Github action: auto-update.

Author: github-actions[bot]

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -123,7 +123,7 @@ <h1>All modules for which code is available</h1>
 <li><a href="neuralop/layers/embeddings.html">neuralop.layers.embeddings</a></li>
 <li><a href="neuralop/layers/gno_block.html">neuralop.layers.gno_block</a></li>
 <li><a href="neuralop/layers/integral_transform.html">neuralop.layers.integral_transform</a></li>
-<li><a href="neuralop/layers/local_fno_block.html">neuralop.layers.local_fno_block</a></li>
+<li><a href="neuralop/layers/local_no_block.html">neuralop.layers.local_no_block</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>
 <li><a href="neuralop/layers/skip_connections.html">neuralop.layers.skip_connections</a></li>
@@ -133,6 +133,7 @@ <h1>All modules for which code is available</h1>
 <li><a href="neuralop/models/base_model.html">neuralop.models.base_model</a></li>
 <li><a href="neuralop/models/fno.html">neuralop.models.fno</a></li>
 <li><a href="neuralop/models/gino.html">neuralop.models.gino</a></li>
+<li><a href="neuralop/models/local_no.html">neuralop.models.local_no</a></li>
 <li><a href="neuralop/models/uno.html">neuralop.models.uno</a></li>
 <li><a href="neuralop/training/incremental.html">neuralop.training.incremental</a></li>
 <li><a href="neuralop/training/trainer.html">neuralop.training.trainer</a></li>

dev/_downloads/0d78e075dd52a34e158d7f5f710dfe89/plot_incremental_FNO_darcy.zip

@@ -357,7 +357,7 @@ in order to compute the convolved image, we need to first bring it into the righ
  .. code-block:: none
 
 
-    <matplotlib.colorbar.Colorbar object at 0x7f4d01c0f110>
+    <matplotlib.colorbar.Colorbar object at 0x7f48a2bb3390>
 
 
 
@@ -448,7 +448,7 @@ in order to compute the convolved image, we need to first bring it into the righ
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 30.901 seconds)
+   **Total running time of the script:** (0 minutes 30.716 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_DISCO_convolutions.py:

dev/_downloads/20c43dd37baf603889c4dc23e93bdb60/plot_count_flops.zip

@@ -248,13 +248,13 @@ Training the model
     )
 
     ### SCHEDULER ###
-     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7f4d14dd25d0>
+     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7f48b5ded1d0>
 
     ### LOSSES ###
 
-     * Train: <neuralop.losses.data_losses.H1Loss object at 0x7f4d14dd34d0>
+     * Train: <neuralop.losses.data_losses.H1Loss object at 0x7f48b5dedbd0>
 
-     * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7f4d14dd34d0>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f4d14dd0cd0>}
+     * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7f48b5dedbd0>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f48b5dece10>}
 
 
 
@@ -311,22 +311,22 @@ Then train the model on our small Darcy-Flow dataset:
     Training on 1000 samples
     Testing on [50, 50] samples         on resolutions [16, 32].
     Raw outputs of shape torch.Size([32, 1, 16, 16])
-    [0] time=2.51, avg_loss=0.6956, train_err=21.7383
+    [0] time=2.48, avg_loss=0.6956, train_err=21.7383
     Eval: 16_h1=0.4298, 16_l2=0.3487, 32_h1=0.5847, 32_l2=0.3542
-    [3] time=2.46, avg_loss=0.2103, train_err=6.5705
+    [3] time=2.47, avg_loss=0.2103, train_err=6.5705
     Eval: 16_h1=0.2030, 16_l2=0.1384, 32_h1=0.5075, 32_l2=0.1774
-    [6] time=2.46, avg_loss=0.1911, train_err=5.9721
+    [6] time=2.47, avg_loss=0.1911, train_err=5.9721
     Eval: 16_h1=0.2099, 16_l2=0.1374, 32_h1=0.4907, 32_l2=0.1783
     [9] time=2.47, avg_loss=0.1410, train_err=4.4073
     Eval: 16_h1=0.2052, 16_l2=0.1201, 32_h1=0.5268, 32_l2=0.1615
     [12] time=2.47, avg_loss=0.1422, train_err=4.4434
     Eval: 16_h1=0.2131, 16_l2=0.1285, 32_h1=0.5413, 32_l2=0.1741
-    [15] time=2.46, avg_loss=0.1198, train_err=3.7424
+    [15] time=2.47, avg_loss=0.1198, train_err=3.7424
     Eval: 16_h1=0.1984, 16_l2=0.1137, 32_h1=0.5255, 32_l2=0.1569
     [18] time=2.47, avg_loss=0.1104, train_err=3.4502
     Eval: 16_h1=0.2039, 16_l2=0.1195, 32_h1=0.5062, 32_l2=0.1603
 
-    {'train_err': 2.9605126455426216, 'avg_loss': 0.0947364046573639, 'avg_lasso_loss': None, 'epoch_train_time': 2.4833866669999907}
+    {'train_err': 2.9605126455426216, 'avg_loss': 0.0947364046573639, 'avg_lasso_loss': None, 'epoch_train_time': 2.4658696660000032}
 
 
 
@@ -476,7 +476,7 @@ are other ways to scale the outputs of the FNO to train a true super-resolution
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 50.700 seconds)
+   **Total running time of the script:** (0 minutes 50.696 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_FNO_darcy.py:

dev/_downloads/3864a2d85c7ce11adeac9580559229ab/plot_darcy_flow.zip

@@ -234,13 +234,13 @@ Creating the losses
     )
 
     ### SCHEDULER ###
-     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7f4d14dad940>
+     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7f48b5dd5940>
 
     ### LOSSES ###
 
-     * Train: <neuralop.losses.data_losses.LpLoss object at 0x7f4d14daf380>
+     * Train: <neuralop.losses.data_losses.LpLoss object at 0x7f48b5dd7380>
 
-     * Test: {'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f4d14daf380>}
+     * Test: {'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f48b5dd7380>}
 
 
 
@@ -297,22 +297,22 @@ Train the model on the spherical SWE dataset
     Training on 200 samples
     Testing on [50, 50] samples         on resolutions [(32, 64), (64, 128)].
     Raw outputs of shape torch.Size([4, 3, 32, 64])
-    [0] time=3.52, avg_loss=2.5525, train_err=10.2100
-    Eval: (32, 64)_l2=1.6327, (64, 128)_l2=2.5739
-    [3] time=3.50, avg_loss=0.3994, train_err=1.5974
-    Eval: (32, 64)_l2=0.6303, (64, 128)_l2=2.5415
-    [6] time=3.41, avg_loss=0.2746, train_err=1.0982
-    Eval: (32, 64)_l2=0.4479, (64, 128)_l2=2.4590
-    [9] time=3.41, avg_loss=0.2362, train_err=0.9447
-    Eval: (32, 64)_l2=0.3426, (64, 128)_l2=2.4463
-    [12] time=3.41, avg_loss=0.2061, train_err=0.8244
-    Eval: (32, 64)_l2=0.3944, (64, 128)_l2=2.4336
-    [15] time=3.40, avg_loss=0.1675, train_err=0.6701
-    Eval: (32, 64)_l2=0.3104, (64, 128)_l2=2.4362
-    [18] time=3.44, avg_loss=0.1469, train_err=0.5876
-    Eval: (32, 64)_l2=0.2370, (64, 128)_l2=2.4253
+    [0] time=3.51, avg_loss=2.6651, train_err=10.6606
+    Eval: (32, 64)_l2=2.0387, (64, 128)_l2=2.3833
+    [3] time=3.50, avg_loss=0.3967, train_err=1.5867
+    Eval: (32, 64)_l2=0.4690, (64, 128)_l2=2.6671
+    [6] time=3.45, avg_loss=0.2665, train_err=1.0661
+    Eval: (32, 64)_l2=0.4195, (64, 128)_l2=2.5312
+    [9] time=3.46, avg_loss=0.2292, train_err=0.9166
+    Eval: (32, 64)_l2=0.3467, (64, 128)_l2=2.5203
+    [12] time=3.43, avg_loss=0.1856, train_err=0.7426
+    Eval: (32, 64)_l2=0.3028, (64, 128)_l2=2.4369
+    [15] time=3.48, avg_loss=0.1537, train_err=0.6149
+    Eval: (32, 64)_l2=0.2960, (64, 128)_l2=2.4150
+    [18] time=3.46, avg_loss=0.1403, train_err=0.5613
+    Eval: (32, 64)_l2=0.2902, (64, 128)_l2=2.4013
 
-    {'train_err': 0.5793274295330048, 'avg_loss': 0.1448318573832512, 'avg_lasso_loss': None, 'epoch_train_time': 3.4297402870000155}
+    {'train_err': 0.5318634355068207, 'avg_loss': 0.13296585887670517, 'avg_lasso_loss': None, 'epoch_train_time': 3.4797961209999926}
 
 
 
@@ -383,7 +383,7 @@ In practice we would train a Neural Operator on one or multiple GPUs
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 23.885 seconds)
+   **Total running time of the script:** (1 minutes 24.697 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_SFNO_swe.py:

dev/_downloads/3faf9d2eaee5cc8e9f1c631c002ce544/plot_darcy_flow_spectrum.zip

@@ -345,13 +345,13 @@ Creating the losses
     )
 
     ### SCHEDULER ###
-     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7f4d0fd5c7d0>
+     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7f48b4d44a50>
 
     ### LOSSES ###
 
-     * Train: <neuralop.losses.data_losses.H1Loss object at 0x7f4d14e7f380>
+     * Train: <neuralop.losses.data_losses.H1Loss object at 0x7f48b5ea7380>
 
-     * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7f4d14e7f380>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f4d0fd5ce10>}
+     * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7f48b5ea7380>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f48b4d447d0>}
 
 
 
@@ -410,22 +410,22 @@ Actually train the model on our small Darcy-Flow dataset
     Training on 1000 samples
     Testing on [50, 50] samples         on resolutions [16, 32].
     Raw outputs of shape torch.Size([32, 1, 16, 16])
-    [0] time=10.01, avg_loss=0.6591, train_err=20.5974
-    Eval: 16_h1=0.4321, 16_l2=0.3189, 32_h1=0.9655, 32_l2=0.5314
-    [3] time=9.93, avg_loss=0.2503, train_err=7.8226
-    Eval: 16_h1=0.2496, 16_l2=0.1610, 32_h1=0.8357, 32_l2=0.5342
-    [6] time=9.97, avg_loss=0.2127, train_err=6.6463
-    Eval: 16_h1=0.2489, 16_l2=0.1675, 32_h1=0.7890, 32_l2=0.5255
-    [9] time=9.99, avg_loss=0.1983, train_err=6.1968
-    Eval: 16_h1=0.2541, 16_l2=0.1658, 32_h1=0.7831, 32_l2=0.5347
-    [12] time=9.98, avg_loss=0.1890, train_err=5.9059
-    Eval: 16_h1=0.2446, 16_l2=0.1492, 32_h1=0.7882, 32_l2=0.4716
-    [15] time=9.97, avg_loss=0.1725, train_err=5.3913
-    Eval: 16_h1=0.3038, 16_l2=0.2021, 32_h1=0.7797, 32_l2=0.5113
-    [18] time=10.03, avg_loss=0.1466, train_err=4.5810
-    Eval: 16_h1=0.2422, 16_l2=0.1467, 32_h1=0.7684, 32_l2=0.4630
+    [0] time=10.02, avg_loss=0.6185, train_err=19.3296
+    Eval: 16_h1=0.5203, 16_l2=0.3428, 32_h1=0.9230, 32_l2=0.6163
+    [3] time=9.96, avg_loss=0.2452, train_err=7.6623
+    Eval: 16_h1=0.2633, 16_l2=0.1671, 32_h1=0.8157, 32_l2=0.5749
+    [6] time=9.96, avg_loss=0.2192, train_err=6.8498
+    Eval: 16_h1=0.3232, 16_l2=0.2005, 32_h1=0.8231, 32_l2=0.5463
+    [9] time=10.01, avg_loss=0.2100, train_err=6.5640
+    Eval: 16_h1=0.2541, 16_l2=0.1552, 32_h1=0.7786, 32_l2=0.5305
+    [12] time=10.02, avg_loss=0.2080, train_err=6.5005
+    Eval: 16_h1=0.2947, 16_l2=0.1989, 32_h1=0.7486, 32_l2=0.4690
+    [15] time=10.02, avg_loss=0.1817, train_err=5.6768
+    Eval: 16_h1=0.2574, 16_l2=0.1490, 32_h1=0.7561, 32_l2=0.4931
+    [18] time=9.97, avg_loss=0.1338, train_err=4.1827
+    Eval: 16_h1=0.2373, 16_l2=0.1454, 32_h1=0.7275, 32_l2=0.4638
 
-    {'train_err': 4.47576854750514, 'avg_loss': 0.14322459352016448, 'avg_lasso_loss': None, 'epoch_train_time': 9.971655856999973}
+    {'train_err': 4.687410641461611, 'avg_loss': 0.14999714052677154, 'avg_lasso_loss': None, 'epoch_train_time': 9.965890896000019}
 
 
 
@@ -499,7 +499,7 @@ In practice we would train a Neural Operator on one or multiple GPUs
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (3 minutes 22.836 seconds)
+   **Total running time of the script:** (3 minutes 22.943 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_UNO_darcy.py:

dev/_downloads/4e1cba46bb51062a073424e1efbaad57/plot_DISCO_convolutions.zip

@@ -80,7 +80,7 @@ This output is organized as a defaultdict object that counts the FLOPS used in e
 
  .. code-block:: none
 
-    defaultdict(<function FlopTensorDispatchMode.__init__.<locals>.<lambda> at 0x7f4d14d68680>, {'': defaultdict(<class 'int'>, {'convolution.default': 2982150144, 'bmm.default': 138412032}), 'lifting': defaultdict(<class 'int'>, {'convolution.default': 562036736}), 'lifting.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 25165824}), 'lifting.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 536870912}), 'fno_blocks': defaultdict(<class 'int'>, {'convolution.default': 2147483648, 'bmm.default': 138412032}), 'fno_blocks.fno_skips.0': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.0.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.0': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.0': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.0.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.0.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.1': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.1.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.1': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.1': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.1.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.1.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.2': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.2.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.2': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.2': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.2.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.2.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.3': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.3.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.3': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.3': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.3.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.3.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'projection': defaultdict(<class 'int'>, {'convolution.default': 272629760}), 'projection.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'projection.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 4194304})})
+    defaultdict(<function FlopTensorDispatchMode.__init__.<locals>.<lambda> at 0x7f48b5d8c7c0>, {'': defaultdict(<class 'int'>, {'convolution.default': 2982150144, 'bmm.default': 138412032}), 'lifting': defaultdict(<class 'int'>, {'convolution.default': 562036736}), 'lifting.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 25165824}), 'lifting.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 536870912}), 'fno_blocks': defaultdict(<class 'int'>, {'convolution.default': 2147483648, 'bmm.default': 138412032}), 'fno_blocks.fno_skips.0': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.0.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.0': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.0': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.0.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.0.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.1': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.1.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.1': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.1': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.1.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.1.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.2': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.2.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.2': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.2': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.2.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.2.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.3': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.3.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.3': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.3': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.3.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.3.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'projection': defaultdict(<class 'int'>, {'convolution.default': 272629760}), 'projection.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'projection.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 4194304})})
 
 
 
@@ -125,7 +125,7 @@ To check the maximum FLOPS used during the forward pass, let's create a recursiv
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 3.129 seconds)
+   **Total running time of the script:** (0 minutes 3.106 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_count_flops.py:

dev/_downloads/5e60095ce99919773daa83384f767e02/plot_SFNO_swe.zip

@@ -163,7 +163,7 @@ Visualizing the data
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.314 seconds)
+   **Total running time of the script:** (0 minutes 0.310 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_darcy_flow.py:

dev/_downloads/645da00b8fbbb9bb5cae877fd0f31635/plot_FNO_darcy.zip

@@ -219,7 +219,7 @@ Loading the Navier-Stokes dataset in 128x128 resolution
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.166 seconds)
+   **Total running time of the script:** (0 minutes 0.185 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_darcy_flow_spectrum.py: