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‎dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

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‎dev/_downloads/082e73328a5caf8c1fe9ad7fe05cf68f/plot_incremental_FNO_darcy.ipynb

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"name": "python",
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"version": "3.10.14"
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"version": "3.9.19"
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}
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‎dev/_downloads/0ac9f102e4f1903984e3b4a7d517c88f/plot_darcy_flow.ipynb

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"name": "python",
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"version": "3.10.14"
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"version": "3.9.19"
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}
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},
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"nbformat": 4,

‎dev/_downloads/0d78e075dd52a34e158d7f5f710dfe89/plot_incremental_FNO_darcy.zip

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‎dev/_downloads/1a3050d57a180b92b424ce128dfe1d36/plot_FNO_darcy.py

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"""
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Training an FNO on Darcy-Flow
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Training a TFNO on Darcy-Flow
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=============================
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In this example, we demonstrate how to use the small Darcy-Flow example we ship with the package
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to train a Fourier-Neural Operator
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to train a Tensorized Fourier-Neural Operator
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"""
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# %%
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#
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import torch
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import matplotlib.pyplot as plt
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import sys
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from neuralop.models import FNO
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from neuralop.models import TFNO
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from neuralop import Trainer
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from neuralop.training import AdamW
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from neuralop.data.datasets import load_darcy_flow_small
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# %%
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# We create a simple FNO model
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# We create a tensorized FNO model
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model = FNO(n_modes=(16, 16),
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in_channels=1,
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out_channels=1,
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hidden_channels=32,
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projection_channels=64)
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model = TFNO(n_modes=(16, 16), in_channels=1, hidden_channels=32, projection_channels=64, factorization='tucker', rank=0.42)
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model = model.to(device)
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n_params = count_model_params(model)

‎dev/_downloads/20c43dd37baf603889c4dc23e93bdb60/plot_count_flops.zip

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‎dev/_downloads/2a3ecbdce9fd535c53d44cc373f6a228/checkpoint_FNO_darcy.py

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# %%
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# We create a tensorized FNO model
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model = TFNO(n_modes=(16, 16),
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in_channels=1,
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out_channels=1,
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hidden_channels=32,
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projection_channels=64,
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factorization='tucker',
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rank=0.42)
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model = TFNO(n_modes=(16, 16), in_channels=1, hidden_channels=32, projection_channels=64, factorization='tucker', rank=0.42)
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model = model.to(device)
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n_params = count_model_params(model)

‎dev/_downloads/3864a2d85c7ce11adeac9580559229ab/plot_darcy_flow.zip

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‎dev/_downloads/3faf9d2eaee5cc8e9f1c631c002ce544/plot_darcy_flow_spectrum.zip

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‎dev/_downloads/52640fe09fbb5b08e5a2370e57b3b066/checkpoint_FNO_darcy.ipynb

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},
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"outputs": [],
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"source": [
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"model = TFNO(n_modes=(16, 16),\n in_channels=1, \n out_channels=1, \n hidden_channels=32, \n projection_channels=64, \n factorization='tucker', \n rank=0.42)\n\nmodel = model.to(device)\n\nn_params = count_model_params(model)\nprint(f'\\nOur model has {n_params} parameters.')\nsys.stdout.flush()"
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"model = TFNO(n_modes=(16, 16), in_channels=1, hidden_channels=32, projection_channels=64, factorization='tucker', rank=0.42)\nmodel = model.to(device)\n\nn_params = count_model_params(model)\nprint(f'\\nOur model has {n_params} parameters.')\nsys.stdout.flush()"
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]
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.14"
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"version": "3.9.19"
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}
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},
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"nbformat": 4,

‎dev/_downloads/5e60095ce99919773daa83384f767e02/plot_SFNO_swe.zip

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‎dev/_downloads/645da00b8fbbb9bb5cae877fd0f31635/plot_FNO_darcy.zip

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‎dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

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‎dev/_downloads/7296405f6df7c2cfe184e9b258cee33e/checkpoint_FNO_darcy.zip

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‎dev/_downloads/84c435865e4e2910253a980881498782/plot_count_flops.ipynb

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},
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"outputs": [],
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"source": [
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"from copy import deepcopy\nimport torch\nfrom torchtnt.utils.flops import FlopTensorDispatchMode\n\nfrom neuralop.models import FNO\n\ndevice = 'cpu'\n\nfno = FNO(n_modes=(64,64), \n in_channels=1, \n out_channels=1, \n hidden_channels=64, \n projection_channels=64)\n\nbatch_size = 4\nmodel_input = torch.randn(batch_size, 1, 128, 128)\n\n\nwith FlopTensorDispatchMode(fno) as ftdm:\n # count forward flops\n res = fno(model_input).mean()\n fno_forward_flops = deepcopy(ftdm.flop_counts)\n \n ftdm.reset()\n res.backward()\n fno_backward_flops = deepcopy(ftdm.flop_counts)"
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"from copy import deepcopy\nimport torch\nfrom torchtnt.utils.flops import FlopTensorDispatchMode\n\nfrom neuralop.models import FNO\n\ndevice = 'cpu'\n\nfno = FNO(n_modes=(64,64), \n in_channels=3, \n out_channels=1, \n hidden_channels=64, \n projection_channels=64)\n\nbatch_size = 4\nmodel_input = torch.randn(batch_size, 3, 128, 128)\n\n\nwith FlopTensorDispatchMode(fno) as ftdm:\n # count forward flops\n res = fno(model_input).mean()\n fno_forward_flops = deepcopy(ftdm.flop_counts)\n \n ftdm.reset()\n res.backward()\n fno_backward_flops = deepcopy(ftdm.flop_counts)"
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]
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.14"
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"version": "3.9.19"
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}
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},
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"nbformat": 4,

‎dev/_downloads/969baa48c90c32befa7c7d2590c8ca42/darcy_data_processor.ipynb

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‎dev/_downloads/af3a515d2684655c6a5a8e0df87a4cf9/plot_SFNO_swe.ipynb

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},
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"outputs": [],
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"source": [
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"model = SFNO(n_modes=(32, 32),\n in_channels=3,\n out_channels=3,\n hidden_channels=32,\n projection_channels=64,\n factorization='dense')\nmodel = model.to(device)\n\nn_params = count_model_params(model)\nprint(f'\\nOur model has {n_params} parameters.')\nsys.stdout.flush()"
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"model = SFNO(n_modes=(32, 32), in_channels=3, out_channels=3, hidden_channels=32, projection_channels=64, factorization='dense')\nmodel = model.to(device)\n\nn_params = count_model_params(model)\nprint(f'\\nOur model has {n_params} parameters.')\nsys.stdout.flush()"
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"name": "python",
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"version": "3.10.14"
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"version": "3.9.19"
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}
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},
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‎dev/_downloads/b84984d89c00aa83f7c56c96a61b8aee/darcy_data_processor.py

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‎dev/_downloads/be42c4c413e9b89016fa3a4984cb9758/plot_SFNO_swe.py

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# %%
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# We create a tensorized FNO model
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model = SFNO(n_modes=(32, 32),
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in_channels=3,
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out_channels=3,
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hidden_channels=32,
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projection_channels=64,
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factorization='dense')
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model = SFNO(n_modes=(32, 32), in_channels=3, out_channels=3, hidden_channels=32, projection_channels=64, factorization='dense')
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model = model.to(device)
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n_params = count_model_params(model)

‎dev/_downloads/c628421a5214dc8f1b3bde8be930715f/plot_darcy_flow_spectrum.ipynb

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"version": "3.9.19"
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}
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‎dev/_downloads/cefc537c5730a6b3e916b83c1fd313d6/plot_UNO_darcy.zip

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‎dev/_downloads/de69282d3144c5a2b675c6f6338237c1/plot_count_flops.py

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device = 'cpu'
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fno = FNO(n_modes=(64,64),
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model_input = torch.randn(batch_size, 1, 128, 128)
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model_input = torch.randn(batch_size, 3, 128, 128)
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with FlopTensorDispatchMode(fno) as ftdm:

‎dev/_downloads/e81e8f640b2a3cda84c542bbc8f36a54/plot_FNO_darcy.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n# Training an FNO on Darcy-Flow\n\nIn this example, we demonstrate how to use the small Darcy-Flow example we ship with the package\nto train a Fourier-Neural Operator\n"
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"\n# Training a TFNO on Darcy-Flow\n\nIn this example, we demonstrate how to use the small Darcy-Flow example we ship with the package\nto train a Tensorized Fourier-Neural Operator\n"
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{
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},
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"outputs": [],
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"source": [
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"import torch\nimport matplotlib.pyplot as plt\nimport sys\nfrom neuralop.models import FNO\nfrom neuralop import Trainer\nfrom neuralop.training import AdamW\nfrom neuralop.data.datasets import load_darcy_flow_small\nfrom neuralop.utils import count_model_params\nfrom neuralop import LpLoss, H1Loss\n\ndevice = 'cpu'"
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"import torch\nimport matplotlib.pyplot as plt\nimport sys\nfrom neuralop.models import TFNO\nfrom neuralop import Trainer\nfrom neuralop.training import AdamW\nfrom neuralop.data.datasets import load_darcy_flow_small\nfrom neuralop.utils import count_model_params\nfrom neuralop import LpLoss, H1Loss\n\ndevice = 'cpu'"
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We create a simple FNO model\n\n"
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"We create a tensorized FNO model\n\n"
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]
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{
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},
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"outputs": [],
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"source": [
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"model = FNO(n_modes=(16, 16),\n in_channels=1, \n out_channels=1,\n hidden_channels=32, \n projection_channels=64)\nmodel = model.to(device)\n\nn_params = count_model_params(model)\nprint(f'\\nOur model has {n_params} parameters.')\nsys.stdout.flush()"
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"model = TFNO(n_modes=(16, 16), in_channels=1, hidden_channels=32, projection_channels=64, factorization='tucker', rank=0.42)\nmodel = model.to(device)\n\nn_params = count_model_params(model)\nprint(f'\\nOur model has {n_params} parameters.')\nsys.stdout.flush()"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.14"
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"version": "3.9.19"
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}
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},
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"nbformat": 4,

‎dev/_downloads/ecc34f03fb30bee651bda959149422c4/plot_UNO_darcy.ipynb

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},
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"outputs": [],
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"train_loader, test_loaders, data_processor = load_darcy_flow_small(\n n_train=1000, batch_size=32, \n test_resolutions=[16, 32], n_tests=[100, 50],\n test_batch_sizes=[32, 32],\n)\n\nmodel = UNO(in_channels=1, \n out_channels=1, \n hidden_channels=64, \n projection_channels=64,\n uno_out_channels=[32,64,64,64,32],\n uno_n_modes=[[16,16],[8,8],[8,8],[8,8],[16,16]],\n uno_scalings=[[1.0,1.0],[0.5,0.5],[1,1],[2,2],[1,1]],\n horizontal_skips_map=None,\n channel_mlp_skip=\"linear\",\n n_layers = 5,\n domain_padding=0.2)\n\nmodel = model.to(device)\n\nn_params = count_model_params(model)\nprint(f'\\nOur model has {n_params} parameters.')\nsys.stdout.flush()"
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"train_loader, test_loaders, data_processor = load_darcy_flow_small(\n n_train=1000, batch_size=32, \n test_resolutions=[16, 32], n_tests=[100, 50],\n test_batch_sizes=[32, 32],\n)\n\n\n\nmodel = UNO(in_channels=1, out_channels=1, hidden_channels=64, projection_channels=64,uno_out_channels = [32,64,64,64,32], \\\n uno_n_modes= [[16,16],[8,8],[8,8],[8,8],[16,16]], uno_scalings= [[1.0,1.0],[0.5,0.5],[1,1],[2,2],[1,1]],\\\n horizontal_skips_map = None, n_layers = 5, domain_padding = 0.2)\nmodel = model.to(device)\n\nn_params = count_model_params(model)\nprint(f'\\nOur model has {n_params} parameters.')\nsys.stdout.flush()"
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{
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"name": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.19"
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}
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},
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‎dev/_downloads/f9c1e50d48828a746410d5400feb61f7/plot_UNO_darcy.py

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test_batch_sizes=[32, 32],
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)
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model = UNO(in_channels=1,
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hidden_channels=64,
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projection_channels=64,
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uno_n_modes=[[16,16],[8,8],[8,8],[8,8],[16,16]],
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uno_scalings=[[1.0,1.0],[0.5,0.5],[1,1],[2,2],[1,1]],
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horizontal_skips_map=None,
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channel_mlp_skip="linear",
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n_layers = 5,
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domain_padding=0.2)
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model = UNO(in_channels=1, out_channels=1, hidden_channels=64, projection_channels=64,uno_out_channels = [32,64,64,64,32], \
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uno_n_modes= [[16,16],[8,8],[8,8],[8,8],[16,16]], uno_scalings= [[1.0,1.0],[0.5,0.5],[1,1],[2,2],[1,1]],\
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horizontal_skips_map = None, n_layers = 5, domain_padding = 0.2)
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model = model.to(device)
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n_params = count_model_params(model)

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‎dev/_modules/index.html

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<link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=a746c00c" />
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<link rel="stylesheet" type="text/css" href="../_static/tensorly_style.css?v=a02e9698" />
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<link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css?v=61a4c737" />
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<link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css?v=d2d258e8" />
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<link rel="stylesheet" type="text/css" href="../_static/sg_gallery-binder.css?v=f4aeca0c" />
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<h1>All modules for which code is available</h1>
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<ul><li><a href="neuralop/data/datasets/darcy.html">neuralop.data.datasets.darcy</a></li>
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<li><a href="neuralop/data/transforms/data_processors.html">neuralop.data.transforms.data_processors</a></li>
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<li><a href="neuralop/layers/embeddings.html">neuralop.layers.embeddings</a></li>
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<li><a href="neuralop/layers/integral_transform.html">neuralop.layers.integral_transform</a></li>
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<li><a href="neuralop/layers/neighbor_search.html">neuralop.layers.neighbor_search</a></li>
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<li><a href="neuralop/layers/padding.html">neuralop.layers.padding</a></li>

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