) + corner1 = self.varChange(domain.corner_1(),varchange) + corner2 = self.varChange(domain.corner_2(),varchange) + base = float(corner1[0]-origin[0]) + height = float(np.sqrt(np.linalg.norm(np.subtract(corner2,corner1))**2-(base/2)**2)) + center = (float(origin[0])+base/2,float(origin[1])) + geometry=getattr(imported_module,"Triangle")(center,base,height) + elif (type(domain).__name__ == 'TriangleBoundary'): + #only Isosceles triangle with axis of symmetry parallel to y-axis + assert (domain.domain.origin()[y_index]==domain.domain.corner_1()[y_index]), "Symmetry axis of triangle has to be y-axis parallel!" + assert (np.linalg.norm(domain.domain.corner_2()-domain.domain.origin()) == np.linalg.norm(domain.domain.corner_2()-domain.domain.corner_1())), "Triangle not Isosceles!" + origin = self.varChange(domain.domain.origin(),varchange) + corner1 = self.varChange(domain.domain.corner_1(),varchange) + corner2 = self.varChange(domain.domain.corner_2(),varchange) + base = float(corner1[0]-origin[0]) + height = float(np.sqrt(np.linalg.norm(np.subtract(corner2,corner1))**2-(base/2)**2)) + center = (float(origin[0])+base/2,float(origin[1])) + geometry=getattr(imported_module,"Triangle")(center,base,height) + else: + assert (True), "Domain type not supported" + else: #dim==3 + imported_module = importlib.import_module("modulus.sym.geometry.primitives_3d") + if type(domain).__name__ == 'Sphere': + _, sorted_center = zip(*(sorted(zip(vars,domain.center())))) + sorted_center = tuple([float(elem) for elem in sorted_center]) + geometry = getattr(imported_module,"Sphere")(sorted_center,float(domain.radius())) + elif type(domain).__name__ == 'SphereBoundary': + _, sorted_center = zip(*(sorted(zip(vars,domain.domain.center())))) + sorted_center = tuple([float(elem) for elem in sorted_center]) + geometry = getattr(imported_module,"Sphere")(sorted_center,float(domain.domain.radius())) + elif type(domain).__name__ == 'TrimeshPolyhedron': + try: + geometry = getattr(importlib.import_module("modulus.sym.geometry.tessellation"),"Tessellation")(domain.mesh) + except: + raise Exception("Tessellation module only supported for Modulus docker installation due to missing pysdf installation!") + elif type(domain).__name__ == 'TrimeshBoundary': + try: + geometry = getattr(importlib.import_module("modulus.sym.geometry.tessellation"),"Tessellation")(domain.domain.mesh) + except: + raise Exception("Tessellation module only supported for Modulus docker installation due to missing pysdf installation!") + else: + assert (True), "Domain type not supported" + + # check if rotation and translation have to be done + rot_angles, rot_axes, rot_points, rot_prio = rotation_list + if rot_angles != []: + if translate_vec != [0,0,0]: + if rot_prio == 0: + for angle, axis, point in zip(rot_angles,rot_axes,rot_points): + geometry = geometry.rotate(angle,axis,point) + geometry = geometry.translate(translate_vec) + cond = self.translate_condition(cond,translate_vec) + else: + geometry = geometry.translate(translate_vec) + cond = self.translate_condition(cond,translate_vec) + for angle, axis, point in zip(rot_angles,rot_axes,rot_points): + geometry = geometry.rotate(angle,axis,point) + rotation_list = [[],[],[],0] + translate_vec = [0,0,0] + else: + for angle, axis, point in zip(rot_angles,rot_axes,rot_points): + geometry = geometry.rotate(angle,axis,point) + rotation_list = [[],[],[],0] + else: + if translate_vec != [0,0,0]: + geometry = geometry.translate(translate_vec) + cond = self.translate_condition(cond,translate_vec) + translate_vec = [0,0,0] + + return is_boundary, geometry, cond, translate_vec, rotation_list + + + def translate_condition(self,cond,translate_vec): + """ + Translate condition by vector + + Parameters + ---------- + cond: sympy expression + condition to translate + translate_vec: tuple of floats + translation vector + + Returns + ------- + sympy expression + translated condition + """ + if cond is not None: + return cond.subs(Symbol('x'),Symbol('x')-Rational(translate_vec[0])).subs(Symbol('y'),Symbol('y')-Rational(translate_vec[1])).subs(Symbol('z'),Symbol('z')-Rational(translate_vec[2])) + else: + return None + + def compute_3D_translate_vec(self,space,translate_vec): + """ + Compute 3D translation vector in the order "x","y","z" out of + 1D, 2D or 3D translation vector + + Parameters + ---------- + space: dict + translate_vec: tuple of floats + translation vector + + Returns + ------- + tuple of floats + translated geometry + """ + vec= [0,0,0] + if 'x' in space: + if space['x'] ==2: + vec= [translate_vec[0],translate_vec[1],0] + elif space['x'] ==3: + vec= translate_vec + else: + for ind,key in enumerate(space.keys()): + if key =='x': + vec[0]=translate_vec[ind] + elif key =='y': + vec[1]=translate_vec[ind] + elif key =='z': + vec[2]=translate_vec[ind] + else: + for ind,key in enumerate(space.keys()): + if key =='y': + vec[1]=translate_vec[ind] + elif key =='z': + vec[2]=translate_vec[ind] + + return vec + + def compute_3D_rotation(self,space,point): + """ + Compute 3D rotation point in the order "x","y","z" out of 2D + rotation point and also rotation axis + + Parameters + ---------- + space: dict + space of domain (variables "x","y" or "x","z" or "y","z" or + "x" (2D)) + point: tuple of floats (2D) + + Returns + ------- + rot_ax: str + rotation axis + rot_point: tuple of floats + rotation point + """ + rot_point = [0.0,0.0,0.0] + if 'x' in space: + if space['x'] ==2: + rot_point= [point[0],point[1],0.0] + rot_ax = 'z' + + else: + for ind,key in enumerate(space.keys()): + if key =='x': + rot_point[0]=point[ind] + elif key =='y': + rot_point[1]=point[ind] + rot_point[2]=0.0 + rot_ax = 'z' + elif key =='z': + rot_point[2]=point[ind] + rot_point[1]=0.0 + rot_ax = 'y' + else: + for ind,key in enumerate(space.keys()): + if key =='y': + rot_point[1]=point[ind] + elif key =='z': + rot_point[2]=point[ind] + rot_point[0]=0.0 + rot_ax = 'x' + + return rot_ax, rot_point + +class ParallelogramCylinder(Geometry): + """ + 3D Cylinder with Parallelogram base area perpendicular to z-axis + + Parameters + ---------- + origin, corner1,corner2 : tuples with 3 ints or floats + Three corners of the parallelogram, in the following order + + | corner_2 -------- x + | / / + | / / + | origin ----- corner_1 + height: z-coordinate of upper plane of parallelogram + parameterization : Parameterization + Parameterization of geometry. + + + """ + + def __init__(self, origin,corner1,corner2,height, parameterization=Parameterization()): + assert ((origin[2] == corner1[2]) & (origin[2] == corner2[2])), "Points must have same coordinate on normal dim:z" + + # make sympy symbols to use + x, y, z = Symbol("x"), Symbol("y"), Symbol("z") + + s1, s2 = Symbol(csg_curve_naming(0)), Symbol(csg_curve_naming(1)) + + + # surface + curve_parameterization = Parameterization({s1: (0, 1),s2: (0,1)}) + curve_parameterization = Parameterization.combine(curve_parameterization, parameterization) + + # area + N = CoordSys3D("N") + vec1 = tuple(np.subtract(corner1,origin)) + vec2 = tuple(np.subtract(corner2,origin)) + vvec1 = vec1[0]*N.i + vec1[1]*N.j + vec1[2]*N.k + vvec2 = vec2[0]*N.i + vec2[1]*N.j + vec2[2]*N.k + + corner3 = tuple(np.subtract(np.add(corner1,corner2),origin)) + + #compute cross product of the "base"-vectors for computing area and testing of right-handed system + cross_vec = vvec1.cross(vvec2) + sgn_normal = sign(cross_vec.dot(N.k)) + + area_p= sqrt(cross_vec.dot(cross_vec)) + + + bottom = SympyCurve( + functions={ + "x": origin[0]+ s1*vec1[0]+s2*vec2[0], + "y": origin[1]+ s1*vec1[1]+s2*vec2[1], + "z": origin[2], + "normal_x": 0, + "normal_y": 0, + "normal_z": -1, + }, + parameterization=curve_parameterization, + area=area_p, + ) + top = SympyCurve( + functions={ + "x": origin[0]+ s1*vec1[0]+s2*vec2[0], + "y": origin[1]+ s1*vec1[1]+s2*vec2[1], + "z": origin[2]+height, + "normal_x": 0, + "normal_y": 0, + "normal_z": 1, + }, + parameterization=curve_parameterization, + area=area_p, + ) + norm_l=np.linalg.norm([-vec2[1],vec2[0]]) + side1 = SympyCurve( + functions={ + "x": origin[0]+ s1*vec2[0], + "y": origin[1]+ s1*vec2[1], + "z": origin[2]+ s2*height, + "normal_x": -vec2[1]/norm_l*sgn_normal, + "normal_y": vec2[0]/norm_l*sgn_normal, + "normal_z": 0, + }, + parameterization=curve_parameterization, + area=np.linalg.norm(vec2)*height, + ) + norm_l=np.linalg.norm([vec1[1],-vec1[0]]) + side2 = SympyCurve( + functions={ + "x": origin[0]+ s1*vec1[0], + "y": origin[1]+ s1*vec1[1], + "z": origin[2]+ s2*height, + "normal_x": vec1[1]/norm_l*sgn_normal, + "normal_y": -vec1[0]/norm_l*sgn_normal, + "normal_z": 0, + }, + parameterization=curve_parameterization, + area=np.linalg.norm(vec1)*height, + ) + + norm_l=np.linalg.norm([-vec2[1],vec2[0]]) + side3 = SympyCurve( + functions={ + "x": origin[0]+ vec1[0]+s1*vec2[0], + "y": origin[1]+ vec1[1]+s1*vec2[1], + "z": origin[2]+ s2*height, + "normal_x": vec2[1]/norm_l*sgn_normal, + "normal_y": -vec2[0]/norm_l*sgn_normal, + "normal_z": 0, + }, + parameterization=curve_parameterization, + area=np.linalg.norm(vec2)*height, + ) + norm_l=np.linalg.norm([vec1[1],-vec1[0]]) + side4 = SympyCurve( + functions={ + "x": origin[0]+ s1*vec1[0]+vec2[0], + "y": origin[1]+ s1*vec1[1]+vec2[1], + "z": origin[2]+ s2*height, + "normal_x": -vec1[1]/norm_l*sgn_normal, + "normal_y": vec1[0]/norm_l*sgn_normal, + "normal_z": 0, + }, + parameterization=curve_parameterization, + area=np.linalg.norm(vec1)*height, + ) + + sides=[top,bottom,side1,side2,side3,side4] + + # compute SDF in 2D Parallelogram + points = [origin[0:2],corner1[0:2],corner3[0:2] ,corner2[0:2]] + # wrap points + wrapted_points = points + [points[0]] + + sdfs = [(x - wrapted_points[0][0]) ** 2 + (y - wrapted_points[0][1]) ** 2] + conds = [] + for v1, v2 in zip(wrapted_points[:-1], wrapted_points[1:]): + # sdf calculation + dx = v1[0] - v2[0] + dy = v1[1] - v2[1] + px = x - v2[0] + py = y - v2[1] + d_dot_d = dx**2 + dy**2 + p_dot_d = px * dx + py * dy + max_min = Max(Min(p_dot_d / d_dot_d, 1.0), 0.0) + vx = px - dx * max_min + vy = py - dy * max_min + sdf = vx**2 + vy**2 + sdfs.append(sdf) + + # winding calculation + cond_1 = Heaviside(y - v2[1]) + cond_2 = Heaviside(v1[1] - y) + cond_3 = Heaviside((dx * py) - (dy * px)) + all_cond = cond_1 * cond_2 * cond_3 + none_cond = (1.0 - cond_1) * (1.0 - cond_2) * (1.0 - cond_3) + cond = 1.0 - 2.0 * Min(all_cond + none_cond, 1.0) + conds.append(cond) + + # set inside outside + sdf_xy = Min(*sdfs) + cond = reduce(mul, conds) + sdf_xy = sqrt(sdf_xy) * -cond + + + #compute distance in z-direction in 3D + center_z = origin[2] + height / 2 + sdf_z = height / 2 - Abs(z - center_z) + + if (sdf_xy >=0) & (sdf_z >= 0): + sdf= Min(sdf_xy,sdf_z) + elif (sdf_xy <0)&(sdf_z>0): + sdf = sdf_xy + elif (sdf_xy>0)&sdf_z<0: + sdf = sdf_z + else: #min distance to all 12 boundary curves + sdf = -sqrt(sdf_xy**2+sdf_z**2) + + + # calculate bounds + max_x = max(origin[0],corner1[0],corner2[0],corner3[0]) + min_x = min(origin[0],corner1[0],corner2[0],corner3[0]) + max_y = max(origin[1],corner1[1],corner2[1],corner3[1]) + min_y = min(origin[1],corner1[1],corner2[1],corner3[1]) + + bounds = Bounds( + { + Parameter("x"): (min_x, max_x), + Parameter("y"): (min_y, max_y), + Parameter("z"): (float(origin[2]), float(origin[2]+height)), + }, + parameterization=parameterization, + ) + + # initialize ParallelogramCylinder + super().__init__( + sides, + _sympy_sdf_to_sdf(sdf), + dims=3, + bounds=bounds, + parameterization=parameterization, + ) diff --git a/src/torchphysics/wrapper/helper.py b/src/torchphysics/wrapper/helper.py new file mode 100644 index 00000000..52271167 --- /dev/null +++ b/src/torchphysics/wrapper/helper.py @@ -0,0 +1,653 @@ +# This file includes work covered by the following copyright and permission notices: +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# All rights reserved. +# Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import numpy as np + +from torchphysics.problem.spaces.points import Points +from torchphysics.utils.plotting.plot_functions import plot +from modulus.sym.loss import Loss +from modulus.sym.utils.io import InferencerPlotter + +from modulus.sym.domain.validator import Validator +from modulus.sym.node import Node + +from modulus.sym.dataset import DictInferencePointwiseDataset, DictPointwiseDataset + +from modulus.sym.domain.constraint import Constraint +from modulus.sym.graph import Graph +from modulus.sym.key import Key +from modulus.sym.node import Node +from modulus.sym.constants import TF_SUMMARY +from modulus.sym.distributed import DistributedManager +from modulus.sym.utils.io.vtk import var_to_polyvtk + +from typing import List, Dict +from typing import Dict +from torch import Tensor + +def convertDataTP2Modulus(points): + """ + Convert data from TorchPhysics to Modulus format + + Parameters + ---------- + + data: Points (torchphysics.problem.space.Points) + Dictionary containing the data points of the TorchPhysics + dataset with the TorchPhysics variable names as keys and the + corresponding data points as values. + + """ + data=points.coordinates + + outdata={} + + for var in points.space.variables: + if var =='x': + outdata['x'] = data[var][:,0].unsqueeze(1) + if points.space[var] > 1: + outdata['y'] = data[var][:,1].unsqueeze(1) + if points.space[var]== 3: + outdata['z'] = data[var][:,2].unsqueeze(1) + + else: + if points.space[var]>1: + for ind in range(points.space[var]): + outdata[var+str(ind+1)]=data[var][:,ind].unsqueeze(1) + else: + outdata[var]=data[var] + + return outdata + + +def convertDataModulus2TP(data,TP_space): + """ + Convert data from Modulus to TorchPhysics format. If a key of + TP_space is not present in data, it will be ignored. + + Parameters + ---------- + + data: dict[str,torch.Tensor] + Dictionary containing the data points of the Modulus dataset + with the Modulus variable names as keys and the corresponding + data points as values + + """ + outdata={} + + + for key in TP_space.variables: + if TP_space[key] > 1: + if key !='x': + if all([key+str(l+1) in data.keys() for l in range(TP_space[key])]): + cat_var = list(data[key+str(l+1)] for l in range(TP_space[key])) + outdata[key] = torch.cat(cat_var,dim=1) + else: + if TP_space[key] ==2: + if ('x' in data.keys()) and ('y' in data.keys()): + outdata['x']=torch.cat((data['x'],data['y']),dim=1) + else: + if ('x' in data.keys()) and ('y' in data.keys()) and ('z' in data.keys()): + outdata['x']=torch.cat((data['x'],data['y'],data['z']),dim=1) + + else: + if key in data.keys(): + outdata[key]=data[key] + + + return outdata + + +class PointwiseLossInfNorm(Loss): + """ + L-inf loss function for pointwise data + Computes the maximum norm loss of each output tensor + """ + + def __init__(self): + super().__init__() + + + @staticmethod + def _loss( + invar: Dict[str, Tensor], + pred_outvar: Dict[str, Tensor], + true_outvar: Dict[str, Tensor], + lambda_weighting: Dict[str, Tensor], + step: int, + ) -> Dict[str, Tensor]: + losses = {} + for key, value in pred_outvar.items(): + l = lambda_weighting[key] * torch.abs( + pred_outvar[key] - true_outvar[key] + ) + if "area" in invar.keys(): + l *= invar["area"] + losses[key] = torch.max(l) + return losses + + def forward( + self, + invar: Dict[str, Tensor], + pred_outvar: Dict[str, Tensor], + true_outvar: Dict[str, Tensor], + lambda_weighting: Dict[str, Tensor], + step: int, + ) -> Dict[str, Tensor]: + return PointwiseLossInfNorm._loss( + invar, pred_outvar, true_outvar, lambda_weighting, step + ) + +class PointwiseLossMean(Loss): + """ + Computes the mean of loss function values + + """ + + def __init__(self): + super().__init__() + + + @staticmethod + def _loss( + invar: Dict[str, Tensor], + pred_outvar: Dict[str, Tensor], + true_outvar: Dict[str, Tensor], + lambda_weighting: Dict[str, Tensor], + step: int, + ) -> Dict[str, Tensor]: + losses = {} + for key, value in pred_outvar.items(): + losses[key]= torch.mean(lambda_weighting[key] * torch.abs(pred_outvar[key] - true_outvar[key])) + return losses + + def forward( + self, + invar: Dict[str, Tensor], + pred_outvar: Dict[str, Tensor], + true_outvar: Dict[str, Tensor], + lambda_weighting: Dict[str, Tensor], + step: int, + ) -> Dict[str, Tensor]: + return PointwiseLossMean._loss( + invar, pred_outvar, true_outvar, lambda_weighting, step + ) + + + + +def OptimizerNameMapper(tp_name): + """ + Maps the optimizer name to intern Modulus names. If the optimizer + is not defined, it returns 'not defined'. + """ + if tp_name == 'Adam': + return 'adam' + elif tp_name == 'LBFGS': + return 'bfgs' + elif tp_name == 'SGD': + return 'sgd' + elif tp_name == 'Adahessian': + return 'adahessian' + elif tp_name == 'Adadelta': + return 'adadelta' + elif tp_name == 'Adagrad': + return 'adagrad' + elif tp_name == 'AdamW': + return 'adamw' + elif tp_name == 'SparseAdam': + return 'sparse_adam' + elif tp_name == 'Adamax': + return 'adamax' + elif tp_name == 'ASGD': + return 'asgd' + elif tp_name == 'NAdam': + return 'Nadam' + elif tp_name == 'RAdam': + return 'Radam' + elif tp_name == 'RMSprop': + return 'rmsprop' + elif tp_name == 'Rprop': + return 'rprop' + elif tp_name == 'A2GradExp': + return 'a2grad_exp' + elif tp_name == 'A2GradInc': + return 'a2grad_inc' + elif tp_name == 'A2GradUni': + return 'a2grad_uni' + elif tp_name == 'AccSGD': + return 'accsgd' + elif tp_name == 'AdaBelief': + return 'adabelief' + elif tp_name == 'AdaBound': + return 'adabound' + elif tp_name == 'AdaMod': + return 'adamod' + elif tp_name == 'AdaFactor': + return 'adafactor' + elif tp_name == 'AdamP': + return 'adamp' + elif tp_name == 'AggMo': + return 'aggmo' + elif tp_name == 'Apollo': + return 'apollo' + elif tp_name == 'DiffGrad': + return 'diffgrad' + elif tp_name == 'Lamb': + return 'lamb' + elif tp_name == 'NovoGrad': + return 'novograd' + elif tp_name == 'PID': + return 'pid' + elif tp_name == 'QHAdam': + return 'qhadam' + elif tp_name == 'MADGRAD': + return 'madgrad' + elif tp_name == 'QHM': + return 'qhm' + elif tp_name == 'Ranger': + return 'ranger' + elif tp_name == 'RangerQH': + return 'ranger_qh' + elif tp_name == 'RangerVA': + return 'ranger_va' + elif tp_name == 'SGDW': + return 'sgdw' + elif tp_name == 'SGDP': + return 'sgdp' + elif tp_name == 'SWATS': + return 'swats' + elif tp_name == 'Shampoo': + return 'shampoo' + elif tp_name == 'Yogi': + return 'yogi' + else: + return 'not defined' + +def SchedulerNameMapper(tp_name): + """ + Maps the scheduler name to intern Modulus names. If the scheduler + is not defined, it returns 'not defined'. + """ + + if tp_name == 'ExponentialLR': + return 'exponential_lr' + elif tp_name == 'TFExponentialLR': + return 'tf_exponential_lr' + elif tp_name == 'CosineAnnealingLR': + return 'cosine_annealing' + elif tp_name == 'CosineAnnealingWarmRestarts': + return 'cosine_annealing_warm_restarts' + else: + return 'not defined' + +def AggregatorNameMapper(tp_name): + """ + Maps the aggregator name to intern Modulus names. If the aggregator + is not defined, it returns 'not defined'. + """ + if tp_name == 'Sum' : + return 'sum' + elif tp_name == 'GradNorm': + return 'grad_norm' + elif tp_name == 'ResNorm': + return 'res_norm' + elif tp_name == 'Homoscedastic': + return 'homoscedastic' + elif tp_name == 'LRAnnealing': + return 'lr_annealing' + elif tp_name == 'SoftAdapt': + return 'soft_adapt' + elif tp_name == 'Relobralo': + return 'relobralo' + else: + return 'not defined' + +class CustomInferencerPlotter(InferencerPlotter): + """ + Custom inferencer plotter class for using TorchPhysics plot + callbacks + """ + def __init__(self,callback): + self.plot_function = callback.plot_function + self.plot_type = callback.plot_type + self.angle = callback.angle + self.point_sampler = callback.point_sampler + self.kwargs = callback.kwargs + self.model = callback.model + + def __call__(self, invar, outvar): + fig = plot(model=self.model, plot_function=self.plot_function, + point_sampler=self.point_sampler, + angle=self.angle, plot_type=self.plot_type, + device=next(self.model.parameters()).device, **self.kwargs) + + return [(fig,'')] + + + + +class PINNConditionValidator(Validator): + """ + Pointwise Validator that allows validating output variables of the + Graph on pointwise data. + + Parameters + ---------- + nodes : List[Node] + List of Modulus Nodes to unroll graph with. + invar : Dict[str, np.ndarray (N, 1)] + Dictionary of numpy arrays as input. + output_names : List[str] + List of desired outputs. + batch_size : int, optional + Batch size used when running validation, by default 1024 + requires_grad : bool = False + If automatic differentiation is needed for computing results. + """ + + def __init__( + self, + nodes: List[Node], + invar: Dict[str, np.array], + output_names: List[str], + batch_size: int = 1024, + requires_grad: bool = False, + ): + + # get dataset and dataloader + self.dataset = DictInferencePointwiseDataset( + invar=invar, output_names=output_names + ) + self.dataloader = Constraint.get_dataloader( + dataset=self.dataset, + batch_size=batch_size, + shuffle=False, + drop_last=False, + num_workers=0, + distributed=False, + infinite=False, + ) + + + # construct model from nodes + self.model = Graph( + nodes, + Key.convert_list(self.dataset.invar_keys), + Key.convert_list(self.dataset.outvar_keys), + ) + + self.manager = DistributedManager() + self.device = self.manager.device + self.model.to(self.device) + + # set forward method + self.requires_grad = requires_grad + self.forward = self.forward_grad if requires_grad else self.forward_nograd + + # set plotter + self.plotter = None + + + + def save_results(self, name, results_dir, writer, save_filetypes, step): + + invar_cpu = {key: [] for key in self.dataset.invar_keys} + #true_outvar_cpu = {key: [] for key in self.dataset.outvar_keys} + pred_outvar_cpu = {key: [] for key in self.dataset.outvar_keys} + # Loop through mini-batches + for i, (invar0,) in enumerate(self.dataloader): + # Move data to device (may need gradients in future, if so requires_grad=True) + invar = Constraint._set_device( + invar0, device=self.device, requires_grad=self.requires_grad + ) + + pred_outvar = self.forward(invar) + + # Collect minibatch info into cpu dictionaries + invar_cpu = { + key: value + [invar[key].cpu().detach()] + for key, value in invar_cpu.items() + } + + pred_outvar_cpu = { + key: value + [pred_outvar[key].cpu().detach()] + for key, value in pred_outvar_cpu.items() + } + + # Concat mini-batch tensors + invar_cpu = {key: torch.cat(value) for key, value in invar_cpu.items()} + + pred_outvar_cpu = { + key: torch.cat(value) for key, value in pred_outvar_cpu.items() + } + # compute losses on cpu + # TODO add metrics specific for validation + # TODO: add potential support for lambda_weighting + #losses = PointwiseValidator._l2_relative_error(true_outvar_cpu, pred_outvar_cpu) + losses = {key: torch.mean(value) for key, value in pred_outvar_cpu.items()} + + # convert to numpy arrays + invar = {k: v.numpy() for k, v in invar_cpu.items()} + pred_outvar = {k: v.numpy() for k, v in pred_outvar_cpu.items()} + + # save batch to vtk file TODO clean this up after graph unroll stuff + + named_pred_outvar = {"pred_" + k: v for k, v in pred_outvar.items()} + + # save batch to vtk/npz file TODO clean this up after graph unroll stuff + if "np" in save_filetypes: + np.savez( + results_dir + name, {**invar, **named_pred_outvar} + ) + if "vtk" in save_filetypes: + var_to_polyvtk( + {**invar, **named_pred_outvar}, results_dir + name + ) + + # add tensorboard plots + if self.plotter is not None: + self.plotter._add_figures( + "Validators", + name, + results_dir, + writer, + step, + invar, + pred_outvar, + ) + + # add tensorboard scalars + for k, loss in losses.items(): + if TF_SUMMARY: + writer.add_scalar("val/" + name + "/" + k, loss, step, new_style=True) + else: + writer.add_scalar( + "Validators/" + name , loss, step, new_style=True + ) + return losses + + +class DataConditionValidator(Validator): + """ + Validator that allows validating on pointwise data. + The validation error is the cumulative norm over all output + variables. The norm can be defined by the user. + + Parameters + ---------- + nodes : List[Node] + List of Modulus Nodes to unroll graph with. + invar : Dict[str, np.ndarray (N, 1)] + Dictionary of numpy arrays as input. + true_outvar : Dict[str, np.ndarray (N, 1)] + Dictionary of numpy arrays used to validate against validation. + batch_size : int, optional + Batch size used when running validation, by default 1024 + requires_grad : bool = False + If automatic differentiation is needed for computing results. + norm: int or 'inf', optional + The 'norm' which should be computed for evaluation. If 'inf', + maximum norm will be used. Else, the result will be taken to + the n-th potency (without computing the root!) + root: int, optional + The root of the norm. If norm is 'inf', this parameter will be + ignored. + """ + + def __init__( + self, + nodes: List[Node], + invar: Dict[str, np.array], + true_outvar: Dict[str, np.array], + batch_size: int = 1024, + requires_grad: bool = False, + norm: int = 2, + root: int = 1, + ): + + # get dataset and dataloader + self.dataset = DictPointwiseDataset(invar=invar, outvar=true_outvar) + self.dataloader = Constraint.get_dataloader( + dataset=self.dataset, + batch_size=batch_size, + shuffle=False, + drop_last=False, + num_workers=0, + distributed=False, + infinite=False, + ) + + # construct model from nodes + self.model = Graph( + nodes, + Key.convert_list(self.dataset.invar_keys), + Key.convert_list(self.dataset.outvar_keys), + ) + self.manager = DistributedManager() + self.device = self.manager.device + self.model.to(self.device) + + # set forward method + self.requires_grad = requires_grad + self.forward = self.forward_grad if requires_grad else self.forward_nograd + + # set plotter + self.plotter = None + + self.norm = norm + self.root = root + + def save_results(self, name, results_dir, writer, save_filetypes, step): + + invar_cpu = {key: [] for key in self.dataset.invar_keys} + true_outvar_cpu = {key: [] for key in self.dataset.outvar_keys} + pred_outvar_cpu = {key: [] for key in self.dataset.outvar_keys} + # Loop through mini-batches + for i, (invar0, true_outvar0, lambda_weighting) in enumerate(self.dataloader): + # Move data to device (may need gradients in future, if so requires_grad=True) + invar = Constraint._set_device( + invar0, device=self.device, requires_grad=self.requires_grad + ) + true_outvar = Constraint._set_device( + true_outvar0, device=self.device, requires_grad=self.requires_grad + ) + pred_outvar = self.forward(invar) + + # Collect minibatch info into cpu dictionaries + invar_cpu = { + key: value + [invar[key].cpu().detach()] + for key, value in invar_cpu.items() + } + true_outvar_cpu = { + key: value + [true_outvar[key].cpu().detach()] + for key, value in true_outvar_cpu.items() + } + pred_outvar_cpu = { + key: value + [pred_outvar[key].cpu().detach()] + for key, value in pred_outvar_cpu.items() + } + + # Concat mini-batch tensors + invar_cpu = {key: torch.cat(value) for key, value in invar_cpu.items()} + true_outvar_cpu = { + key: torch.cat(value) for key, value in true_outvar_cpu.items() + } + pred_outvar_cpu = { + key: torch.cat(value) for key, value in pred_outvar_cpu.items() + } + # compute losses on cpu + # TODO add metrics specific for validation + # TODO: add potential support for lambda_weighting + #losses = PointwiseValidator._l2_relative_error(true_outvar_cpu, pred_outvar_cpu) + loss=torch.zeros(1) + for key in true_outvar_cpu.keys(): + if self.norm == 'inf': + loss = torch.max(loss,torch.max(torch.abs(true_outvar_cpu[key] - pred_outvar_cpu[key]))) + else: + loss += torch.mean(torch.abs(true_outvar_cpu[key] - pred_outvar_cpu[key])**self.norm) + + losses ={'': loss**1/self.root} + + + + # convert to numpy arrays + invar = {k: v.numpy() for k, v in invar_cpu.items()} + true_outvar = {k: v.numpy() for k, v in true_outvar_cpu.items()} + pred_outvar = {k: v.numpy() for k, v in pred_outvar_cpu.items()} + + # save batch to vtk file TODO clean this up after graph unroll stuff + named_true_outvar = {"true_" + k: v for k, v in true_outvar.items()} + named_pred_outvar = {"pred_" + k: v for k, v in pred_outvar.items()} + + # save batch to vtk/npz file TODO clean this up after graph unroll stuff + if "np" in save_filetypes: + np.savez( + results_dir + name, {**invar, **named_true_outvar, **named_pred_outvar} + ) + if "vtk" in save_filetypes: + var_to_polyvtk( + {**invar, **named_true_outvar, **named_pred_outvar}, results_dir + name + ) + + # add tensorboard plots + if self.plotter is not None: + self.plotter._add_figures( + "Validators", + name, + results_dir, + writer, + step, + invar, + true_outvar, + pred_outvar, + ) + + # add tensorboard scalars + for k, loss in losses.items(): + if TF_SUMMARY: + writer.add_scalar("val/" + name + "/" + k, loss, step, new_style=True) + else: + writer.add_scalar( + "Validators/" + name, loss, step, new_style=True + ) + return losses + \ No newline at end of file diff --git a/src/torchphysics/wrapper/model.py b/src/torchphysics/wrapper/model.py new file mode 100644 index 00000000..965f3653 --- /dev/null +++ b/src/torchphysics/wrapper/model.py @@ -0,0 +1,383 @@ +from torchphysics.models.model import Model +from torchphysics.problem.spaces import Points + +from modulus.sym.hydra.utils import compose +from modulus.sym.hydra import instantiate_arch +from modulus.sym.models.arch import Arch +from modulus.sym.key import Key + +from typing import Dict +from torch import Tensor +import torch + +import logging +import os + +class TPModelArch(Arch): + """ + Wrapper class to convert instances of TorchPhysics model base class + getting all necessary attributes of Modulus Arch models + + Parameters + ---------- + model: TorchPhysics model object + The model object to be converted into Modulus model object + + """ + + def __init__( self,model) -> None: + input_keys, output_keys = self.getVarKeys(model) + super().__init__( + input_keys=input_keys, + output_keys=output_keys, + detach_keys=[], + periodicity=None, + ) + + self._impl = model + + def forward(self, in_vars: Dict[str, Tensor]) -> Dict[str, Tensor]: + conv_in_vars_2 = self.convertSpatialVariables(in_vars) + y=self._tensor_forward(conv_in_vars_2) + + res= self.split_output(y, self.output_key_dict, dim=-1) + return res + + def _tensor_forward(self, x: Tensor) -> Tensor: + # there were an error message coming out of an x-dictionary with tensor.float64 type entries + # so is converted to normal float here + x_t = {k: v.float() for k, v in Points.from_coordinates(x).coordinates.items()} + points = Points.from_coordinates(x_t) + y = self._impl(points) + return y + + + def convertSpatialVariables(self,vars): + conv_vars = {} + for key in self._impl.input_space.keys(): + if key =='x': + if self._impl.input_space['x'] > 1: + if self._impl.input_space['x'] ==2: + conv_vars['x']=torch.cat((vars['x'],vars['y']),dim=1) + else: + conv_vars['x']=torch.cat((vars['x'],vars['y'],vars['z']),dim=1) + else: + conv_vars['x']=vars['x'] + else: + if self._impl.input_space[key] >1: + cat_var = list(vars[key+str(l+1)] for l in range(self._impl.input_space[key])) + conv_vars[key] = torch.cat(cat_var,dim=1) + else: + conv_vars[key]=vars[key] + return conv_vars + + + def getVarKeys(self,model): + inputkeys = [] + outputkeys = [] + for key in model.input_space.keys(): + if key =='x': + inputkeys.append(Key('x')) + if model.input_space['x'] > 1: + inputkeys.append(Key('y')) + if model.input_space['x']== 3: + inputkeys.append(Key('z')) + else: + if model.input_space[key] >1: + for l in range(model.input_space[key]): + inputkeys.append(Key(key+str(l+1))) + else: + inputkeys.append(Key(key)) + for key in model.output_space.keys(): + if model.output_space[key] >1: + for l in range(model.output_space[key]): + outputkeys.append(Key(key+str(l+1))) + else: + outputkeys.append(Key(key)) + + return inputkeys, outputkeys + + def getInputSpace(self): + return self._impl.input_space + + + + + +class ModulusArchitectureWrapper(Model): + """ + Wrapper class to use all model architectures implemented in Modulus + library as TorchPhysics model. The chosen Modulus architecture is + defined by the arch_name parameter and optional architecture + specific parameters can be passed as keyword arguments. + The model then gets all necessary attributes of TorchPhysics model + base class. The input points are converted to the Modulus input + format and the Modulus output points back to the TorchPhysics + output format. + + Parameters + ---------- + input_space : Space + The space of the points the can be put into this model. + output_space : Space + The space of the points returned by this model. + arch_name : {"afno","distributed_afno","deeponet","fno","fourier", + "fully_connected","conv_fully_connected", + "fused_fully_connected","fused_fourier","fused_hash_encoding", + "hash_encoding","highway_fourier","modified_fourier", + "multiplicative_fourier","multiscale_fourier","pix2pix", + "siren","super_res"} + Name of the Modulus architecture. + **kwargs : optional + Additional keyword arguments, depending on the chosen Modulus + architecture - listed with default values: + "afno": + img_shape: Tuple[int] = MISSING + patch_size: int = 16 + embed_dim: int = 256 + depth: int = 4 + num_blocks: int = 8 + "distributed_afno": + img_shape: Tuple[int] = MISSING + patch_size: int = 16 + embed_dim: int = 256 + depth: int = 4 + num_blocks: int = 8 + channel_parallel_inputs: bool = False + channel_parallel_outputs: bool = False + "deeponet": + trunk_dim: Any = None # Union[None, int] + branch_dim: Any = None # Union[None, int] + "fno": + dimension: int = MISSING + decoder_net: Arch + nr_fno_layers: int = 4 + fno_modes: Any = 16 # Union[int, List[int]] + padding: int = 8 + padding_type: str = "constant" + activation_fn: str = "gelu" + coord_features: bool = True + "fourier": + frequencies: Any = "('axis', [0, 1, 2, 3, 4, 5, 6, 7, + 8, 9])" + frequencies_params: Any = "('axis', [0, 1, 2, 3, 4, 5, + 6, 7, 8, 9])" + activation_fn: str = "silu" + layer_size: int = 512 + nr_layers: int = 6 + skip_connections: bool = False + weight_norm: bool = True + adaptive_activations: bool = False + "fully_connected": + layer_size: int = 512 + nr_layers: int = 6 + skip_connections: bool = False + activation_fn: str = "silu" + adaptive_activations: bool = False + weight_norm: bool = True + "conv_fully_connected": + layer_size: int = 512 + nr_layers: int = 6 + skip_connections: bool = False + activation_fn: str = "silu" + adaptive_activations: bool = False + weight_norm: bool = True + "fused_fully_connected": + layer_size: int = 128 + nr_layers: int = 6 + activation_fn: str = "sigmoid" + "fused_fourier": + layer_size: int = 128 + nr_layers: int = 6 + activation_fn: str = "sigmoid" + n_frequencies: int = 12 + "fused_hash_encoding": + layer_size: int = 128 + nr_layers: int = 6 + activation_fn: str = "sigmoid" + indexing: str = "Hash" + n_levels: int = 16 + n_features_per_level: int = 2 + log2_hashmap_size: int = 19 + base_resolution: int = 16 + per_level_scale: float = 2.0 + interpolation: str = "Smoothstep" + "hash_encoding": + layer_size: int = 64 + nr_layers: int = 3 + skip_connections: bool = False + weight_norm: bool = True + adaptive_activations: bool = False + bounds: Any = "[(1.0, 1.0), (1.0, 1.0)]" + nr_levels: int = 16 + nr_features_per_level: int = 2 + log2_hashmap_size: int = 19 + base_resolution: int = 2 + finest_resolution: int = 32 + "highway_fourier": + frequencies: Any = "('axis', [0, 1, 2, 3, 4, 5, 6, 7, + 8, 9])" + frequencies_params: Any = "('axis', [0, 1, 2, 3, 4, + 5, 6, 7, 8, 9])" + activation_fn: str = "silu" + layer_size: int = 512 + nr_layers: int = 6 + skip_connections: bool = False + weight_norm: bool = True + adaptive_activations: bool = False + transform_fourier_features: bool = True + project_fourier_features: bool = False + "modified_fourier": + frequencies: Any = "('axis', [0, 1, 2, 3, 4, 5, 6, 7, + 8, 9])" + frequencies_params: Any = "('axis', [0, 1, 2, 3, 4, + 5, 6, 7, 8, 9])" + activation_fn: str = "silu" + layer_size: int = 512 + nr_layers: int = 6 + skip_connections: bool = False + weight_norm: bool = True + adaptive_activations: bool = False + "multiplicative_fourier": + layer_size: int = 512 + nr_layers: int = 6 + skip_connections: bool = False + activation_fn: str = "identity" + filter_type: str = "fourier" + weight_norm: bool = True + input_scale: float = 10.0 + gabor_alpha: float = 6.0 + gabor_beta: float = 1.0 + normalization: Any = (None # Change to Union[None, + Dict[str, Tuple[float, float]]] when supported) + "multiscale_fourier": + frequencies: Any = field(default_factory=lambda: [32]) + frequencies_params: Any = None + activation_fn: str = "silu" + layer_size: int = 512 + nr_layers: int = 6 + skip_connections: bool = False + weight_norm: bool = True + adaptive_activations: bool = False + "pix2pix": + dimension: int = MISSING + conv_layer_size: int = 64 + n_downsampling: int = 3 + n_blocks: int = 3 + scaling_factor: int = 1 + batch_norm: bool = True + padding_type: str = "reflect" + activation_fn: str = "relu" + "siren": + layer_size: int = 512 + nr_layers: int = 6 + first_omega: float = 30.0 + omega: float = 30.0 + normalization: Any = (None # Change to Union[None, + Dict[str, Tuple[float, float]]] when supported) + "super_res": + large_kernel_size: int = 7 + small_kernel_size: int = 3 + conv_layer_size: int = 32 + n_resid_blocks: int = 8 + scaling_factor: int = 8 + activation_fn: str = "prelu" + + + Examples + -------- + >>> testmodel=ModulusArchitectureWrapper(input_space=X*T, + output_space=U,arch_name='fully_connected',layer_size=30, nr_layers=3) + >>> testmodel=ModulusArchitectureWrapper(input_space=X*T, + output_space=U,arch_name='fourier',frequencies = ['axis',[0,1,2]]) + + """ + def __init__(self,input_space, output_space,arch_name,**kwargs): + # get the absolute path of the conf-directory + caller_path=os.path.abspath(os.getcwd()+'/conf') + os.makedirs(caller_path, exist_ok=True) + # Get the relative path of the current file to the conf-directory + current_path = os.path.relpath(caller_path,os.path.dirname(os.path.abspath(__file__))) + with open(caller_path+'/config_model.yaml', 'w') as f: + f.write('defaults :\n - modulus_default\n - arch:\n - '+arch_name) + cfg3 = compose(config_path=current_path, config_name="config_model") + + #from modulus.sym.models.arch import arch_name + inputkeys = [] + input_keys = [] + for key in input_space.keys(): + if key =='x': + inputkeys.append(Key('x')) + input_keys.append('x') + + if input_space['x'] > 1: + inputkeys.append(Key('y')) + input_keys.append('y') + if input_space['x']== 3: + inputkeys.append(Key('z')) + input_keys.append('z') + else: + if input_space[key] >1: + for l in range(input_space[key]): + inputkeys.append(Key(key+str(l+1))) + input_keys.append(key+str(l+1)) + else: + inputkeys.append(Key(key)) + input_keys.append(key) + + outputkeys = [] + for key in output_space.keys(): + if output_space[key] >1: + for l in range(output_space[key]): + outputkeys.append(Key(key+str(l+1))) + else: + outputkeys.append(Key(key)) + + super().__init__(input_space,output_space) + + self.output_space = output_space + self.input_keys = input_keys + hydra_conf = eval(f"cfg3.arch.{arch_name}") + + for key, value in kwargs.items(): + hydra_conf[key] = value + + self.modulus_net = instantiate_arch(input_keys=inputkeys,output_keys=outputkeys,cfg = hydra_conf) + + def forward(self, points): + num_points = len(points) + dim = len(self.input_keys) + # values of TP input points should get the same order as the keys of the input_space + points = self._fix_points_order(points) + # ordered values get the corresponding Modulus input keys (if some of TP input keys are of higher dimension than 1, they are split into several Modulus input keys) + points_modulus = dict(zip(self.input_keys,[points.as_tensor[:,index].reshape(num_points,1) for index in range(0,dim)])) + output_modulus = self.modulus_net(points_modulus) + for key in self.output_space.keys(): + if self.output_space[key] >1: + cat_out = [output_modulus[key+str(l+1)] for l in range(self.output_space[key])] + output_modulus[key] = torch.cat(cat_out,dim=1) + for l in range(self.output_space[key]): + output_modulus.pop(key+str(l+1)) + + return Points.from_coordinates(output_modulus) + + +class ModulusNetWrapper(Model): + """ + Wrapper to convert objects of Modulus base class Arch into + TorchPhysics models + + Parameters + ---------- + arch: Modulus Arch object + + """ + def __init__(self,arch): + input_space = torchphysics.problem.spaces.Space(arch.input_key_dict) + output_space = torchphysics.problem.spaces.Space(arch.output_key_dict) + + super().__init__(input_space,output_space) + + + + \ No newline at end of file diff --git a/src/torchphysics/wrapper/nodes.py b/src/torchphysics/wrapper/nodes.py new file mode 100644 index 00000000..fd0f3e79 --- /dev/null +++ b/src/torchphysics/wrapper/nodes.py @@ -0,0 +1,228 @@ +import inspect +from typing import Dict +from torch import Tensor +import torch.nn as nn +import torch +import numpy as np +import logging + + +class TPNodeFunction(nn.Module): + """ + Module to evaluates a given TorchPhysics function (objective) with + the given input variables. + In the forward call it converts the input variables to the format + of the TP objective function and evaluates it. + It returns the result of the objective function as a dictionary + with the name of the objective function as key and the norm of the + result as value. + + Parameters + ---------- + tpfunction : callable + The TorchPhysics function to evaluate. + input_space : dict + The input space of the objective function. + output_space : dict + The output space of the objective function. + data_functions : dict + The data functions of the objective function. + params : list + The parameters of the objective function. + + """ + + def __init__(self,tpfunction,input_space,output_space,data_functions,params): + super().__init__() + self.objective = tpfunction + self.input_space = input_space + self.output_space = output_space + self.data_functions = data_functions + self.params = params + variables = set(inspect.signature(self.objective.fun).parameters) + input_keys, output_keys = self.getInOutputVariables() + parameter_variables=variables-self.input_space.keys()-self.output_space.keys() + + parameter_variables_new = set([]) + for param in parameter_variables: + if param in params[0].space.variables: + if params[0].space.dim>1: + parameter_variables_new = parameter_variables_new | set([param+str(l) for l in range(params[0].space.dim)]) + else: + parameter_variables_new = parameter_variables_new | set(param) + self.parameter_variables=parameter_variables_new + self.variables = ((parameter_variables_new)|input_keys|output_keys)-set(self.data_functions.keys()) + self.cond_names = [tpfunction.fun.__name__] + + def forward(self, in_vars: Dict[str, Tensor]) -> Dict[str, Tensor]: + invars = self.convertVariables(in_vars) + objectives = self.objective(invars) + res = {self.cond_names[0]: torch.norm(objectives,dim=1)} + return res + + def getInOutputVariables(self): + ''' + Returns the input keys, output keys and spatial keys of the + objective function. + ''' + + spatial_keys = set([]) + inputkeys = set([]) + outputkeys = set([]) + + if {'x','y','z'} <= set(self.input_space.keys()): + spatial_keys = {'x','y','z'} + elif {'x','y'} <= set(self.input_space.keys()): + spatial_keys = {'x','y'} + elif {'x'} <= set(self.input_space.keys()): + if self.input_space['x'] > 1: + if self.input_space['x'] ==2: + spatial_keys = {'x','y'} + else: + spatial_keys = {'x','y','z'} + else: + spatial_keys = {'x'} + + for key in set(self.input_space.keys())- {'x','y','z'}: + if self.input_space[key] >1: + inputkeys = {key+str(l+1) for l in range(self.input_space[key])} + else: + inputkeys = inputkeys|{key} + for key in set(self.output_space.keys()): + if self.output_space[key] >1: + outputkeys = outputkeys|{key+str(l+1) for l in range(self.output_space[key])} + else: + outputkeys = outputkeys|{key} + + + return spatial_keys|inputkeys, outputkeys + + def convertVariables(self,vars): + ''' + Converts the input variables to the format of the TP + objective function + ''' + conv_vars = vars.copy() + + if {'x','y','z'} <= set(self.input_space.keys()): + pass + elif {'x','y'} <= set(self.input_space.keys()): + pass + elif {'x'} <= set(self.input_space.keys()): + if self.input_space['x'] > 1: + if self.input_space['x'] ==2: + conv_vars['x']=torch.cat((vars['x'],vars['y']),dim=1) + conv_vars.pop('y') + else: + conv_vars['x']=torch.cat((vars['x'],vars['y'],vars['z']),dim=1) + conv_vars.pop('y') + conv_vars.pop('z') + + for key in set(self.input_space.keys())- {'x','y','z'}: + if self.input_space[key] >1: + cat_var = list(vars[key+str(l+1)] for l in range(self.input_space[key])) + conv_vars[key] = torch.cat(cat_var,dim=1) + for l in range(self.input_space[key]): + conv_vars.pop(key+str(l+1)) + + for key in self.output_space.keys(): + if self.output_space[key] >1: + if self.input_space['x'] > 1: + if self.input_space['x'] ==2: + cat_var = list(OutvarFunction.apply(vars[key+str(l+1)], conv_vars['x'],vars['x'],vars['y']) for l in range(self.output_space[key])) + else: + cat_var = list(OutvarFunction.apply(vars[key+str(l+1)], conv_vars['x'],vars['x'],vars['y'],vars['z']) for l in range(self.output_space[key])) + conv_vars[key] = torch.cat(cat_var,dim=1) + else: + cat_var = list(vars[key+str(l+1)] for l in range(self.output_space[key])) + conv_vars[key] = torch.cat(cat_var,dim=1) + for l in range(self.output_space[key]): + conv_vars.pop(key+str(l+1)) + else: + if self.input_space['x'] > 1: + if self.input_space['x'] ==2: + conv_vars[key] = OutvarFunction.apply(vars[key], conv_vars['x'],vars['x'],vars['y']) + else: + conv_vars[key] = OutvarFunction.apply(vars[key], conv_vars['x'],vars['x'],vars['y'],vars['z']) + + for param in self.params[0].space.variables: + if self.params[0].space.dim>1: + conv_vars[param] = torch.cat(list(vars[param+str(l)] for l in range(self.params[0].space.dim)),dim=1) + for l in range(self.params[0].space.dim): + conv_vars.pop(param+str(l)) + else: + conv_vars[param] = vars[param] + + for fun in self.data_functions.keys(): + conv_vars[fun] = self.data_functions[fun](conv_vars) + + return conv_vars + + +class OutvarFunction(torch.autograd.Function): + """ + Function that calculates the gradient of the output variable with + respect to a multi-dimensional spatial input variable, if the + gradient function is given for the corresponding one-dimensional + spatial input variable. + Variables x,y,z are the one-dimensional spatial input variables and + x_vec is the multi-dimensional spatial input variable. + """ + @staticmethod + def forward(ctx, u, x_vec, x, y, z=None): + """ + Forward pass of the node. + + Args: + ctx (torch.autograd.function._ContextMethodMixin): Context + object for autograd. + u (torch.Tensor): Input tensor. + x_vec (torch.Tensor): Input tensor. + x (torch.Tensor): Input tensor. + y (torch.Tensor): Input tensor. + z (torch.Tensor, optional): Input tensor. Defaults to None. + + Returns: + torch.Tensor: Output tensor. + """ + # Compute u(u, xy) (new u) during forward pass + result = u + + # Save u and xy for the backward pass + if z is not None: + ctx.save_for_backward(u, x_vec, x, y, z) + else: + ctx.save_for_backward(u, x_vec, x, y) + return result + + @staticmethod + def backward(ctx, grad_output): + """ + Computes the backward pass for the custom autograd function. + + Args: + ctx (torch.autograd.function._ContextMethodMixin): The + context object that holds the saved tensors. + grad_output (torch.Tensor): The gradient of the output + with respect to the function's output. + + Returns: + tuple: A tuple containing the gradients of the input + tensors with respect to the function's inputs. + """ + grad_u = grad_output + + # Load u and xy from the context + if len(ctx.saved_tensors)==5: + u, x_vec,x,y,z = ctx.saved_tensors + else: + u, x_vec, x,y = ctx.saved_tensors + # Compute the gradients of u (new) with respect to u and xy + if len(ctx.saved_tensors)==5: + grad_x, grad_y, grad_z = torch.autograd.grad(u, (x,y,z), grad_output, create_graph=True) + grad_xyz = torch.cat((grad_x,grad_y,grad_z),dim=1) + return grad_u, OutvarFunction.apply(grad_xyz,x_vec,x,y,z), OutvarFunction.apply(grad_x,x_vec,x,y,z), OutvarFunction.apply(grad_y,x_vec,x,y,z), OutvarFunction.apply(grad_z,x_vec,x,y,z) + else: + grad_x, grad_y = torch.autograd.grad(u, (x,y), grad_output, create_graph=True) + grad_xy = torch.cat((grad_x,grad_y),dim=1) + return grad_u, OutvarFunction.apply(grad_xy,x_vec,x,y), OutvarFunction.apply(grad_x,x_vec,x,y), OutvarFunction.apply(grad_y,x_vec,x,y) diff --git a/src/torchphysics/wrapper/solver.py b/src/torchphysics/wrapper/solver.py new file mode 100644 index 00000000..413386e4 --- /dev/null +++ b/src/torchphysics/wrapper/solver.py @@ -0,0 +1,539 @@ +from torchphysics.problem.conditions.condition import PINNCondition, DataCondition, ParameterCondition +from torchphysics.problem.domains.domainoperations.product import ProductDomain +from torchphysics.problem.domains.domainoperations.union import UnionDomain, UnionBoundaryDomain +from torchphysics.problem.domains.domainoperations.intersection import IntersectionDomain, IntersectionBoundaryDomain +from torchphysics.problem.domains.domainoperations.cut import CutDomain, CutBoundaryDomain +from torchphysics.problem.domains.domainoperations.rotate import Rotate +from torchphysics.problem.domains.domainoperations.translate import Translate +from torchphysics.models.parameter import Parameter +from torchphysics.problem.spaces import Points + +from modulus.sym.domain import Domain +from modulus.sym.domain.constraint import PointwiseBoundaryConstraint, PointwiseInteriorConstraint, PointwiseConstraint +from modulus.sym.domain.inferencer import PointwiseInferencer +from modulus.sym.domain.validator import PointwiseValidator +from modulus.sym.domain.monitor import PointwiseMonitor +from modulus.sym.loss import PointwiseLossNorm + + +from modulus.sym.node import Node +from modulus.sym.key import Key +from modulus.sym.models.fully_connected import FullyConnectedArch + +from sympy import Symbol +from functools import partial + +import warnings +import torch +import sympy + +from .model import TPModelArch +from .nodes import TPNodeFunction +from .geometry import TPGeometryWrapper +from .helper import convertDataModulus2TP, convertDataTP2Modulus, PointwiseLossInfNorm, PointwiseLossMean, CustomInferencerPlotter, PINNConditionValidator, DataConditionValidator + +class ModulusSolverWrapper(): + """ + Wrapper to use the solver class of Modulus with the rest + implemented in TorchPhysics + + Parameters + ---------- + + tp_solver: torchphysics.solver.Solver (pl.LightningModule) + TorchPhysics solver class + callbacks: list of torchphysics.callbacks.Callback + List of TorchPhysics callbacks + **kwargs: + lambda_weighting: list of floats, integers, sympy expressions + or the string "sdf" + List of lambda weightings for the conditions. If the + string "sdf" is used, the lambda weighting is multiplied + with the signed distance function of the boundary condition. + If only one value is given, it is used for all conditions. + If the length of the list is not equal to the number of + conditions, the default value of 1.0 is used for the + remaining conditions. + + Returns + ------- + ModulusSolverWrapper + Wrapper class for Modulus solver class containing the Modulus + domain with the necessary nodes, geometries, conditions and + parameters + + """ + + def __init__(self,tpsolver,callbacks,**kwargs): + self.nodes = [] + self.domain = Domain() + + self.lambda_weighting_vals = [1.0]*len(tpsolver.train_conditions) + for key, value in kwargs.items(): + if key == 'lambda_weighting': + assert type(value) == list, "lambda_weighting must be a list" + assert all((type(val) in (int,float)) or (isinstance(val, sympy.Expr)) or (val=="sdf") for val in value), "lambda_weighting must be a list of floats, integers, sympy expressions or the string ""sdf""" + if len(value) == 1: + value = value*len(tpsolver.train_conditions) + elif len(value) != len(tpsolver.train_conditions): + assert False, "lambda_weighting must have the same length as the number of conditions or 1" + self.lambda_weighting_vals = value + + self.device = "cuda:0" if torch.cuda.is_available() else "cpu" + + self.models = [] + num_models = 0 + self.orig_models = [] + self.Modmodels = [] + self.Geometries = [] + self.conds = [] + self.isBoundary = [] + self.parameters = [] + self.parameter_samples = [] + self.parameter_nets = [] + objectives = [] + exist_DataCondition = False + is_inverse_problem = False + not_seen_before = True + + # loop through all conditions to collect nodes out of objective functions + for condition in tpsolver.train_conditions+tpsolver.val_conditions: + if type(condition)!=ParameterCondition: + model = condition.module + self.orig_models.append(model) + if type(model).__name__=='Parallel': + models = model.models + else: + models = [model] + for mod in models: + if mod not in self.models: + self.models.append(mod) + Modmodel = TPModelArch(mod) + self.Modmodels.append(Modmodel) + num_models +=1 + self.nodes.append(Modmodel.make_node("model"+str(num_models-1))) + + if (type(condition) == PINNCondition): + if condition.sampler.is_static: + for fn in condition.data_functions: + condition.data_functions[fn].fun = condition.data_functions[fn].fun.to(self.device) + # construct node out of objective function + NodeFCN=TPNodeFunction(condition.residual_fn,model.input_space,model.output_space,condition.data_functions,condition.parameter) + objectives.append(NodeFCN) + if condition in tpsolver.train_conditions and condition in tpsolver.val_conditions: + if not_seen_before: + self.nodes.append(Node(NodeFCN.variables, NodeFCN.cond_names, NodeFCN)) + not_seen_before = False + else: + self.nodes.append(Node(NodeFCN.variables, NodeFCN.cond_names, NodeFCN)) + # if there is a learnable parameter in the condition, add a parameter net to learn this parameter as Modulus does not support additional learnable parameters in the conditions. + # The parameter is then learned as a function of the input variables of the TP model + if (condition.parameter!=Parameter.empty())& (condition.parameter not in self.parameters): + is_inverse_problem = True + inputkeys, _= TPModelArch(model).getVarKeys(model) + outputkeys = [Key(var+str(ind+1)) for var in condition.parameter.space.variables for ind in range(condition.parameter.space.dim)] if condition.parameter.space.dim>1 else [Key(var) for var in condition.parameter.space.variables] + # the parameter net is a fully connected network with 2 layers and 30 neurons per layer + # the input keys are the keys of the corresponding TP model input space + parameter_net = FullyConnectedArch(input_keys=inputkeys, output_keys=outputkeys, layer_size = 30, nr_layers = 2) + self.parameter_nets.append(parameter_net) + self.nodes.append(parameter_net.make_node("parameter_net")) + self.parameters.append(condition.parameter) + points = condition.sampler.sample_points() + # sort out the input keys of the TP sampler and the corresponding values to the order of TP model input keys + self.parameter_samples.append(points[..., list(model.input_space.keys())]) + elif type(condition) == DataCondition: + exist_DataCondition = True + objectives.append(None) + elif type(condition) == ParameterCondition: + objectives.append(None) + else: + assert False, "Only PINNCondition, DataCondition, ParameterCondition are allowed as conditions" + + assert(exist_DataCondition if is_inverse_problem else True), "DataCondition must be present for inverse problems" + + # loop over train conditions to build Modulus constraints out of TorchPhysics conditions + for condition, obj, weight in zip(tpsolver.train_conditions,objectives[0:len(tpsolver.train_conditions)],self.lambda_weighting_vals): + if type(condition) == PINNCondition: + + # identify sampler + # check if static sampler + is_static = condition.sampler.is_static + sampler = condition.sampler.sampler if is_static else condition.sampler + quasi_random = False + if type(sampler).__name__ != 'RandomUniformSampler': + if type(sampler).__name__ == 'LHSSampler': + warnings.warn("Modulus only supports RandomUniformSampler or Halton sequence. Using Halton sequence instead.") + quasi_random = True + else: + warnings.warn("Modulus only supports RandomUniformSampler or Halton sequence. Using RandomUniformSampler instead.") + + + # identify different types of domains and split them into spatial, time and parameter domains + # spatial_domain is a nested dictionary containing different domain operations and the underlying TorchPhysics domains + spatial_domain, time_domain, parameter_domain = self.TPDomainWrapper(sampler.domain) + + # identify parameter ranges + param_ranges={} + for dom in parameter_domain: + if dom.dim>1: + for l in range(0,dom.dim): + param_ranges[Symbol(list(dom.space.variables)[0]+str(l+1))]= tuple((dom.bounding_box()[2*l].item(),dom.bounding_box()[2*l+1].item())) + else: + param_ranges[Symbol(list(dom.space.variables)[0])]= tuple(dom.bounding_box().numpy()) + + # identify time variable ranges + if time_domain != []: + assert(all(dom == time_domain[0] for dom in time_domain)), "Only single time domain allowed" + time_domain = time_domain[0] + if time_domain.dim == 1: + param_ranges[Symbol("t")]=tuple(time_domain.bounding_box().numpy()) + elif time_domain.dim == 0: + param_ranges[Symbol("t")]= time_domain.bounding_box()[0].item() + + # Build geometry out of spatial domain + is_boundary, geometry, cond, _, _= TPGeometryWrapper(spatial_domain).getModulusGeometry() + self.Geometries.append(geometry) + self.conds.append(cond) + self.isBoundary.append(is_boundary) + + # determine lambda_weightings + if weight == "sdf": + if is_boundary: + lambda_weightings = {name : condition.weight for name in obj.cond_names} + else: + lambda_weightings = {name : condition.weight*Symbol("sdf") for name in obj.cond_names} + else: + lambda_weightings = {name : condition.weight*weight for name in obj.cond_names} + + assert (condition.track_gradients == True), "track_gradients must be True for PINNCondition" + + # add constraints to domain + if is_boundary: + constraint = PointwiseBoundaryConstraint( + nodes=self.nodes, + geometry=geometry, + outvar={name : 0.0 for name in obj.cond_names}, + batch_size=len(sampler.sample_points()), + parameterization=param_ranges, + lambda_weighting = lambda_weightings, + fixed_dataset = is_static, + criteria = cond, + batch_per_epoch = 1, + quasirandom = quasi_random, + ) + + else: + constraint = PointwiseInteriorConstraint( + nodes=self.nodes, + geometry=geometry, + outvar={name : 0.0 for name in obj.cond_names}, + batch_size=len(sampler.sample_points()), + parameterization=param_ranges, + lambda_weighting = lambda_weightings, + fixed_dataset = is_static, + batch_per_epoch = 1, + criteria = cond, + quasirandom = quasi_random, + ) + self.domain.add_constraint(constraint, condition.name) + + + elif type(condition) == DataCondition: + if condition.use_full_dataset == True: + batch_size = len(condition.dataloader.dataset.data_points[0]) + else: + batch_size = condition.dataloader.dataset.batch_size + + if condition.norm == "inf": + norm = PointwiseLossInfNorm() + else: + norm = PointwiseLossNorm(condition.norm) + assert (condition.root == 1), "Only root=1 is allowed for DataCondition" + + outvar=convertDataTP2Modulus(condition.dataloader.dataset.data_points[1]) + invar=convertDataTP2Modulus(condition.dataloader.dataset.data_points[0]) + + # determine lambda_weightings + # lambda_weightings has to be dict of numpy arrays in the same length as invar + lambda_weightings = {name : condition.weight*weight*torch.ones(len(outvar[list(outvar.keys())[0]]),1) for name in outvar.keys()} + + data_constraint = PointwiseConstraint.from_numpy( + nodes=self.nodes, + invar=invar, + outvar=outvar, + loss = norm, + batch_size=batch_size, + lambda_weighting = lambda_weightings, + ) + + # define new forward function for data constraint to include the constrain_fn + if condition.constrain_fn: + def create_new_forward(condition): + def new_forward(self): + self._output_vars = self.model(self._input_vars) + inputvars = convertDataModulus2TP(self._input_vars,condition.module.input_space) + outputvars = convertDataModulus2TP(self._output_vars,condition.module.output_space) + constraint_output = condition.constrain_fn({**outputvars, **inputvars}) + output_dict = {key: constraint_output[:,index:index+condition.module.output_space[key]] for index, key in enumerate(outputvars.keys())} + #output_dict = {key: constraint_output[:,index:index+condition.module.output_space[key]] for index, key in enumerate(condition.module.output_space.keys())} + output_vars_points = Points.from_coordinates(output_dict) + self._output_vars = convertDataTP2Modulus(output_vars_points) + + return new_forward + + data_constraint.forward = create_new_forward(condition).__get__(data_constraint, PointwiseConstraint) + + self.domain.add_constraint(data_constraint, condition.name) + + elif type(condition) == ParameterCondition: + assert (condition.parameter.space.dim ==1), "Only single parameter allowed for ParameterCondition" + param_index = self.parameters.index(condition.parameter) + + points = self.parameter_samples[param_index] + modpoints = dict(zip([str(key) for key in self.parameter_nets[param_index].input_keys],[points.as_tensor[:,index].reshape(len(points),1) for index in range(len(self.parameter_nets[param_index].input_keys))])) + + # determine lambda_weightings + # lambda_weightings has to be dict of numpy arrays with the same length as invar + lambda_weightings = {condition.parameter.variables.pop() : condition.weight*weight*torch.ones(len(points),1)} + + parameter_constraint = PointwiseConstraint.from_numpy( + nodes=self.nodes, + invar=modpoints, + outvar= {condition.parameter.variables.pop():torch.zeros(len(points),1)}, + batch_size=batch_size, + loss = PointwiseLossMean(), + lambda_weighting = lambda_weightings, + ) + + # create new forward function for parameter constraint to include the penalty function + def create_new_forward_paramCond(condition): + def new_forward(self): + self._output_vars = self.model(self._input_vars) + penalty_output = condition.penalty({**self._output_vars}) + self._output_vars = {condition.parameter.variables.pop(): penalty_output} + return new_forward + + parameter_constraint.forward = create_new_forward_paramCond(condition).__get__(parameter_constraint, PointwiseConstraint) + self.domain.add_constraint(parameter_constraint, condition.name) + + else: + assert False, "Condition type not yet supported! Only PINNCondition, DataCondition or ParameterCondition!" + + # loop over validation conditions to build Modulus constraints out of TorchPhysics conditions + for condition, obj in zip(tpsolver.val_conditions,objectives[len(tpsolver.train_conditions):]): + if type(condition) == PINNCondition: + # convert sample points to Modulus format + samples=convertDataTP2Modulus(sampler.sample_points()) + + # build validator + validator = PINNConditionValidator( + nodes=self.nodes, + invar = samples, + output_names = obj.cond_names, + batch_size=len(samples), + requires_grad = condition.track_gradients, + ) + + self.domain.add_validator(validator, condition.name) + + elif type(condition) == DataCondition: + batch_size = condition.dataloader.dataset.batch_size + + outvar=convertDataTP2Modulus(condition.dataloader.dataset.data_points[1]) + invar=convertDataTP2Modulus(condition.dataloader.dataset.data_points[0]) + + validator = DataConditionValidator( + nodes=self.nodes, + invar=invar, + true_outvar=outvar, + batch_size=batch_size, + requires_grad = condition.track_gradients, + norm = condition.norm, + root = condition.root + ) + + # define new forward function for data validator to include the constrain_fn + if condition.constrain_fn: + def create_new_forward(condition): + def new_forward(self,invar): + with torch.set_grad_enabled(condition.track_gradients): + pred_outvar = self.model(invar) + inputvars = convertDataModulus2TP(invar,condition.module.input_space) + outputvars = convertDataModulus2TP(pred_outvar,condition.module.output_space) + constraint_output = condition.constrain_fn({**outputvars, **inputvars}) + output_dict = {key: constraint_output[:,index:index+condition.module.output_space[key]] for index, key in enumerate(condition.module.output_space.keys())} + output_vars_points = Points.from_coordinates(output_dict) + return convertDataTP2Modulus(output_vars_points) + return new_forward + + validator.forward = create_new_forward(condition).__get__(validator, PointwiseValidator) + self.domain.add_validator(validator, condition.name) + + # if inverse problem with single parameters to identify, add parameter monitor for each parameter + # A parameter is learned by a parameter net, and the mean of the net output will be the parameter value and is monitored + if is_inverse_problem: + for index, param_obj in enumerate(zip(self.parameter_samples,self.parameter_nets)): + points = param_obj[0] + # ordered values get the corresponding Modulus input keys (if some of TP input keys are of higher dimension than 1, they are split into several Modulus input keys) + modpoints = dict(zip([str(key) for key in param_obj[1].input_keys],[points.as_tensor[:,index].reshape(len(points),1) for index in range(len(param_obj[1].input_keys))])) + + # define mean function to compute mean of parameter net over a fixed set of input points + def mean_func(var, key): + return torch.mean(var[str(key)], dim=0) + + metrics = {"mean_"+str(key): partial(mean_func, key=key) for key in param_obj[1].output_keys} + + + parameter_monitor = PointwiseMonitor( + nodes=self.nodes, + invar=modpoints, + output_names=[str(key) for key in param_obj[1].output_keys], + metrics = metrics, + ) + self.domain.add_monitor(parameter_monitor, "parameter_monitor"+str(index)) + + + # if plotter callback is present, an inferencer with plotter is added to the domain + if callbacks: + for callback in callbacks: + invars = { key: value.cpu().detach().numpy() for key, value in convertDataTP2Modulus(callback.point_sampler.sample_points()).items()} + callback.point_sampler.created_points = None + + plotter_inferencer = PointwiseInferencer( + nodes=self.nodes, + invar=invars, + output_names= [var+str(ind+1) if callback.model.output_space[var] >1 else var for var in callback.model.output_space.variables for ind in range(callback.model.output_space[var]) ], + batch_size=len(invars), + plotter=CustomInferencerPlotter(callback), + ) + self.domain.add_inferencer(plotter_inferencer, callback.log_name) + + + def TPDomainWrapper(self,domain): + """ + Function that parses domain and splits it into spatial, time + and parameter domains. + The spatial domain is recursively split into nested dictionary + containing the following keys: + -'u': union + -'i': intersection + -'p': product + -'c': cut + -'r': rotate + -'t': translate + -'d': final domain + The values are the partial/splitted domains + + It returns the spatial_domain, time_domain and parameter_domain. + + Parameters + ---------- + domain: torchphysics.problem.domains.domain.Domain + TorchPhysics domain + + Returns + ------- + spatial_domain: dict + Nested dictionary containing the splitted domain + time_domain: list + List of time domains + parameter_domain: list + List of parameter domains + + """ + + # we have to define these global variables due to the recursive nature of the function + global parameter_domain, time_domain + parameter_domain = [] + time_domain = [] + spatial_domain = self.splitDomains(domain) + return spatial_domain, time_domain, parameter_domain + + + def splitDomains(self, domain): + """ + Recursive function that splits the spatial domain into nested + dictionary containing the following keys: + -'u': union + -'i': intersection + -'p': product + -'c': cut + -'r': rotate + -'t': translate + -'d': final domain + The values are then the partial/splitted domains + """ + global parameter_domain, time_domain + if self.is_splittable(domain): + if not hasattr(domain,'domain_a'): + domain.domain_a = domain.domain.domain_a + domain.domain_b = domain.domain.domain_b + + if self.is_xspace(domain.domain_a): + if self.is_xspace(domain.domain_b): + if type(domain) == ProductDomain: + return {'p': (self.splitDomains(domain.domain_a),self.splitDomains(domain.domain_b))} + elif type(domain) == UnionDomain: + return {'u': (self.splitDomains(domain.domain_a),self.splitDomains(domain.domain_b))} + elif type(domain) == UnionBoundaryDomain: + return {'ub': (self.splitDomains(domain.domain.domain_a),self.splitDomains(domain.domain.domain_b))} + elif type(domain) == IntersectionDomain: + return {'i': (self.splitDomains(domain.domain_a),self.splitDomains(domain.domain_b))} + elif type(domain) == IntersectionBoundaryDomain: + return {'ib': (self.splitDomains(domain.domain.domain_a),self.splitDomains(domain.domain.domain_b))} + elif type(domain) == CutDomain: + return {'c': (self.splitDomains(domain.domain_a),self.splitDomains(domain.domain_b))} + elif type(domain) == CutBoundaryDomain: + return {'cb': (self.splitDomains(domain.domain.domain_a),self.splitDomains(domain.domain.domain_b))} + else: + if self.is_tspace(domain.domain_b): + time_domain.append(domain.domain_b) + else: + parameter_domain.append(domain.domain_b) + return self.splitDomains(domain.domain_a) + + else: + if self.is_xspace(domain.domain_b): + if self.is_tspace(domain.domain_a): + time_domain.append(domain.domain_a) + else: + parameter_domain.append(domain.domain_a) + return self.splitDomains(domain.domain_b) + else: + if self.is_tspace(domain.domain_a): + time_domain.append(domain.domain_a) + else: + parameter_domain.append(domain.domain_a) + if self.is_tspace(domain.domain_b): + time_domain.append(domain.domain_b) + else: + parameter_domain.append(domain.domain_b) + return None + + elif type(domain) == Rotate: + # in the case of rotation, we have to collect the rotation function and the rotation center for later use + return {'r': [self.splitDomains(domain.domain),domain.rotation_fn(),domain.rotate_around()]} + elif type(domain) == Translate: + # in the case of translation, we have to collect the translation function for later use + return {'t': [self.splitDomains(domain.domain),domain.translate_fn()]} + else: + if self.is_xspace(domain): + return {'d': domain} + else: + if self.is_tspace(domain): + time_domain.append(domain) + else: + parameter_domain.append(domain) + return None + + def is_xspace(self,domain): + return ('x' in domain.space.variables)|('y' in domain.space.variables)|('z' in domain.space.variables) + + def is_tspace(self,domain): + return 't' in domain.space.variables + + def is_splittable(self,domain): + return type(domain) in set((ProductDomain,UnionDomain,UnionBoundaryDomain,IntersectionDomain,IntersectionBoundaryDomain,CutBoundaryDomain,CutDomain)) + + + diff --git a/src/torchphysics/wrapper/tests/test_ParallelogramCylinder.py b/src/torchphysics/wrapper/tests/test_ParallelogramCylinder.py new file mode 100644 index 00000000..a7a68aba --- /dev/null +++ b/src/torchphysics/wrapper/tests/test_ParallelogramCylinder.py @@ -0,0 +1,77 @@ +import pytest +from math import sqrt +import numpy as np +from modulus.sym.geometry.geometry import Geometry +from modulus.sym.geometry.curve import Curve +from modulus.sym.geometry.parameterization import Parameterization +from torchphysics.wrapper.geometry import ParallelogramCylinder + + +def test_ParallelogramCylinder_creation(): + origin = (0, 0, 0) + corner1 = (1, 0, 0) + corner2 = (0, 1, 0) + height = 2 + parameterization = Parameterization() + + cylinder = ParallelogramCylinder(origin, corner1, corner2, height, parameterization) + + assert isinstance(cylinder, Geometry) + assert cylinder.parameterization == parameterization + assert callable(cylinder.sdf) + assert cylinder.dims == ['x', 'y', 'z'] + assert cylinder.sdf({'x': np.array([[0]]),'y':np.array([[0]]),'z':np.array([[0]])},params={})['sdf'][0][0] == 0 + assert cylinder.sdf({'x': np.array([[0.5]]),'y':np.array([[0.5]]),'z':np.array([[1]])},params={})['sdf'][0][0]==pytest.approx(0.5,rel=1e-12) + assert cylinder.sdf({'x': np.array([[1.5]]),'y':np.array([[0.5]]),'z':np.array([[2]])},params={})['sdf'][0][0]== -0.5 + + + +def test_ParallelogramCylinder_curves(): + origin = (0, 0, 0) + corner1 = (1, 0, 0) + corner2 = (0, 1, 0) + height = 2 + parameterization = Parameterization() + + cylinder = ParallelogramCylinder(origin, corner1, corner2, height, parameterization) + + assert len(cylinder.curves) == 6 + assert all(isinstance(curve, Curve) for curve in cylinder.curves) + + + +def test_ParallelogramCylinder_invalid_parameters(): + origin = (0, 0, 0) + corner1 = (1, 0, 1) + corner2 = (0, 1, 0) + height = 2 + parameterization = Parameterization() + + with pytest.raises(AssertionError): + ParallelogramCylinder(origin, corner1, corner2, height, parameterization) + +def test_ParallelogramCylinder_negative_height(): + origin = (0, 0, 0) + corner1 = (1, 0, 0) + corner2 = (0, 1, 0) + height = -2 # Negative height should raise an error or be considered invalid + parameterization = Parameterization() + + with pytest.raises(TypeError): + ParallelogramCylinder(origin, corner1, corner2, height, parameterization) + + + + +def test_ParallelogramCylinder_repr(): + origin = (0, 0, 0) + corner1 = (1, 0, 0) + corner2 = (0, 1, 0) + height = 2 + parameterization = Parameterization() + + cylinder = ParallelogramCylinder(origin, corner1, corner2, height, parameterization) + + assert isinstance(repr(cylinder), str) + assert "ParallelogramCylinder" in repr(cylinder) + diff --git a/src/torchphysics/wrapper/tests/test_TPGeometryWrapper.py b/src/torchphysics/wrapper/tests/test_TPGeometryWrapper.py new file mode 100644 index 00000000..c09b5db6 --- /dev/null +++ b/src/torchphysics/wrapper/tests/test_TPGeometryWrapper.py @@ -0,0 +1,269 @@ +import pytest +import torchphysics as tp +import numpy as np +import torch + +from torchphysics.wrapper.geometry import TPGeometryWrapper +from torchphysics.problem.domains import (Interval, Circle, Sphere, Triangle, Parallelogram) +from torchphysics.problem.domains.domain2D.shapely_polygon import ShapelyPolygon +from torchphysics.problem.domains.domain3D.trimesh_polyhedron import TrimeshPolyhedron +from modulus.sym.geometry.primitives_1d import (Line1D) +from modulus.sym.geometry.primitives_2d import Circle as Circle_modulus +from modulus.sym.geometry.primitives_2d import Triangle as Triangle_modulus +from modulus.sym.geometry.primitives_2d import Polygon +from modulus.sym.geometry.primitives_3d import Sphere as Sphere_modulus + +from sympy import Eq, Symbol + + + +def single_domain_dict(domain): + domain_dict = {'d': domain} + return domain_dict + +def union_domain_dict(domain1,domain2): + domain_dict = {'u': [{'d': domain1},{'d': domain2}]} + return domain_dict + +def intersection_domain_dict(domain1,domain2): + return {'i': [{'d': domain1},{'d': domain2}]} + + +def cut_domain_dict(domain1,domain2): + return {'c' : [{'d': domain1},{'d': domain2}]} + +def translate_domain_dict(domain,translation_params): + return {'t' : [{'d': domain},translation_params]} + +def rotate_domain_dict(domain,rotation_matrix,rotation_center): + return {'r' : [{'d': domain},rotation_matrix,rotation_center]} + +def combined_operations_dict(domain1,domain2,domain3,translation_params,rotation_matrix,rotation_center): + return {'i' : [{'t': [{'r' : [{'d': domain1},rotation_matrix,rotation_center]},translation_params]},{'p':[{'d': domain2},{'d': domain3}]}]} + + +def product_domain_dict(domain1,domain2): + return {'p': [{'d': domain1},{'d': domain2}]} + +# Fixtures for different domain types +@pytest.fixture +def interval_domain(): + X = tp.spaces.R1('x') + return Interval(X,0, 1) + +@pytest.fixture +def interval_domain2(): + X = tp.spaces.R1('x') + return Interval(X,2,4) + +@pytest.fixture +def interval_domain_y(): + Y = tp.spaces.R1('y') + return Interval(Y,0,4) + + +@pytest.fixture +def interval_domain_z(): + Z = tp.spaces.R1('z') + return Interval(Z,0,4) + + + +@pytest.fixture +def circle_domain(): + X = tp.spaces.R2('x') + return Circle(X,center=(0, 0), radius=1) + +@pytest.fixture +def circle_domain_xy(): + X = tp.spaces.R1('x') + Y = tp.spaces.R1('y') + return Circle(X*Y,center=(0, 0), radius=1) + +@pytest.fixture +def sphere_domain(): + X = tp.spaces.R3('x') + return Sphere(X,center=(0, 0, 0), radius=1) + +@pytest.fixture +def triangle_domain(): + X = tp.spaces.R2('x') + return Triangle(X,origin=(0, 0), corner_1=(1, 0), corner_2=(0.5, np.sqrt(3)/2)) + +@pytest.fixture +def parallelogram_domain(): + X = tp.spaces.R2('x') + return Parallelogram(X,origin=(0, 0), corner_1=(1, 0), corner_2=(0.5, 0.5)) + +@pytest.fixture +def parallelogram_domain_xy(): + XY = tp.spaces.R1('x')*tp.spaces.R1('y') + return Parallelogram(XY,origin=(0, 0), corner_1=(1, 0), corner_2=(0, 2)) + +@pytest.fixture +def trimesh_polyhedron_domain(): + vertices = [[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1], [0.5, 0.5, -1]] + faces = [[0, 1, 3], [0, 2, 3], [1, 2, 3], [0, 2, 4], [0, 1, 4], [1, 2, 4]] + poly3D = TrimeshPolyhedron(tp.spaces.R3('x'), vertices=vertices, faces=faces) + return poly3D + +@pytest.fixture +def shapely_polygon_domain(): + X = tp.spaces.R2('x') + return ShapelyPolygon(X, vertices=[[0, 0], [1, 0], [1, 2], [0, 1]]) + +# Test functions for each domain type +def test_interval_domain(interval_domain): + is_boundary, geometry, cond, _, _= TPGeometryWrapper(single_domain_dict(interval_domain)).getModulusGeometry() + assert isinstance(geometry, Line1D) + assert not is_boundary + assert cond == None + +def test_circle_domain(circle_domain): + is_boundary, geometry, cond, _, _ = TPGeometryWrapper(single_domain_dict(circle_domain)).getModulusGeometry() + assert isinstance(geometry, Circle_modulus) + assert not is_boundary + assert cond == None + +def test_sphere_domain(sphere_domain): + is_boundary, geometry, cond, _, _ = TPGeometryWrapper(single_domain_dict(sphere_domain)).getModulusGeometry() + assert isinstance(geometry, Sphere_modulus) + assert not is_boundary + assert cond == None + +def test_triangle_domain(triangle_domain): + is_boundary, geometry, cond, _, _ = TPGeometryWrapper(single_domain_dict(triangle_domain)).getModulusGeometry() + assert isinstance(geometry, Triangle_modulus) + assert not is_boundary + assert cond == None + +def test_parallelogram_domain(parallelogram_domain): + is_boundary, geometry, cond, _, _ = TPGeometryWrapper(single_domain_dict(parallelogram_domain)).getModulusGeometry() + assert isinstance(geometry, Polygon) + assert not is_boundary + assert cond == None + +def test_trimesh_polyhedron_domain(trimesh_polyhedron_domain): + with pytest.raises(Exception) as excinfo: + is_boundary, geometry, cond, _, _ = TPGeometryWrapper(single_domain_dict(trimesh_polyhedron_domain)).getModulusGeometry() + try: + assert isinstance(geometry, Tessellation) + assert not is_boundary + assert cond == None + except NameError: + assert "Tessellation module only supported for Modulus docker installation due to missing pysdf installation!" in str(excinfo.value) + + +def test_boundary_of_interval_domain(interval_domain): + is_boundary, geometry, cond, _, _ = TPGeometryWrapper(single_domain_dict(interval_domain.boundary_left)).getModulusGeometry() + assert isinstance(geometry, Line1D) + assert is_boundary + assert (cond==Eq(Symbol('x'),0)) + +def test_boundary_of_circle_domain(circle_domain): + is_boundary, geometry, cond, _, _ = TPGeometryWrapper(single_domain_dict(circle_domain.boundary)).getModulusGeometry() + assert isinstance(geometry, Circle_modulus) + assert is_boundary + +def test_boundary_of_sphere_domain(sphere_domain): + is_boundary, geometry, cond, _, _ = TPGeometryWrapper(single_domain_dict(sphere_domain.boundary)).getModulusGeometry() + assert isinstance(geometry, Sphere_modulus) + assert is_boundary + +def test_boundary_of_triangle_domain(triangle_domain): + is_boundary, geometry, cond, _, _ = TPGeometryWrapper(single_domain_dict(triangle_domain.boundary)).getModulusGeometry() + assert isinstance(geometry,Triangle_modulus) + assert is_boundary + +def test_boundary_of_parallelogram_domain(parallelogram_domain): + is_boundary, geometry, cond, _, _ = TPGeometryWrapper(single_domain_dict(parallelogram_domain.boundary)).getModulusGeometry() + assert isinstance(geometry, Polygon) + assert is_boundary + +def test_boundary_of_trimesh_polyhedron_domain(trimesh_polyhedron_domain): + with pytest.raises(Exception) as excinfo: + is_boundary, geometry, cond, _, _ = TPGeometryWrapper(single_domain_dict(trimesh_polyhedron_domain.boundary)).getModulusGeometry() + try: + assert isinstance(geometry, Tesselation) + assert is_boundary + assert cond == None + except NameError: + assert "Tessellation module only supported for Modulus docker installation due to missing pysdf installation!" in str(excinfo.value) + + + + +def test_union_operation(interval_domain,interval_domain2): + is_boundary, geometry, cond, _, _ = TPGeometryWrapper(union_domain_dict(interval_domain,interval_domain2)).getModulusGeometry() + assert not is_boundary + assert geometry.sdf({'x': [[1.5]]},params={})['sdf'][0][0] < 0 + assert geometry.sdf({'x': [[3]]},params={})['sdf'][0][0] > 0 + assert geometry.sdf({'x': [[0.5]]},params={})['sdf'][0][0]>0 + assert abs(geometry.sdf({'x': [[2]]},params={})['sdf'][0][0]) < 1e-12 + assert cond == None + + + +def test_intersection_operation(circle_domain,parallelogram_domain): + is_boundary, geometry, cond, _, _ = TPGeometryWrapper(intersection_domain_dict(circle_domain,parallelogram_domain)).getModulusGeometry() + assert not is_boundary + assert abs(geometry.sdf({'x': np.array([[0.5]]),'y':np.array([[0.5]])},params={})['sdf'][0][0]) < 1e-16 + assert geometry.sdf({'x': np.array([[0.5]]),'y':np.array([[0.1]])},params={})['sdf'][0][0] > 0 + assert geometry.sdf({'x': np.array([[2]]),'y':np.array([[0]])},params={})['sdf'][0][0] < 0 + assert geometry.sdf({'x': np.array([[-0.5]]),'y':np.array([[0.5]])},params={})['sdf'][0][0] < 0 + assert cond == None + +def test_cut_operation(circle_domain,parallelogram_domain): + is_boundary, geometry, cond, _, _ = TPGeometryWrapper(cut_domain_dict(circle_domain,parallelogram_domain)).getModulusGeometry() + assert not is_boundary + assert abs(geometry.sdf({'x': np.array([[0.5]]),'y':np.array([[0.5]])},params={})['sdf'][0][0]) < 1e-16 + assert geometry.sdf({'x': np.array([[0.5]]),'y':np.array([[0.1]])},params={})['sdf'][0][0] < 0 + assert geometry.sdf({'x': np.array([[2]]),'y':np.array([[0]])},params={})['sdf'][0][0] < 0 + assert geometry.sdf({'x': np.array([[-0.5]]),'y':np.array([[0.5]])},params={})['sdf'][0][0] > 0 + assert cond == None + +def test_translate_operation(sphere_domain): + is_boundary, geometry, cond, translate_vec, rotation_list = TPGeometryWrapper(translate_domain_dict(sphere_domain,torch.tensor([5,0,0]))).getModulusGeometry() + assert not is_boundary + assert cond == None + assert rotation_list == [[],[],[],1] + assert translate_vec == [0,0,0] + assert geometry.sdf({'x': np.array([[0]]),'y':np.array([[0]]),'z':np.array([[0]])},params={})['sdf'][0][0] < 0 + assert abs(geometry.sdf({'x': np.array([[4]]),'y':np.array([[0]]),'z':np.array([[0]])},params={})['sdf'][0][0]) < 1e-12 + assert geometry.sdf({'x': np.array([[5.5]]),'y':np.array([[0.5]]),'z':np.array([[-0.5]])},params={})['sdf'][0][0] > 0 + + +def test_rotate_operation(circle_domain): + is_boundary, geometry, cond, translate_vec, rotation_list = TPGeometryWrapper(rotate_domain_dict(circle_domain,torch.tensor([[[-1.0000e+00, 0], + [0, -1.0000e+00]]]),torch.tensor([5,0]))).getModulusGeometry() + assert not is_boundary + assert cond == None + assert rotation_list == [[],[],[],0] + assert translate_vec == [0,0,0] + assert geometry.sdf({'x': np.array([[12]]),'y':np.array([[0]])},params={})['sdf'][0][0] < 0 + assert abs(geometry.sdf({'x': np.array([[11]]),'y':np.array([[0]])},params={})['sdf'][0][0]) <1e-12 + assert geometry.sdf({'x': np.array([[10]]),'y':np.array([[0.5]])},params={})['sdf'][0][0] > 0 + + +def test_product_operation(parallelogram_domain_xy,interval_domain_z): + is_boundary, geometry, cond, _, _ = TPGeometryWrapper(product_domain_dict(parallelogram_domain_xy,interval_domain_z)).getModulusGeometry() + assert not is_boundary + assert geometry.sdf({'x': np.array([[2]]),'y':np.array([[1]]),'z':np.array([[1]])},params={})['sdf'][0][0] < 0 + assert geometry.sdf({'x': np.array([[0.5]]),'y':np.array([[1]]),'z':np.array([[2]])},params={})['sdf'][0][0] > 0 + assert abs(geometry.sdf({'x': np.array([[1]]),'y':np.array([[1]]),'z':np.array([[1]])},params={})['sdf'][0][0]) <1e-12 + + assert cond == None + + +def test_multiple_operations(circle_domain_xy,interval_domain,interval_domain_y): + is_boundary, geometry, cond, translate_vec, rotation_list = TPGeometryWrapper(combined_operations_dict(circle_domain_xy,interval_domain,interval_domain_y,torch.tensor([1,-1]),torch.tensor([[[-1.0000e+00, 0],[0, -1.0000e+00]]]),torch.tensor([0,2]))).getModulusGeometry() + assert not is_boundary + assert cond == None + assert rotation_list == [[],[],[],0] + assert translate_vec == [0,0,0] + # left half circle with radius 1 and center at (1,3) + assert geometry.sdf({'x': np.array([[1.5]]),'y':np.array([[3]])},params={})['sdf'][0][0] < 0 + assert geometry.sdf({'x': np.array([[0.5]]),'y':np.array([[3]])},params={})['sdf'][0][0] > 0 + assert abs(geometry.sdf({'x': np.array([[1]]),'y':np.array([[3]])},params={})['sdf'][0][0]) <1e-12 + + diff --git a/src/torchphysics/wrapper/tests/test_model.py b/src/torchphysics/wrapper/tests/test_model.py new file mode 100644 index 00000000..1dc2f820 --- /dev/null +++ b/src/torchphysics/wrapper/tests/test_model.py @@ -0,0 +1,42 @@ +import pytest +import shutil +import os +import torch +from torchphysics.wrapper.model import ModulusArchitectureWrapper + +from torchphysics.problem.spaces import Space +from torchphysics.problem.spaces.points import Points + +@pytest.fixture +def input_space(): + return Space({'x': 2}) + +@pytest.fixture +def output_space(): + return Space({'output': 1}) + +@pytest.fixture +def modulus_architecture_wrapper(input_space, output_space): + return ModulusArchitectureWrapper(input_space, output_space, arch_name="fully_connected") + +def test_modulus_architecture_wrapper_init(modulus_architecture_wrapper): + assert isinstance(modulus_architecture_wrapper, ModulusArchitectureWrapper) + assert list(modulus_architecture_wrapper.input_space.keys()) == ['x'] + assert list(modulus_architecture_wrapper.output_space.keys()) == ['output'] + assert modulus_architecture_wrapper.input_space.dim == 2 + assert modulus_architecture_wrapper.output_space.dim == 1 + assert type(modulus_architecture_wrapper.modulus_net).__name__=='FullyConnectedArch' + +def test_modulus_architecture_wrapper_forward(modulus_architecture_wrapper): + in_vars = Points.from_coordinates({'x': torch.tensor([[1.0, 2.0], [3.0, 4.0]])}) + result = modulus_architecture_wrapper.forward(in_vars) + assert isinstance(result, Points) + assert list(result.coordinates.keys())==['output'] + assert result.coordinates['output'].shape == torch.Size([2,1]) + + +def teardown_module(module): + """This method is called after test completion.""" + conf_dir = os.path.abspath(os.getcwd() + '/conf') + if os.path.isdir(conf_dir): + shutil.rmtree(conf_dir) \ No newline at end of file diff --git a/src/torchphysics/wrapper/tests/test_wrapper.py b/src/torchphysics/wrapper/tests/test_wrapper.py new file mode 100644 index 00000000..62794608 --- /dev/null +++ b/src/torchphysics/wrapper/tests/test_wrapper.py @@ -0,0 +1,147 @@ +import pytest +import torchphysics as tp +import torch +import pytorch_lightning as pl +import os +import shutil +from tensorboard.backend.event_processing import event_accumulator + +from torchphysics.wrapper import TPModulusWrapper, ModulusSolverWrapper +from torchphysics.utils import PointsDataLoader +from torchphysics.problem.spaces import Points, R1 +from omegaconf import DictConfig +from modulus.sym.solver import Solver + + +def _create_dummies(): + fcn = tp.FCN(tp.spaces.R1('x'), tp.spaces.R1('u')) + ps = tp.samplers.RandomUniformSampler(tp.domains.Interval(tp.spaces.R1('x'), 0, 1), + n_points=10) + cond = tp.conditions.PINNCondition(fcn, ps, lambda u: u) + opti = tp.OptimizerSetting(optimizer_class=torch.optim.Adam, lr=0.1,) + solver = tp.Solver(train_conditions=[cond],optimizer_setting=opti) + trainer = pl.Trainer(max_steps=15) + + return fcn, trainer, solver + +def _create_dummies_with_params(): + model = tp.FCN(tp.spaces.R1('x'), tp.spaces.R1('u')) + ps = tp.samplers.RandomUniformSampler(tp.domains.Interval(tp.spaces.R1('x'), 0, 1), + n_points=10) + p = tp.models.parameter.Parameter(init=1.0, space=tp.spaces.R1('D')) + cond1 = tp.conditions.PINNCondition(model, ps, lambda u,D: u*D,parameter=p) + + loader = PointsDataLoader((Points(torch.tensor([[0.0], [2.0]]), R1('x')), + Points(torch.tensor([[0.0], [4.0]]), R1('u'))),batch_size=2) + + + cond2 = tp.conditions.DataCondition(module=model, dataloader=loader, norm=2) + + opti = tp.OptimizerSetting(optimizer_class=torch.optim.Adam, lr=0.1,) + solver = tp.Solver(train_conditions=[cond1,cond2],optimizer_setting=opti) + trainer = pl.Trainer(max_steps=15) + return trainer, solver, p + +def test_TPModulusWrapper_creation(): + _, trainer,solver = _create_dummies() + aggregator_args = {"alpha": 0.5} + scheduler_args = {"T_max": 999} + wrapper = TPModulusWrapper(trainer=trainer, solver=solver, lambda_weighting=["sdf"],aggregator="GradNorm", aggregator_args=aggregator_args,scheduler="CosineAnnealingLR", scheduler_args=scheduler_args,outputdir_name="my_output_dir",keep_output=True,confdir_name="my_conf_dir") + assert isinstance(wrapper, TPModulusWrapper) + + assert wrapper.cfg.loss._target_ == "modulus.sym.loss.aggregator.GradNorm" + assert wrapper.cfg.loss["alpha"] == 0.5 + assert wrapper.cfg.scheduler._target_ == "torch.optim.lr_scheduler.CosineAnnealingLR" + assert isinstance(wrapper.cfg, DictConfig) + assert wrapper.cfg.scheduler.T_max == 999 + assert wrapper.cfg.loss.alpha == 0.5 + assert wrapper.cfg.optimizer.lr == 0.1 + assert wrapper.cfg.network_dir == "my_output_dir" + assert wrapper.cfg.training.max_steps == 15 + assert wrapper.keep_output == True + assert wrapper.cfg.optimizer._target_ == "torch.optim.Adam" + + assert wrapper.Msolver.lambda_weighting_vals == ["sdf"] + assert isinstance(wrapper.Msolver,ModulusSolverWrapper) + assert isinstance(wrapper.slv,Solver) + + assert os.path.isdir(os.path.abspath(os.getcwd()+'/my_conf_dir')) + + + solver.optimizer_setting.scheduler_class=torch.optim.lr_scheduler.ExponentialLR + solver.optimizer_setting.scheduler_args={'gamma':0.8} + wrapper = TPModulusWrapper(trainer=trainer, solver=solver, lambda_weighting=["sdf"],aggregator="GradNorm", aggregator_args=aggregator_args,outputdir_name="my_output_dir",keep_output=False,confdir_name="my_conf_dir") + assert wrapper.cfg.scheduler._target_ == "torch.optim.lr_scheduler.ExponentialLR" + assert wrapper.cfg.scheduler.gamma == 0.8 + + + +def test_callbacks(): + model, trainer,solver = _create_dummies() + + trainer.callbacks.append(tp.utils.WeightSaveCallback(model= model,path=os.getcwd(),name='test',check_interval = 5, save_initial_model=True, + save_final_model = True)) + + with pytest.warns(UserWarning, match="The option check_interval of the WeightSaveCallback with check for minimial loss is not supported by Modulus. Only initial and final model state saves."): + wrapper = TPModulusWrapper(trainer=trainer, solver=solver,outputdir_name="my_output_dir",confdir_name="my_conf_dir") + wrapper.train() + + assert os.path.isfile(os.path.abspath(os.getcwd()+'/test_init.pt')) + assert os.path.isfile(os.path.abspath(os.getcwd()+'/test_final.pt')) + + model, trainer,solver = _create_dummies() + plot_sampler = tp.samplers.PlotSampler(tp.domains.Interval(tp.spaces.R1('x'), 0, 1), + n_points=50) + trainer.callbacks.append(tp.utils.PlotterCallback(model=model,plot_function=lambda u: u,point_sampler=plot_sampler, + log_name='plot_u', plot_type='plot',check_interval=5)) + wrapper = TPModulusWrapper(trainer=trainer, solver=solver,keep_output=True, outputdir_name="my_output_dir",confdir_name="my_conf_dir") + wrapper.train() + # path to tensorboard-events-file + log_dir = os.path.join(os.getcwd(), 'my_output_dir') + event_file = None + + # Check if tensorboard-events-file exists + for file in os.listdir(log_dir): + if file.startswith("events.out.tfevents"): + event_file = os.path.join(log_dir, file) + break + + assert event_file is not None, "tensorboard-events-file not found." + + # Usage of EventAccumulator to load events file + ea = event_accumulator.EventAccumulator(event_file) + ea.Reload() + + # Check if plot is contained + tags = ea.Tags()['images'] + assert 'Inferencers/plot_u/' in tags, "Plot 'plot_u' not found in tensorboard-events-file." + + + + +def test_train_function(): + _, trainer,solver = _create_dummies() + wrapper = TPModulusWrapper(trainer=trainer, solver=solver,outputdir_name="my_output_dir",confdir_name="my_conf_dir") + assert wrapper.train()==[] + assert os.path.isdir(os.path.abspath(os.getcwd()+'/my_output_dir')) + + trainer,solver, p = _create_dummies_with_params() + wrapper = TPModulusWrapper(trainer=trainer, solver=solver,keep_output=True, outputdir_name="my_output_dir",confdir_name="my_conf_dir") + wrapper.train() + assert abs(p.as_tensor- 1.0) >1e-15 + + + + +def teardown_module(module): + """This method is called after test completion.""" + conf_dir = os.path.abspath(os.getcwd() + '/my_conf_dir') + if os.path.isdir(conf_dir): + shutil.rmtree(conf_dir) + output_dir = os.path.abspath(os.getcwd() + '/my_output_dir') + if os.path.isdir(output_dir): + shutil.rmtree(output_dir) + if os.path.isfile(os.path.abspath(os.getcwd()+'/test_init.pt')): + os.remove(os.path.abspath(os.getcwd()+'/test_init.pt')) + if os.path.isfile(os.path.abspath(os.getcwd()+'/test_final.pt')): + os.remove(os.path.abspath(os.getcwd()+'/test_final.pt')) \ No newline at end of file diff --git a/src/torchphysics/wrapper/wrapper.py b/src/torchphysics/wrapper/wrapper.py new file mode 100644 index 00000000..450154d6 --- /dev/null +++ b/src/torchphysics/wrapper/wrapper.py @@ -0,0 +1,361 @@ +from modulus.sym.hydra.utils import compose +from modulus.sym.solver import Solver + +from torchphysics.models import Parameter +import torch + +import os +import csv +import numpy as np + +from .helper import OptimizerNameMapper, SchedulerNameMapper, AggregatorNameMapper +from .solver import ModulusSolverWrapper + +import shutil +import warnings +import logging +# Set the logging level for matplotlib to WARNING +matplotlib_logger = logging.getLogger('matplotlib') +matplotlib_logger.setLevel(logging.WARNING) + + +class TPModulusWrapper(): + ''' + Training of a TorchPhysics trainer/solver with the Modulus wrapper. + The wrapper is a bridge between TorchPhysics and Modulus. It uses + the Modulus configuration and the Modulus solver to train the + TorchPhysics solver/trainer/models. + Loss weighting algorithms can be selected by choosing an + aggregation function. The aggregation function can be selected by + the parameter "aggregator" and additional arguments can be set by + the parameter "aggregator_args". + A learning rate scheduler can be selected by the parameter + "scheduler" and additional arguments can be set by the parameter + "scheduler_args". + Pointwise weighting of the loss can be set by the parameter + "lambda_weighting". The pointwise weighting can be a list of + numbers or sympy expressions or the string 'sdf'. + Notes + ----- + The following conventions are important for the usage of the + wrapper: + Possible spatial variables: x, y, z or x (multidimensional) + Time variable: t + Geometries: TP geometries (domains) should be defined in the + space of x, y, z or x (multidimensional). + A general product of domains as in TorchPhysics + can not be implemented in Modulus, because the + domain defines a spatial geometry that must have a + sdf implementation which is not available in + general for an arbitrary product domain. + Cross products of domains and domain operations are + generally allowed, but too complicated + constructions should be avoided, e.g. a cross + product of 3 translated intervals is not allowed or + only isosceles triangle with axis of symmetry + parallel to y-axis in cross product with an interval + are supported. + Shapely polygons in 2D are supported, but currently + 3D geometries (TrimeshPolyhedron) defined in stl- + files are only supported in Modulus in the container + installation. + Translation of primitive domains is supported, but + not translation of domains resulting from domain + operations like union, intersection, difference. + + Parameters + ---------- + trainer : pytorch_lightning.Trainer + The Pytorch Lightning Trainer instance. + Supported parameters of trainer instance: + Modulus always uses GPU device if available. Trainer + settings concerning GPU devices or cuda handling, e.g. + 'accelerator' or 'devices', are not supported by this + wrapper. + Modulus automatically logs the training process with + tensorboard. The tensorboard logs are saved in the output + directory. + All TorchPhysics callbacks are supported by the wrapper. + The following Trainer parameters are supported by this + wrapper: + 'max_steps' : int, optional + The maximum number of training steps. If not + specified, the default value of Pytorch Lightning + Trainer is used. + 'val_check_interval' : int optional + How often to check the validation set. Default is + 1.0, meaning once per training epoch. + 'log_every_n_steps' : int, optional + How often to log within steps. Modulus/wrapper + default is 50. + Checkpoints, progress bar and model summary are + automatically used by Modulus. + + solver: torchphysics.solvers.Solver + The TorchPhysics solver instance. + All parameters of the TorchPhysics solver are supported by the + wrapper. + outputdir_name : str, optional + The name of the Modulus output directory, where the trained + models, the optimization configuration, tensorboard files, etc. + are saved. Default is 'outputs'. + If the directory contains the results of a previous run and the + configuration of a second call is mainly changed, there will be + a conflict loading existing models or configuration leading to + an error. + If the directory contains the results of a previous run and the + configuration of a second call is mainly unchanged, the new run + will continue the previous run with the already trained Modulus + models. + If not desired or in error case, it is recommended to remove + the content of the output directory before starting a new run. + confdir_name : str, optional + The name of a Modulus configuration directory, where initially + a hydra configuration file is saved. It is overwritten on each + call. Default is 'conf'. + keep_output : bool, optional. Default is True. + If True, the output directory is not deleted after the training + process. Otherwise, it is deleted after the training process. + **kwargs : optional + Additional keyword arguments: + "lambda_weighting": list[Union[int, float, sp.Basic]]=None + The spatial pointwise weighting of the constraint. It + is a list of numbers or sympy expressions or the string + 'sdf'. + If the list has more than one element, the length of + the list and the order has to match the number of + TorchPhysics conditions in the call + tp.solver.Solver([condition_1,condition_2,...]). + If the list has only one element, the same weighting is + applied to all conditions. + If it is a sympy expression, it has to be a function of + the spatial coordinates x, y, z. + If the TorchPhysics conditions contain weight + definitions with the keyword "weight", these are + additionally applied. + For example, + 'lambda_weighting=["sdf"]' would apply a pointwise + weighting of the loss by the signed distance function, + but only for interior sampling, not boundary sampling. + 'lambda_weighting=[100.0, 2.0] would apply a pointwise + weighting of the loss by 100 to the first TorchPhysics + condition and 2 to the second TorchPhysics condition. + 'lambda_weighting=[2.0*sympy.Symbol('x')]' would apply + a pointwise weighting to the loss of `2.0 * x`. + "aggregator" : str = None + The aggregation function for the loss. It is a string + with the name of the aggregation function. Default is + 'Sum'. + Possible values are 'Sum', 'GradNorm', 'ResNorm, + 'Homoscedastic','LRAnnealing','SoftAdapt','Relobralo'. + "aggregator_args" : dict = None + Additional arguments for the aggregation function. It + is a dictionary with the argument names as keys and the + argument values as values. Default is None. + Possible arguments with its default values are, + depending on the aggregator: + GradNorm: + alpha = 1.0 + ResNorm: + alpha = 1.0 + LRAnnealing: + update_freq = 1 + alpha = 0.01 + ref_key = None # Change to Union[None, str] when + supported by hydra + eps = 1e-8 + SoftAdapt: + eps = 1e-8 + Relobralo: + alpha = 0.95 + beta = 0.99 + tau = 1.0 + eps = 1e-8 + "scheduler" : str = None + The learning rate scheduler. It is a string with the + name of the scheduler. Default is constant learning + rate. + Possible values are 'ExponentialLR', + 'CosineAnnealingLR' or 'CosineAnnealingWarmRestarts'. + "scheduler_args" : dict = None + Additional arguments for the scheduler. It is a + dictionary with the argument names as keys and the + argument values as values. Default is None. + Possible arguments with its default values are, + depending on the scheduler: + ExponentialLR: + gamma = 0.99998718 + TFExponentialLR: + decay_rate = 0.95 + decay_steps = 1000 + CosineAnnealingLR: + T_max = 1000 + eta_min = 0.0 + last_epoch= -1 + CosineAnnealingWarmRestarts: + T_0 = 1000 + T_mult = 1 + eta_min = 0.0 + last_epoch = -1 + ''' + def __init__(self,trainer,solver,outputdir_name = 'outputs',confdir_name = 'conf',keep_output = True, **kwargs): + self.outputdir_name = outputdir_name + self.keep_output = keep_output + + self.logger=logging.getLogger() + self.ch = logging.StreamHandler() + self.logger.addHandler(self.ch) + + # get the absolute path of the conf-directory + caller_path=os.path.abspath(os.getcwd()+'/'+confdir_name) + + # Get the relative path of the current file to the conf-directory + current_path = os.path.relpath(caller_path,os.path.dirname(os.path.abspath(__file__))) + + if 'aggregator' in kwargs.keys(): + aggregator_name = AggregatorNameMapper(kwargs['aggregator']) + assert (aggregator_name != 'not defined'), "This aggregator class is currently not supported by Modulus!" + else: + aggregator_name = AggregatorNameMapper('Sum') + + optimizer_name = OptimizerNameMapper(solver.optimizer_setting.optimizer_class .__name__) + assert (optimizer_name != 'not defined'), "This optimizer class is currently not supported by Modulus!" + assert ((solver.optimizer_setting.scheduler_class is None) or (kwargs.get('scheduler') is None)), "The scheduler should either be defined in the optimizer settings or as additional parameter of the TPModulusWrapper!" + if solver.optimizer_setting.scheduler_class is not None: + scheduler_name = SchedulerNameMapper(solver.optimizer_setting.scheduler_class.__name__) + elif kwargs.get('scheduler') is not None: + scheduler_name = SchedulerNameMapper(kwargs.get('scheduler')) + else: + scheduler_name = 'exponential_lr' + + assert (scheduler_name != 'not defined'), "This scheduler class is currently not supported by Modulus!" + + os.makedirs(caller_path, exist_ok=True) + with open(caller_path+'/config_Modulus.yaml', 'w') as f: + f.write('defaults :\n - modulus_default\n - loss: '+aggregator_name+'\n - optimizer: '+optimizer_name+'\n - scheduler: '+scheduler_name+'\n - _self_\n') + self.cfg = compose(config_path=current_path, config_name="config_Modulus") + + training_rec_results_freq = self.cfg.training.rec_results_freq + + # as the initialization without scheduler leads to an error and additionally the constant LR scheduler is not implemented as class, + # we use the exponential LR scheduler with gamma=1 for constant lr + if (solver.optimizer_setting.scheduler_class is None) and (kwargs.get('scheduler') is None): + self.cfg.scheduler.gamma = 1.0 + else: + if solver.optimizer_setting.scheduler_args is not None: + for key, value in solver.optimizer_setting.scheduler_args.items(): + self.cfg.scheduler[key]=value + if kwargs.get('scheduler_args') is not None: + for key, value in kwargs.get('scheduler_args').items(): + self.cfg.scheduler[key]=value + + assert (solver.optimizer_setting.scheduler_frequency == 1), "The scheduler frequency is not supported in Modulus!" + + for key, value in solver.optimizer_setting.optimizer_args.items(): + self.cfg.optimizer[key]=value + + self.cfg.network_dir = self.outputdir_name + + self.cfg.training.max_steps = trainer.max_steps + self.cfg.optimizer.lr = solver.optimizer_setting.lr + + + self.cfg.training.rec_results_freq = min(training_rec_results_freq,trainer.max_steps) + + if (type(trainer.val_check_interval)) is float: + self.cfg.training.rec_validation_freq =int(trainer.val_check_interval*self.cfg.training.max_steps) + else: + self.cfg.training.rec_validation_freq = trainer.val_check_interval + self.cfg.training.summary_freq = trainer.log_every_n_steps + + self.weight_save_callback = None + self.checkpoint_callback = None + callbacks2Modulus=[] + + for callback in trainer.callbacks: + if type(callback).__name__=='WeightSaveCallback': + self.weight_save_callback = callback + if self.weight_save_callback.check_interval >0: + warnings.warn('The option check_interval of the WeightSaveCallback with check for minimial loss is not supported by Modulus. Only initial and final model state saves.') + elif type(callback).__name__=='PlotterCallback': + self.cfg.training.rec_inference_freq = callback.check_interval + callbacks2Modulus.append(callback) + elif type(callback).__name__=='TrainerStateCheckpoint': + self.checkpoint_callback = callback + self.cfg.training.save_network_freq = self.checkpoint_callback.check_interval + warnings.warn('TorchPhysics TrainerStateCheckpoint callback is requested. The checkpointing will be automatically done by Modulus and training can be restarted. The option weights_only is not supported by Modulus. Please use the WeightSaveCallback instead.') + + + + self.Msolver=ModulusSolverWrapper(solver,callbacks2Modulus,**kwargs) + + # adapt cfg-parameters + for key, value in kwargs.items(): + if key == 'aggregator_args': + for key2, value2 in value.items(): + self.cfg.loss[key2] = value2 + + + # Modulus solver instance is created + self.slv = Solver(self.cfg, self.Msolver.domain) + + + def train(self,resume_from_ckpt=False): + ''' + Call the training process of the Modulus solver. The training + process is started with the Modulus configuration and the + Modulus solver. + The TorchPhysics models are trained and the function + additionally returns trained parameters. + + Parameters + ---------- + resume_from_ckpt: bool, optional. Default is False. + If True, the training is resumed from the Modulus checkpoint files. + + ''' + # if a TorchPhysics checkpoint callback exists and the training is started without resume option, delete Modulus checkpoint and model files in the Modulus output directory. + if not resume_from_ckpt: + if os.path.isdir(os.path.abspath(os.getcwd()+'/'+self.outputdir_name)): + shutil.rmtree(os.path.abspath(os.getcwd()+'/'+self.outputdir_name)) + + # save initial model before training + if self.weight_save_callback: + if self.weight_save_callback.save_initial_model: + torch.save(self.weight_save_callback.model.state_dict(), self.weight_save_callback.path+'/' + self.weight_save_callback.name + '_init.pt') + # start training + self.slv.solve() + + + for model in self.Msolver.models: + model.to('cpu') + for model in self.Msolver.orig_models: + model.to('cpu') + + result_vec=[] + for index, param_obj in enumerate(zip(self.Msolver.parameters,self.Msolver.parameter_nets)): + for outvar in param_obj[1].output_keys: + with open(os.path.abspath(os.getcwd()+'/'+self.outputdir_name+'/monitors/mean_'+str(outvar)+'.csv'), 'r') as f: + reader = csv.reader(f) + key_line = next(reader) + last_line = list(reader)[-1] + result_vec.append(float(last_line[1])) + + self.Msolver.parameters[index]._t=torch.tensor([result_vec]) + if self.weight_save_callback: + if self.weight_save_callback.save_final_model: + torch.save(self.weight_save_callback.model.state_dict(), self.weight_save_callback.path+'/' + self.weight_save_callback.name + '_final.pt') + + # if no TorchPhysics checkpoint callback exists, delete Modulus checkpoint and model files in the Modulus output directory + if not self.checkpoint_callback and not self.keep_output: + if os.path.isdir(os.path.abspath(os.getcwd()+'/'+self.outputdir_name)): + shutil.rmtree(os.path.abspath(os.getcwd()+'/'+self.outputdir_name)) + result = self.Msolver.parameters + + + self.logger.removeHandler(self.ch) + + return result + + + From 6bf5fec1b0b65ab0055b1024605827c42327f29e Mon Sep 17 00:00:00 2001 From: "Geisel Maren (CR/AME3)" Date: Wed, 4 Sep 2024 10:53:13 +0200 Subject: [PATCH 2/8] Fixed README and documentation --- README.rst | 7 +++---- src/torchphysics/wrapper/TPModulusWrapper.rst | 18 +++++++++--------- 2 files changed, 12 insertions(+), 13 deletions(-) diff --git a/README.rst b/README.rst index 27d102eb..a092c4dc 100644 --- a/README.rst +++ b/README.rst @@ -61,13 +61,12 @@ Some built-in features are: .. _Shapely: https://github.com/shapely/shapely .. _`PyTorch Lightning`: https://www.pytorchlightning.ai/ -Additional module: +Additional modules: -- TorchPhysics comes with a wrapper module for `NVIDIA Modulus`_. -This module serves as a bridge between the two frameworks and allows users to train TorchPhysics models with the Modulus training framework with minimal changes to their existing code. The additional installation of Modulus is required and documented in the `Wrapper Readme`_. +- TorchPhysics comes with a wrapper module for `NVIDIA Modulus`_. This module serves as a bridge between the two frameworks and allows users to train TorchPhysics models with the Modulus training framework with minimal changes to their existing code. The additional installation of Modulus is required and documented in the `Wrapper Readme`_. .. _`NVIDIA Modulus`: https://developer.nvidia.com/modulus -.. _`Wrapper Readme`: https://github.com/boschresearch/torchphysics/blob/main/src/wrapper/TPModulusWrapper.rst +.. _`Wrapper Readme:` ./src/wrapper/TPModulusWrapper.rst Getting Started diff --git a/src/torchphysics/wrapper/TPModulusWrapper.rst b/src/torchphysics/wrapper/TPModulusWrapper.rst index 3fa54a54..52dfb945 100644 --- a/src/torchphysics/wrapper/TPModulusWrapper.rst +++ b/src/torchphysics/wrapper/TPModulusWrapper.rst @@ -5,7 +5,7 @@ TorchPhysics to Modulus Wrapper This folder contains a wrapper module for TorchPhysics to `NVIDIA Modulus`_. This module serves as a bridge between the two frameworks and allows users to train TorchPhysics models with the Modulus training framework with minimal changes to their existing code. -Both libraries are based on Pytorch_, but instead of `PyTorch Lightning`_, Modulus uses its own *Distributedmanager*, `Pytorch JIT`_ or TorchScript_ backend and CUDA graphs as a further framework to optimize and accelerate the training process, especially on NVIDIA GPUs. +Both libraries are based on Pytorch_, but instead of `PyTorch Lightning`_, Modulus uses its own *Distributedmanager*, `TorchScript`_ with Pytorch JIT backend and CUDA graphs as a further framework to optimize and accelerate the training process, especially on NVIDIA GPUs. Both libraries use ``torch.nn.Module`` as the base class for the model definition, so that any model architecture of Modulus can easily be used as a TorchPhysics model, which is one of the main purposes of this wrapper module. Modulus offers a wide range of model architectures, including various types of Fourier neural networks. @@ -33,15 +33,15 @@ The wrapper module consists of two main classes: Usage ===== -To use the wrapper module to run a TorchPhysics model in Modulus, you can add the following lines to your existing TorchPhysics code after the definition -of the ``trainer`` and the ``solver`` object and replace the ``trainer.fit(solver)`` call: +To use the wrapper module to run a TorchPhysics model in Modulus, you can add the following line to your existing TorchPhysics code after the definition +of the ``trainer`` and the ``solver`` object, replacing the ``trainer.fit(solver)`` call: .. code-block:: python torchphysics.wrapper.TPModulusWrapper(trainer,solver).train() -To use one of the Modulus model architectures, you can add the following lines to your existing TorchPhysics code and replace the model definition, +To use one of the Modulus model architectures, you can add the following line to your existing TorchPhysics code and replace the model definition, e.g. if you want to use the Modulus Fourier architecture as a TorchPhysics model: .. code-block:: python @@ -50,12 +50,12 @@ e.g. if you want to use the Modulus Fourier architecture as a TorchPhysics model Installation ============ -The wrapper module requires a working installation of TorchPhysics and Modulus Symbolic (Sym), which is a framework providing pythonic APIs, algorithms -and utilities to be used with Modulus core to physics inform model training as well as higher level abstraction for domain experts. +The wrapper module requires a working installation of TorchPhysics and Modulus Symbolic (Sym), which is a framework providing algorithms +and utilities to be used with Modulus core for physics informed model training. The installation of Modulus Sym is documented here: `NVIDIA Modulus Github Repository`_ -We recommend to create a new conda environment and to first install NVIDIA Modulus with the following commands: +We recommend to create a new conda environment and to first install NVIDIA Modulus with the following command: .. code-block:: python @@ -78,7 +78,7 @@ To circumvent disabling of TorchScript you can edit the file /modulus/sym/consta .. _`NVIDIA Modulus Github Repository`: https://github.com/NVIDIA/modulus-sym/tree/main .. _PyTorch: https://pytorch.org/ .. _TorchScript: https://pytorch.org/docs/stable/jit.html -.. _`Pytorch JIT`: https://pytorch.org/docs/stable/jit.html +.. _'TorchPhysics documentation': https://github.com/boschresearch/torchphysics/blob/main/README.rst @@ -97,7 +97,7 @@ Some notes * The loss definition in Modulus is based on Monte Carlo integration and therefore the loss is scaled proportional to the corresponding area, i.e. it is usually different from the loss in TorchPhysics, where the loss is the mean value. * Currently, ``stl``-file support in Modulus is only available for Docker installation, so ``shapely`` and ``Trimesh`` geometries in TorchPhysics can not be converted. * Cross product domains are generally not supported in Modulus, so must be automatically converted by the wrapper to existing primary geometries, so not all combinations of domain operations are allowed, e.g. product domains only from the union of 1D or 0D domains and no further rotation and translation is allowed (must be done with the entire product). -* Physics-Informed Deep Operator Networks (PIDOns) are currently not supported in the wrapper. The current implementation and documentation is much better in TorchPhysics than in Modulus +* Physics-Informed Deep Operator Networks (PIDOns) are currently not supported in the wrapper. * Fourier Neural Operators (FNOs) are currently not supported in the wrapper, but an FNO framework is currently being developed in TorchPhysics. * Samplers other than random uniformn and Halton sequence are not supported in Modulus. * The imposition of exact boundary conditions using hard constraints with Approximate Distance Functions (ADFs) is not yet supported in TorchPhysics. From 3d3f31a4d39cc9eddc0e1bc69d5e6b23e56dea49 Mon Sep 17 00:00:00 2001 From: "Geisel Maren (CR/AME3)" Date: Wed, 4 Sep 2024 11:00:41 +0200 Subject: [PATCH 3/8] Fixed link in README --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index a092c4dc..813b8916 100644 --- a/README.rst +++ b/README.rst @@ -66,7 +66,7 @@ Additional modules: - TorchPhysics comes with a wrapper module for `NVIDIA Modulus`_. This module serves as a bridge between the two frameworks and allows users to train TorchPhysics models with the Modulus training framework with minimal changes to their existing code. The additional installation of Modulus is required and documented in the `Wrapper Readme`_. .. _`NVIDIA Modulus`: https://developer.nvidia.com/modulus -.. _`Wrapper Readme:` ./src/wrapper/TPModulusWrapper.rst +.. _`Wrapper Readme`: ./src/wrapper/TPModulusWrapper.rst Getting Started From fc3bd7cf4b36466701f986f4a4a5689327cfe1ad Mon Sep 17 00:00:00 2001 From: "Geisel Maren (CR/AME3)" Date: Wed, 4 Sep 2024 11:06:13 +0200 Subject: [PATCH 4/8] Link fix --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 813b8916..7c4e0728 100644 --- a/README.rst +++ b/README.rst @@ -66,7 +66,7 @@ Additional modules: - TorchPhysics comes with a wrapper module for `NVIDIA Modulus`_. This module serves as a bridge between the two frameworks and allows users to train TorchPhysics models with the Modulus training framework with minimal changes to their existing code. The additional installation of Modulus is required and documented in the `Wrapper Readme`_. .. _`NVIDIA Modulus`: https://developer.nvidia.com/modulus -.. _`Wrapper Readme`: ./src/wrapper/TPModulusWrapper.rst +.. _`Wrapper Readme`: ./src/torchphysics/wrapper/TPModulusWrapper.rst Getting Started From 0f3b292c7177afd0fcdcfa52466af3ee19533014 Mon Sep 17 00:00:00 2001 From: "Geisel Maren (CR/AME3)" Date: Wed, 4 Sep 2024 11:11:25 +0200 Subject: [PATCH 5/8] Link fixed in documentation --- src/torchphysics/wrapper/TPModulusWrapper.rst | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/torchphysics/wrapper/TPModulusWrapper.rst b/src/torchphysics/wrapper/TPModulusWrapper.rst index 52dfb945..b829aecd 100644 --- a/src/torchphysics/wrapper/TPModulusWrapper.rst +++ b/src/torchphysics/wrapper/TPModulusWrapper.rst @@ -48,6 +48,7 @@ e.g. if you want to use the Modulus Fourier architecture as a TorchPhysics model model=torchphysics.wrapper.ModulusArchitectureWrapper(input_space=X*T, output_space=U,arch_name='fourier',frequencies = ['axis',[0,1,2]]) + Installation ============ The wrapper module requires a working installation of TorchPhysics and Modulus Symbolic (Sym), which is a framework providing algorithms @@ -78,7 +79,7 @@ To circumvent disabling of TorchScript you can edit the file /modulus/sym/consta .. _`NVIDIA Modulus Github Repository`: https://github.com/NVIDIA/modulus-sym/tree/main .. _PyTorch: https://pytorch.org/ .. _TorchScript: https://pytorch.org/docs/stable/jit.html -.. _'TorchPhysics documentation': https://github.com/boschresearch/torchphysics/blob/main/README.rst +.. _`TorchPhysics documentation`: https://github.com/boschresearch/torchphysics/blob/main/README.rst From dfee7a57b8e2a6f705ddf05cafe0f2cb89baf87a Mon Sep 17 00:00:00 2001 From: "Geisel Maren (CR/AME3)" Date: Mon, 23 Sep 2024 09:35:32 +0200 Subject: [PATCH 6/8] Delete notebook outputs --- examples/wrapper/heat-equation-wrapper.ipynb | 592 +----------------- ...rse-heat-equation-D-function-wrapper.ipynb | 396 ++---------- 2 files changed, 71 insertions(+), 917 deletions(-) diff --git a/examples/wrapper/heat-equation-wrapper.ipynb b/examples/wrapper/heat-equation-wrapper.ipynb index 6d49677b..8cce4ee0 100644 --- a/examples/wrapper/heat-equation-wrapper.ipynb +++ b/examples/wrapper/heat-equation-wrapper.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -62,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -102,20 +102,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig = tp.utils.scatter(X*T, inner_sampler, initial_v_sampler, boundary_v_sampler)" ] @@ -129,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -148,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -170,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -192,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -218,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -240,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -267,146 +256,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (cuda), used: True\n", - "TPU available: False, using: 0 TPU cores\n", - "HPU available: False, using: 0 HPUs\n", - "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", - "/home/gea3si/.conda/envs/mod_tp/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py:258: Found unsupported keys in the lr scheduler dict: {'gamma'}. HINT: remove them from the output of `configure_optimizers`.\n", - "\n", - " | Name | Type | Params | Mode \n", - "--------------------------------------------------------\n", - "0 | train_conditions | ModuleList | 5.4 K | train\n", - "1 | val_conditions | ModuleList | 5.4 K | train\n", - "--------------------------------------------------------\n", - "5.4 K Trainable params\n", - "0 Non-trainable params\n", - "5.4 K Total params\n", - "0.022 Total estimated model params size (MB)\n", - "20 Modules in train mode\n", - "0 Modules in eval mode\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "394bfad9438e419795450a4418d75731", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: | | 0/? [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "anim_sampler = tp.samplers.AnimationSampler(A_x, A_t, 100, n_points=400, data_for_other_variables={'D': 1.0})\n", "anim = tp.utils.animate(model, lambda u: u[:, 0], anim_sampler, ani_speed=10)\n", @@ -496,21 +337,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/gea3si/.conda/envs/mod_tp/lib/python3.10/site-packages/modulus/sym/hydra/utils.py:150: UserWarning: \n", - "The version_base parameter is not specified.\n", - "Please specify a compatability version level, or None.\n", - "Will assume defaults for version 1.1\n", - " hydra.initialize(\n" - ] - } - ], + "outputs": [], "source": [ "model = tp.models.Sequential(\n", " tp.models.NormalizationLayer(A_x*A_t*A_D), \n", @@ -529,7 +358,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -562,80 +391,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (cuda), used: True\n", - "TPU available: False, using: 0 TPU cores\n", - "HPU available: False, using: 0 HPUs\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/gea3si/.conda/envs/mod_tp/lib/python3.10/site-packages/modulus/sym/hydra/utils.py:150: UserWarning: \n", - "The version_base parameter is not specified.\n", - "Please specify a compatability version level, or None.\n", - "Will assume defaults for version 1.1\n", - " hydra.initialize(\n", - "Setting JobRuntime:name=app\n", - "/home/gea3si/NewModTpWrapper/torchphysics/src/torchphysics/wrapper/solver.py:168: UserWarning: Modulus only supports RandomUniformSampler or Halton sequence. Using RandomUniformSampler instead.\n", - " warnings.warn(\"Modulus only supports RandomUniformSampler or Halton sequence. Using RandomUniformSampler instead.\")\n", - "/home/gea3si/.conda/envs/mod_tp/lib/python3.10/site-packages/modulus/sym/trainer.py:453: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n", - " self.scaler = GradScaler(enabled=enable_scaler)\n", - "attempting to restore from: /home/gea3si/NewModTpWrapper/torchphysics/examples/wrapper/outputs_heat\n", - "optimizer checkpoint not found\n", - "model model0.0.pth not found\n", - "[step: 0] saved constraint results to outputs_heat\n", - "[step: 0] record constraint batch time: 2.487e-01s\n", - "[step: 0] saved validator results to outputs_heat\n", - "[step: 0] record validators time: 4.094e+01s\n", - "[step: 0] saved inferencer results to outputs_heat\n", - "[step: 0] record inferencers time: 1.546e+00s\n", - "[step: 0] saved checkpoint to /home/gea3si/NewModTpWrapper/torchphysics/examples/wrapper/outputs_heat\n", - "[step: 0] loss: 3.529e+04\n", - "Attempting cuda graph building, this may take a bit...\n", - "[step: 40] saved validator results to outputs_heat\n", - "[step: 40] record validators time: 3.914e+01s\n", - "[step: 50] saved inferencer results to outputs_heat\n", - "[step: 50] record inferencers time: 1.449e+00s\n", - "[step: 80] saved validator results to outputs_heat\n", - "[step: 80] record validators time: 4.045e+01s\n", - "[step: 100] saved inferencer results to outputs_heat\n", - "[step: 100] record inferencers time: 1.440e+00s\n", - "[step: 100] loss: 8.275e+02, time/iteration: 1.250e+03 ms\n", - "[step: 120] saved validator results to outputs_heat\n", - "[step: 120] record validators time: 4.159e+01s\n", - "[step: 150] saved inferencer results to outputs_heat\n", - "[step: 150] record inferencers time: 1.436e+00s\n", - "[step: 160] saved validator results to outputs_heat\n", - "[step: 160] record validators time: 4.328e+01s\n", - "[step: 200] saved constraint results to outputs_heat\n", - "[step: 200] record constraint batch time: 2.162e-01s\n", - "[step: 200] saved validator results to outputs_heat\n", - "[step: 200] record validators time: 4.192e+01s\n", - "[step: 200] saved inferencer results to outputs_heat\n", - "[step: 200] record inferencers time: 1.478e+00s\n", - "[step: 200] loss: 1.945e+02, time/iteration: 1.620e+03 ms\n", - "[step: 200] reached maximum training steps, finished training!\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "decay_rate = 0.95\n", "decay_steps = 15000\n", @@ -675,20 +433,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "anim_sampler = tp.samplers.AnimationSampler(A_x, A_t, 100, n_points=400, data_for_other_variables={'D': 1.0})\n", "anim = tp.utils.animate(model, lambda u: u[:, 0], anim_sampler, ani_speed=10)\n", @@ -705,224 +452,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on class TPModulusWrapper in module torchphysics.wrapper.wrapper:\n", - "\n", - "class TPModulusWrapper(builtins.object)\n", - " | TPModulusWrapper(trainer, solver, outputdir_name='outputs', confdir_name='conf', keep_output=True, **kwargs)\n", - " | \n", - " | Training of a TorchPhysics trainer/solver with the Modulus wrapper.\n", - " | The wrapper is a bridge between TorchPhysics and Modulus. It uses\n", - " | the Modulus configuration and the Modulus solver to train the \n", - " | TorchPhysics solver/trainer/models.\n", - " | Loss weighting algorithms can be selected by choosing an\n", - " | aggregation function. The aggregation function can be selected by\n", - " | the parameter \"aggregator\" and additional arguments can be set by \n", - " | the parameter \"aggregator_args\".\n", - " | A learning rate scheduler can be selected by the parameter \n", - " | \"scheduler\" and additional arguments can be set by the parameter \n", - " | \"scheduler_args\".\n", - " | Pointwise weighting of the loss can be set by the parameter \n", - " | \"lambda_weighting\". The pointwise weighting can be a list of \n", - " | numbers or sympy expressions or the string 'sdf'. \n", - " | Notes \n", - " | ----- \n", - " | The following conventions are important for the usage of the \n", - " | wrapper:\n", - " | Possible spatial variables: x, y, z or x (multidimensional)\n", - " | Time variable: t\n", - " | Geometries: TP geometries (domains) should be defined in the \n", - " | space of x, y, z or x (multidimensional).\n", - " | A general product of domains as in TorchPhysics \n", - " | can not be implemented in Modulus, because the \n", - " | domain defines a spatial geometry that must have a\n", - " | sdf implementation which is not available in \n", - " | general for an arbitrary product domain.\n", - " | Cross products of domains and domain operations are\n", - " | generally allowed, but too complicated \n", - " | constructions should be avoided, e.g. a cross \n", - " | product of 3 translated intervals is not allowed or\n", - " | only isosceles triangle with axis of symmetry \n", - " | parallel to y-axis in cross product with an interval \n", - " | are supported.\n", - " | Shapely polygons in 2D are supported, but currently\n", - " | 3D geometries (TrimeshPolyhedron) defined in stl-\n", - " | files are only supported in Modulus in the container \n", - " | installation.\n", - " | Translation of primitive domains is supported, but \n", - " | not translation of domains resulting from domain \n", - " | operations like union, intersection, difference.\n", - " | \n", - " | Parameters\n", - " | ---------- \n", - " | trainer : pytorch_lightning.Trainer\n", - " | The Pytorch Lightning Trainer instance. \n", - " | Supported parameters of trainer instance:\n", - " | Modulus always uses GPU device if available. Trainer \n", - " | settings concerning GPU devices or cuda handling, e.g. \n", - " | 'accelerator' or 'devices', are not supported by this\n", - " | wrapper.\n", - " | Modulus automatically logs the training process with \n", - " | tensorboard. The tensorboard logs are saved in the output \n", - " | directory.\n", - " | All TorchPhysics callbacks are supported by the wrapper.\n", - " | The following Trainer parameters are supported by this \n", - " | wrapper:\n", - " | 'max_steps' : int, optional\n", - " | The maximum number of training steps. If not \n", - " | specified, the default value of Pytorch Lightning \n", - " | Trainer is used.\n", - " | 'val_check_interval' : int optional\n", - " | How often to check the validation set. Default is \n", - " | 1.0, meaning once per training epoch.\n", - " | 'log_every_n_steps' : int, optional\n", - " | How often to log within steps. Modulus/wrapper \n", - " | default is 50. \n", - " | Checkpoints, progress bar and model summary are \n", - " | automatically used by Modulus.\n", - " | \n", - " | solver: torchphysics.solvers.Solver\n", - " | The TorchPhysics solver instance.\n", - " | All parameters of the TorchPhysics solver are supported by the \n", - " | wrapper.\n", - " | outputdir_name : str, optional\n", - " | The name of the Modulus output directory, where the trained \n", - " | models, the optimization configuration, tensorboard files, etc. \n", - " | are saved. Default is 'outputs'.\n", - " | If the directory contains the results of a previous run and the\n", - " | configuration of a second call is mainly changed, there will be \n", - " | a conflict loading existing models or configuration leading to \n", - " | an error.\n", - " | If the directory contains the results of a previous run and the\n", - " | configuration of a second call is mainly unchanged, the new run \n", - " | will continue the previous run with the already trained Modulus \n", - " | models.\n", - " | If not desired or in error case, it is recommended to remove \n", - " | the content of the output directory before starting a new run.\n", - " | confdir_name : str, optional \n", - " | The name of a Modulus configuration directory, where initially \n", - " | a hydra configuration file is saved. It is overwritten on each \n", - " | call. Default is 'conf'. \n", - " | keep_output : bool, optional. Default is True.\n", - " | If True, the output directory is not deleted after the training\n", - " | process. Otherwise, it is deleted after the training process. \n", - " | **kwargs : optional\n", - " | Additional keyword arguments:\n", - " | \"lambda_weighting\": list[Union[int, float, sp.Basic]]=None \n", - " | The spatial pointwise weighting of the constraint. It \n", - " | is a list of numbers or sympy expressions or the string\n", - " | 'sdf'. \n", - " | If the list has more than one element, the length of \n", - " | the list and the order has to match the number of \n", - " | TorchPhysics conditions in the call \n", - " | tp.solver.Solver([condition_1,condition_2,...]).\n", - " | If the list has only one element, the same weighting is\n", - " | applied to all conditions.\n", - " | If it is a sympy expression, it has to be a function of \n", - " | the spatial coordinates x, y, z.\n", - " | If the TorchPhysics conditions contain weight \n", - " | definitions with the keyword \"weight\", these are \n", - " | additionally applied.\n", - " | For example,\n", - " | 'lambda_weighting=[\"sdf\"]' would apply a pointwise \n", - " | weighting of the loss by the signed distance function, \n", - " | but only for interior sampling, not boundary sampling.\n", - " | 'lambda_weighting=[100.0, 2.0] would apply a pointwise \n", - " | weighting of the loss by 100 to the first TorchPhysics \n", - " | condition and 2 to the second TorchPhysics condition.\n", - " | 'lambda_weighting=[2.0*sympy.Symbol('x')]' would apply \n", - " | a pointwise weighting to the loss of `2.0 * x`.\n", - " | \"aggregator\" : str = None\n", - " | The aggregation function for the loss. It is a string \n", - " | with the name of the aggregation function. Default is \n", - " | 'Sum'.\n", - " | Possible values are 'Sum', 'GradNorm', 'ResNorm, \n", - " | 'Homoscedastic','LRAnnealing','SoftAdapt','Relobralo'.\n", - " | \"aggregator_args\" : dict = None\n", - " | Additional arguments for the aggregation function. It \n", - " | is a dictionary with the argument names as keys and the\n", - " | argument values as values. Default is None.\n", - " | Possible arguments with its default values are, \n", - " | depending on the aggregator:\n", - " | GradNorm: \n", - " | alpha = 1.0\n", - " | ResNorm:\n", - " | alpha = 1.0\n", - " | LRAnnealing:\n", - " | update_freq = 1\n", - " | alpha = 0.01\n", - " | ref_key = None # Change to Union[None, str] when \n", - " | supported by hydra\n", - " | eps = 1e-8\n", - " | SoftAdapt:\n", - " | eps = 1e-8\n", - " | Relobralo:\n", - " | alpha = 0.95\n", - " | beta = 0.99\n", - " | tau = 1.0\n", - " | eps = 1e-8\n", - " | \"scheduler\" : str = None\n", - " | The learning rate scheduler. It is a string with the \n", - " | name of the scheduler. Default is constant learning \n", - " | rate. \n", - " | Possible values are 'ExponentialLR', \n", - " | 'CosineAnnealingLR' or 'CosineAnnealingWarmRestarts'. \n", - " | \"scheduler_args\" : dict = None\n", - " | Additional arguments for the scheduler. It is a \n", - " | dictionary with the argument names as keys and the \n", - " | argument values as values. Default is None.\n", - " | Possible arguments with its default values are, \n", - " | depending on the scheduler:\n", - " | ExponentialLR:\n", - " | gamma = 0.99998718\n", - " | TFExponentialLR:\n", - " | decay_rate = 0.95\n", - " | decay_steps = 1000\n", - " | CosineAnnealingLR:\n", - " | T_max = 1000\n", - " | eta_min = 0.0\n", - " | last_epoch= -1\n", - " | CosineAnnealingWarmRestarts:\n", - " | T_0 = 1000\n", - " | T_mult = 1\n", - " | eta_min = 0.0\n", - " | last_epoch = -1\n", - " | \n", - " | Methods defined here:\n", - " | \n", - " | __init__(self, trainer, solver, outputdir_name='outputs', confdir_name='conf', keep_output=True, **kwargs)\n", - " | Initialize self. See help(type(self)) for accurate signature.\n", - " | \n", - " | train(self, resume_from_ckpt=False)\n", - " | Call the training process of the Modulus solver. The training \n", - " | process is started with the Modulus configuration and the \n", - " | Modulus solver.\n", - " | The TorchPhysics models are trained and the function \n", - " | additionally returns trained parameters.\n", - " | \n", - " | Parameters\n", - " | ----------\n", - " | resume_from_ckpt: bool, optional. Default is False.\n", - " | If True, the training is resumed from the Modulus checkpoint files.\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Data descriptors defined here:\n", - " | \n", - " | __dict__\n", - " | dictionary for instance variables (if defined)\n", - " | \n", - " | __weakref__\n", - " | list of weak references to the object (if defined)\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "help(tp.wrapper.TPModulusWrapper)" ] @@ -936,77 +468,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/gea3si/.conda/envs/mod_tp/lib/python3.10/site-packages/modulus/sym/hydra/utils.py:150: UserWarning: \n", - "The version_base parameter is not specified.\n", - "Please specify a compatability version level, or None.\n", - "Will assume defaults for version 1.1\n", - " hydra.initialize(\n", - "Setting JobRuntime:name=app\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/gea3si/NewModTpWrapper/torchphysics/src/torchphysics/wrapper/solver.py:168: UserWarning: Modulus only supports RandomUniformSampler or Halton sequence. Using RandomUniformSampler instead.\n", - " warnings.warn(\"Modulus only supports RandomUniformSampler or Halton sequence. Using RandomUniformSampler instead.\")\n", - "/home/gea3si/.conda/envs/mod_tp/lib/python3.10/site-packages/modulus/sym/trainer.py:453: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n", - " self.scaler = GradScaler(enabled=enable_scaler)\n", - "attempting to restore from: /home/gea3si/NewModTpWrapper/torchphysics/examples/wrapper/outputs_heat\n", - "optimizer checkpoint not found\n", - "model model0.0.pth not found\n", - "[step: 0] saved constraint results to outputs_heat\n", - "[step: 0] record constraint batch time: 1.811e-01s\n", - "[step: 0] saved validator results to outputs_heat\n", - "[step: 0] record validators time: 4.099e+01s\n", - "[step: 0] saved inferencer results to outputs_heat\n", - "[step: 0] record inferencers time: 1.543e+00s\n", - "[step: 0] saved checkpoint to /home/gea3si/NewModTpWrapper/torchphysics/examples/wrapper/outputs_heat\n", - "[step: 0] loss: 5.744e+02\n", - "Attempting cuda graph building, this may take a bit...\n", - "[step: 40] saved validator results to outputs_heat\n", - "[step: 40] record validators time: 4.181e+01s\n", - "[step: 50] saved inferencer results to outputs_heat\n", - "[step: 50] record inferencers time: 1.395e+00s\n", - "[step: 80] saved validator results to outputs_heat\n", - "[step: 80] record validators time: 4.790e+01s\n", - "[step: 100] saved inferencer results to outputs_heat\n", - "[step: 100] record inferencers time: 1.517e+00s\n", - "[step: 100] loss: 2.471e+03, time/iteration: 1.583e+03 ms\n", - "[step: 120] saved validator results to outputs_heat\n", - "[step: 120] record validators time: 5.824e+01s\n", - "[step: 150] saved inferencer results to outputs_heat\n", - "[step: 150] record inferencers time: 1.448e+00s\n", - "[step: 160] saved validator results to outputs_heat\n", - "[step: 160] record validators time: 5.856e+01s\n", - "[step: 200] saved constraint results to outputs_heat\n", - "[step: 200] record constraint batch time: 2.374e-01s\n", - "[step: 200] saved validator results to outputs_heat\n", - "[step: 200] record validators time: 4.454e+01s\n", - "[step: 200] saved inferencer results to outputs_heat\n", - "[step: 200] record inferencers time: 1.528e+00s\n", - "[step: 200] loss: 7.796e+02, time/iteration: 2.192e+03 ms\n", - "[step: 200] reached maximum training steps, finished training!\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "tp.wrapper.TPModulusWrapper(trainer,solver,lambda_weighting=[\"sdf\"],aggregator='LRAnnealing',confdir_name='conf_heat',outputdir_name='outputs_heat').train()" ] diff --git a/examples/wrapper/inverse-heat-equation-D-function-wrapper.ipynb b/examples/wrapper/inverse-heat-equation-D-function-wrapper.ipynb index 013e456d..79bbb137 100644 --- a/examples/wrapper/inverse-heat-equation-D-function-wrapper.ipynb +++ b/examples/wrapper/inverse-heat-equation-D-function-wrapper.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -78,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -103,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -128,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -149,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -181,81 +181,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (cuda), used: True\n", - "TPU available: False, using: 0 TPU cores\n", - "HPU available: False, using: 0 HPUs\n", - "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", - "\n", - " | Name | Type | Params | Mode \n", - "--------------------------------------------------------\n", - "0 | train_conditions | ModuleList | 26.8 K | train\n", - "1 | val_conditions | ModuleList | 0 | train\n", - "--------------------------------------------------------\n", - "26.8 K Trainable params\n", - "0 Non-trainable params\n", - "26.8 K Total params\n", - "0.107 Total estimated model params size (MB)\n", - "30 Modules in train mode\n", - "0 Modules in eval mode\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8911d9de5d25409ab344ea15b782b5a6", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: | | 0/? [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plot_sampler = tp.samplers.PlotSampler(plot_domain=domain_x, n_points=760, device='cuda')\n", "fig = tp.utils.plot(model_D, lambda D : D, plot_sampler, plot_type='contour_surface')" @@ -411,20 +248,9 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "def exact(x):\n", " return 5*torch.exp(-1/20.0 * ((x[:, :1] - 3)**2 + (x[:, 1:] - 3)**2))\n", @@ -441,20 +267,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "def error(D, x):\n", " return torch.abs(5*torch.exp(-1/20.0 * ((x[:, :1] - 3)**2 + (x[:, 1:] - 3)**2)) - D)\n", @@ -464,20 +279,9 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "anim_sampler = tp.samplers.AnimationSampler(domain_x, domain_t, 200, n_points=760)\n", "fig, anim = tp.utils.animate(model_u, lambda u: u, anim_sampler, ani_speed=10, ani_type='contour_surface')\n", @@ -504,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -519,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -539,7 +343,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -557,52 +361,9 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (cuda), used: True\n", - "TPU available: False, using: 0 TPU cores\n", - "HPU available: False, using: 0 HPUs\n", - "/home/gea3si/.conda/envs/mod_tp/lib/python3.10/site-packages/modulus/sym/hydra/utils.py:150: UserWarning: \n", - "The version_base parameter is not specified.\n", - "Please specify a compatability version level, or None.\n", - "Will assume defaults for version 1.1\n", - " hydra.initialize(\n", - "Setting JobRuntime:name=UNKNOWN_NAME\n", - "Setting JobRuntime:name=app\n", - "/home/gea3si/.conda/envs/mod_tp/lib/python3.10/site-packages/modulus/sym/trainer.py:453: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n", - " self.scaler = GradScaler(enabled=enable_scaler)\n", - "attempting to restore from: /home/gea3si/NewModTpWrapper/torchphysics/examples/wrapper/outputs\n", - "optimizer checkpoint not found\n", - "model model0.0.pth not found\n", - "model model1.0.pth not found\n", - "[step: 0] saved constraint results to outputs\n", - "[step: 0] record constraint batch time: 1.765e-01s\n", - "[step: 0] saved checkpoint to /home/gea3si/NewModTpWrapper/torchphysics/examples/wrapper/outputs\n", - "[step: 0] loss: 2.444e+07\n", - "Attempting cuda graph building, this may take a bit...\n", - "[step: 100] loss: 1.170e+07, time/iteration: 2.093e+02 ms\n", - "[step: 200] saved constraint results to outputs\n", - "[step: 200] record constraint batch time: 1.608e-01s\n", - "[step: 200] loss: 9.220e+06, time/iteration: 1.023e+02 ms\n", - "[step: 200] reached maximum training steps, finished training!\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "optim = tp.OptimizerSetting(optimizer_class=torch.optim.Adam, lr=0.001)\n", "\n", @@ -628,47 +389,9 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (cuda), used: True\n", - "TPU available: False, using: 0 TPU cores\n", - "HPU available: False, using: 0 HPUs\n", - "/home/gea3si/.conda/envs/mod_tp/lib/python3.10/site-packages/modulus/sym/hydra/utils.py:150: UserWarning: \n", - "The version_base parameter is not specified.\n", - "Please specify a compatability version level, or None.\n", - "Will assume defaults for version 1.1\n", - " hydra.initialize(\n", - "Setting JobRuntime:name=app\n", - "attempting to restore from: /home/gea3si/NewModTpWrapper/torchphysics/examples/wrapper/outputs\n", - "optimizer checkpoint not found\n", - "model model0.0.pth not found\n", - "model model1.0.pth not found\n", - "lbfgs optimizer selected. Setting max_steps to 0\n", - "[step: 0] lbfgs optimization in running\n", - "lbfgs optimization completed after 200 steps\n", - "[step: 0] saved constraint results to outputs\n", - "[step: 0] record constraint batch time: 1.846e-01s\n", - "[step: 0] saved checkpoint to /home/gea3si/NewModTpWrapper/torchphysics/examples/wrapper/outputs\n", - "[step: 0] loss: 9.196e+06\n", - "[step: 0] reached maximum training steps, finished training!\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "optim = tp.OptimizerSetting(optimizer_class=torch.optim.LBFGS, lr=0.5, \n", " optimizer_args={'max_iter': 200,#10000, # number of training steps\n", @@ -690,20 +413,9 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plot_sampler = tp.samplers.PlotSampler(plot_domain=domain_x, n_points=760, device='cuda')\n", "fig = tp.utils.plot(model_D, lambda D : D, plot_sampler, plot_type='contour_surface')" @@ -711,20 +423,9 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "def error(D, x):\n", " return torch.abs(5*torch.exp(-1/20.0 * ((x[:, :1] - 3)**2 + (x[:, 1:] - 3)**2)) - D)\n", @@ -734,20 +435,9 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "anim_sampler = tp.samplers.AnimationSampler(domain_x, domain_t, 200, n_points=760)\n", "fig, anim = tp.utils.animate(model_u, lambda u: u, anim_sampler, ani_speed=10, ani_type='contour_surface')\n", From bd220efad5ebb4c9e010a3929218ffe94f90f167 Mon Sep 17 00:00:00 2001 From: Daniel Kreuter Date: Fri, 27 Sep 2024 08:49:40 +0200 Subject: [PATCH 7/8] add modulus to setup.cfg --- setup.cfg | 1 + src/torchphysics/utils/differentialoperators.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index b7912acd..88c59ae9 100644 --- a/setup.cfg +++ b/setup.cfg @@ -48,6 +48,7 @@ package_dir = install_requires = torch>=2.0.0, <=2.4 pytorch-lightning>=2.0.0 + # nvidia-modulus numpy>=1.20.2, <2.0 matplotlib>=3.0.0 scipy>=1.6.3 diff --git a/src/torchphysics/utils/differentialoperators.py b/src/torchphysics/utils/differentialoperators.py index 588e43a8..0bf50853 100644 --- a/src/torchphysics/utils/differentialoperators.py +++ b/src/torchphysics/utils/differentialoperators.py @@ -1,6 +1,6 @@ """File contains differentialoperators -NOTE: We aim to make the computation of differential operaotrs more efficient +NOTE: We aim to make the computation of differential operators more efficient by building an intelligent framework that is able to keep already computed derivatives and therefore make the computations more efficient. """ From e6ef7946a12150d7e429d2cecfa5f41f772c37b4 Mon Sep 17 00:00:00 2001 From: Daniel Kreuter Date: Fri, 27 Sep 2024 08:51:49 +0200 Subject: [PATCH 8/8] uncommit Signed-off-by: Daniel Kreuter --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 88c59ae9..0e2b76f8 100644 --- a/setup.cfg +++ b/setup.cfg @@ -48,7 +48,7 @@ package_dir = install_requires = torch>=2.0.0, <=2.4 pytorch-lightning>=2.0.0 - # nvidia-modulus + nvidia-modulus numpy>=1.20.2, <2.0 matplotlib>=3.0.0 scipy>=1.6.3

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