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neuraloperator · Aug 28, 2024 · 6d1bca0 · 6d1bca0
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diff --git a/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip b/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
diff --git a/dev/_downloads/082e73328a5caf8c1fe9ad7fe05cf68f/plot_incremental_FNO_darcy.ipynb b/dev/_downloads/082e73328a5caf8c1fe9ad7fe05cf68f/plot_incremental_FNO_darcy.ipynb
@@ -197,7 +197,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.14"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

diff --git a/dev/_downloads/0ac9f102e4f1903984e3b4a7d517c88f/plot_darcy_flow.ipynb b/dev/_downloads/0ac9f102e4f1903984e3b4a7d517c88f/plot_darcy_flow.ipynb
@@ -78,7 +78,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.14"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

diff --git a/dev/_downloads/0d78e075dd52a34e158d7f5f710dfe89/plot_incremental_FNO_darcy.zip b/dev/_downloads/0d78e075dd52a34e158d7f5f710dfe89/plot_incremental_FNO_darcy.zip
diff --git a/dev/_downloads/1a3050d57a180b92b424ce128dfe1d36/plot_FNO_darcy.py b/dev/_downloads/1a3050d57a180b92b424ce128dfe1d36/plot_FNO_darcy.py
@@ -1,18 +1,19 @@
 """
-Training an FNO on Darcy-Flow
+Training a TFNO on Darcy-Flow
 =============================
 
 In this example, we demonstrate how to use the small Darcy-Flow example we ship with the package
-to train a Fourier-Neural Operator
+to train a Tensorized Fourier-Neural Operator
 """
 
 # %%
 # 
 
+
 import torch
 import matplotlib.pyplot as plt
 import sys
-from neuralop.models import FNO
+from neuralop.models import TFNO
 from neuralop import Trainer
 from neuralop.training import AdamW
 from neuralop.data.datasets import load_darcy_flow_small
@@ -33,13 +34,9 @@
 
 
 # %%
-# We create a simple FNO model
+# We create a tensorized FNO model
 
-model = FNO(n_modes=(16, 16),
-             in_channels=1, 
-             out_channels=1,
-             hidden_channels=32, 
-             projection_channels=64)
+model = TFNO(n_modes=(16, 16), in_channels=1, hidden_channels=32, projection_channels=64, factorization='tucker', rank=0.42)
 model = model.to(device)
 
 n_params = count_model_params(model)

diff --git a/dev/_downloads/20c43dd37baf603889c4dc23e93bdb60/plot_count_flops.zip b/dev/_downloads/20c43dd37baf603889c4dc23e93bdb60/plot_count_flops.zip
diff --git a/dev/_downloads/2a3ecbdce9fd535c53d44cc373f6a228/checkpoint_FNO_darcy.py b/dev/_downloads/2a3ecbdce9fd535c53d44cc373f6a228/checkpoint_FNO_darcy.py
@@ -33,14 +33,7 @@
 # %%
 # We create a tensorized FNO model
 
-model = TFNO(n_modes=(16, 16),
-             in_channels=1, 
-             out_channels=1, 
-             hidden_channels=32, 
-             projection_channels=64, 
-             factorization='tucker', 
-             rank=0.42)
-
+model = TFNO(n_modes=(16, 16), in_channels=1, hidden_channels=32, projection_channels=64, factorization='tucker', rank=0.42)
 model = model.to(device)
 
 n_params = count_model_params(model)

diff --git a/dev/_downloads/3864a2d85c7ce11adeac9580559229ab/plot_darcy_flow.zip b/dev/_downloads/3864a2d85c7ce11adeac9580559229ab/plot_darcy_flow.zip
diff --git a/dev/_downloads/3faf9d2eaee5cc8e9f1c631c002ce544/plot_darcy_flow_spectrum.zip b/dev/_downloads/3faf9d2eaee5cc8e9f1c631c002ce544/plot_darcy_flow_spectrum.zip
diff --git a/dev/_downloads/52640fe09fbb5b08e5a2370e57b3b066/checkpoint_FNO_darcy.ipynb b/dev/_downloads/52640fe09fbb5b08e5a2370e57b3b066/checkpoint_FNO_darcy.ipynb
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "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()"
+        "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()"
       ]
     },
     {
@@ -154,7 +154,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.14"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

diff --git a/dev/_downloads/5e60095ce99919773daa83384f767e02/plot_SFNO_swe.zip b/dev/_downloads/5e60095ce99919773daa83384f767e02/plot_SFNO_swe.zip
diff --git a/dev/_downloads/645da00b8fbbb9bb5cae877fd0f31635/plot_FNO_darcy.zip b/dev/_downloads/645da00b8fbbb9bb5cae877fd0f31635/plot_FNO_darcy.zip
diff --git a/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
diff --git a/dev/_downloads/7296405f6df7c2cfe184e9b258cee33e/checkpoint_FNO_darcy.zip b/dev/_downloads/7296405f6df7c2cfe184e9b258cee33e/checkpoint_FNO_darcy.zip
diff --git a/dev/_downloads/84c435865e4e2910253a980881498782/plot_count_flops.ipynb b/dev/_downloads/84c435865e4e2910253a980881498782/plot_count_flops.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "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)"
+        "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)"
       ]
     },
     {
@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.14"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

diff --git a/dev/_downloads/969baa48c90c32befa7c7d2590c8ca42/darcy_data_processor.ipynb b/dev/_downloads/969baa48c90c32befa7c7d2590c8ca42/darcy_data_processor.ipynb
diff --git a/dev/_downloads/af3a515d2684655c6a5a8e0df87a4cf9/plot_SFNO_swe.ipynb b/dev/_downloads/af3a515d2684655c6a5a8e0df87a4cf9/plot_SFNO_swe.ipynb
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "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()"
+        "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()"
       ]
     },
     {
@@ -172,7 +172,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.14"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

diff --git a/dev/_downloads/b84984d89c00aa83f7c56c96a61b8aee/darcy_data_processor.py b/dev/_downloads/b84984d89c00aa83f7c56c96a61b8aee/darcy_data_processor.py
diff --git a/dev/_downloads/be42c4c413e9b89016fa3a4984cb9758/plot_SFNO_swe.py b/dev/_downloads/be42c4c413e9b89016fa3a4984cb9758/plot_SFNO_swe.py
@@ -31,12 +31,7 @@
 # %%
 # We create a tensorized FNO model
 
-model = SFNO(n_modes=(32, 32),
-             in_channels=3,
-             out_channels=3,
-             hidden_channels=32,
-             projection_channels=64,
-             factorization='dense')
+model = SFNO(n_modes=(32, 32), in_channels=3, out_channels=3, hidden_channels=32, projection_channels=64, factorization='dense')
 model = model.to(device)
 
 n_params = count_model_params(model)

diff --git a/dev/_downloads/c628421a5214dc8f1b3bde8be930715f/plot_darcy_flow_spectrum.ipynb b/dev/_downloads/c628421a5214dc8f1b3bde8be930715f/plot_darcy_flow_spectrum.ipynb
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.14"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

diff --git a/dev/_downloads/cefc537c5730a6b3e916b83c1fd313d6/plot_UNO_darcy.zip b/dev/_downloads/cefc537c5730a6b3e916b83c1fd313d6/plot_UNO_darcy.zip
diff --git a/dev/_downloads/de69282d3144c5a2b675c6f6338237c1/plot_count_flops.py b/dev/_downloads/de69282d3144c5a2b675c6f6338237c1/plot_count_flops.py
@@ -19,13 +19,13 @@
 device = 'cpu'
 
 fno = FNO(n_modes=(64,64), 
-          in_channels=1, 
+          in_channels=3, 
           out_channels=1, 
           hidden_channels=64, 
           projection_channels=64)
 
 batch_size = 4
-model_input = torch.randn(batch_size, 1, 128, 128)
+model_input = torch.randn(batch_size, 3, 128, 128)
 
 
 with FlopTensorDispatchMode(fno) as ftdm:

diff --git a/dev/_downloads/e81e8f640b2a3cda84c542bbc8f36a54/plot_FNO_darcy.ipynb b/dev/_downloads/e81e8f640b2a3cda84c542bbc8f36a54/plot_FNO_darcy.ipynb
@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\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"
+        "\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"
       ]
     },
     {
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "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'"
+        "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'"
       ]
     },
     {
@@ -40,7 +40,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We create a simple FNO model\n\n"
+        "We create a tensorized FNO model\n\n"
       ]
     },
     {
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "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()"
+        "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()"
       ]
     },
     {
@@ -172,7 +172,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.14"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

diff --git a/dev/_downloads/ecc34f03fb30bee651bda959149422c4/plot_UNO_darcy.ipynb b/dev/_downloads/ecc34f03fb30bee651bda959149422c4/plot_UNO_darcy.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "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()"
+        "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()"
       ]
     },
     {
@@ -154,7 +154,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.14"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

diff --git a/dev/_downloads/f9c1e50d48828a746410d5400feb61f7/plot_UNO_darcy.py b/dev/_downloads/f9c1e50d48828a746410d5400feb61f7/plot_UNO_darcy.py
@@ -31,18 +31,11 @@
         test_batch_sizes=[32, 32],
 )
 
-model = UNO(in_channels=1, 
-            out_channels=1, 
-            hidden_channels=64, 
-            projection_channels=64,
-            uno_out_channels=[32,64,64,64,32],
-            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]],
-            horizontal_skips_map=None,
-            channel_mlp_skip="linear",
-            n_layers = 5,
-            domain_padding=0.2)
 
+
+model = UNO(in_channels=1, out_channels=1, hidden_channels=64, projection_channels=64,uno_out_channels = [32,64,64,64,32], \
+            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]],\
+            horizontal_skips_map = None, n_layers = 5, domain_padding = 0.2)
 model = model.to(device)
 
 n_params = count_model_params(model)

diff --git a/dev/_images/sphx_glr_plot_FNO_darcy_001.png b/dev/_images/sphx_glr_plot_FNO_darcy_001.png
diff --git a/dev/_images/sphx_glr_plot_FNO_darcy_thumb.png b/dev/_images/sphx_glr_plot_FNO_darcy_thumb.png
diff --git a/dev/_images/sphx_glr_plot_SFNO_swe_001.png b/dev/_images/sphx_glr_plot_SFNO_swe_001.png
diff --git a/dev/_images/sphx_glr_plot_SFNO_swe_thumb.png b/dev/_images/sphx_glr_plot_SFNO_swe_thumb.png
diff --git a/dev/_images/sphx_glr_plot_UNO_darcy_001.png b/dev/_images/sphx_glr_plot_UNO_darcy_001.png
diff --git a/dev/_images/sphx_glr_plot_UNO_darcy_thumb.png b/dev/_images/sphx_glr_plot_UNO_darcy_thumb.png
diff --git a/dev/_images/sphx_glr_plot_count_flops_thumb.png b/dev/_images/sphx_glr_plot_count_flops_thumb.png
diff --git a/dev/_images/sphx_glr_plot_darcy_flow_001.png b/dev/_images/sphx_glr_plot_darcy_flow_001.png
diff --git a/dev/_images/sphx_glr_plot_darcy_flow_spectrum_001.png b/dev/_images/sphx_glr_plot_darcy_flow_spectrum_001.png
diff --git a/dev/_images/sphx_glr_plot_incremental_FNO_darcy_001.png b/dev/_images/sphx_glr_plot_incremental_FNO_darcy_001.png
diff --git a/dev/_images/sphx_glr_plot_incremental_FNO_darcy_thumb.png b/dev/_images/sphx_glr_plot_incremental_FNO_darcy_thumb.png
diff --git a/dev/_modules/index.html b/dev/_modules/index.html
@@ -16,7 +16,7 @@
 
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=a746c00c" />
     <link rel="stylesheet" type="text/css" href="../_static/tensorly_style.css?v=a02e9698" />
-    <link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css?v=61a4c737" />
+    <link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-dataframe.css?v=2082cf3c" />
     <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-rendered-html.css?v=1277b6f3" />
@@ -115,7 +115,6 @@
   <h1>All modules for which code is available</h1>
 <ul><li><a href="neuralop/data/datasets/darcy.html">neuralop.data.datasets.darcy</a></li>
 <li><a href="neuralop/data/transforms/data_processors.html">neuralop.data.transforms.data_processors</a></li>
-<li><a href="neuralop/layers/embeddings.html">neuralop.layers.embeddings</a></li>
 <li><a href="neuralop/layers/integral_transform.html">neuralop.layers.integral_transform</a></li>
 <li><a href="neuralop/layers/neighbor_search.html">neuralop.layers.neighbor_search</a></li>
 <li><a href="neuralop/layers/padding.html">neuralop.layers.padding</a></li>

diff --git a/dev/_modules/neuralop/data/datasets/darcy.html b/dev/_modules/neuralop/data/datasets/darcy.html
@@ -16,7 +16,7 @@
 
     <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css?v=a746c00c" />
     <link rel="stylesheet" type="text/css" href="../../../../_static/tensorly_style.css?v=a02e9698" />
-    <link rel="stylesheet" type="text/css" href="../../../../_static/sg_gallery.css?v=61a4c737" />
+    <link rel="stylesheet" type="text/css" href="../../../../_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="../../../../_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="../../../../_static/sg_gallery-dataframe.css?v=2082cf3c" />
     <link rel="stylesheet" type="text/css" href="../../../../_static/sg_gallery-rendered-html.css?v=1277b6f3" />