instance._init_kwargs = kwargs
return instance
-
+
+ def state_dict(self, destination: dict=None, prefix: str='', keep_vars: bool=False):
+ """
+ state_dict subclasses nn.Module.state_dict() and adds a metadata field
+ to track the model version and ensure only compatible saves are loaded.
+
+ Parameters
+ ----------
+ destination : dict, optional
+ If provided, the state of module will
+ be updated into the dict and the same object is returned.
+ Otherwise, an OrderedDict will be created and returned, by default None
+ prefix : str, optional
+ a prefix added to parameter and buffer
+ names to compose the keys in state_dict, by default ``''``
+ keep_vars (bool, optional): by default the torch.Tensors
+ returned in the state dict are detached from autograd.
+ If True, detaching will not be performed, by default False
+
+ """
+ state_dict = super().state_dict(destination=destination, prefix=prefix, keep_vars=keep_vars)
+ if state_dict.get('_metadata') == None:
+ state_dict['_metadata'] = self._init_kwargs
+ else:
+ warnings.warn("Attempting to update metadata for a module with metadata already in self.state_dict()")
+ return state_dict
+
+ def load_state_dict(self, state_dict, strict=True, assign=False):
+ """load_state_dict subclasses nn.Module.load_state_dict() and adds a metadata field
+ to track the model version and ensure only compatible saves are loaded.
+
+ Parameters
+ ----------
+ state_dict : dict
+ state dictionary generated by ``nn.Module.state_dict()``
+ strict : bool, optional
+ whether to strictly enforce that the keys in ``state_dict``
+ match the keys returned by this module's, by default True.
+ assign : bool, optional
+ whether to assign items in the state dict to their corresponding keys
+ in the module instead of copying them inplace into the module's current
+ parameters and buffers. When False, the properties of the tensors in the
+ current module are preserved while when True, the properties of the Tensors
+ in the state dict are preserved, by default False
+
+ Returns
+ -------
+ _type_
+ _description_
+ """
+ metadata = state_dict.pop('_metadata', None)
+
+ if metadata is not None:
+ saved_version = metadata.get('_version', None)
+ if saved_version is None:
+ warnings.warn(f"Saved instance of {self.__class__} has no stored version attribute.")
+ if saved_version != self._version:
+ warnings.warn(f"Attempting to load a {self.__class__} of version {saved_version},"
+ f"But current version of {self.__class__} is {saved_version}")
+ # remove state dict metadata at the end to ensure proper loading with PyTorch module
+ return super().load_state_dict(state_dict, strict=strict, assign=assign)
+
def save_checkpoint(self, save_folder, save_name):
"""Saves the model state and init param in the given folder under the given name
"""
diff --git a/dev/_sources/auto_examples/plot_DISCO_convolutions.rst.txt b/dev/_sources/auto_examples/plot_DISCO_convolutions.rst.txt
index b319b4f..ead0a56 100644
--- a/dev/_sources/auto_examples/plot_DISCO_convolutions.rst.txt
+++ b/dev/_sources/auto_examples/plot_DISCO_convolutions.rst.txt
@@ -357,7 +357,7 @@ in order to compute the convolved image, we need to first bring it into the righ
.. code-block:: none
-
+
@@ -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 31.674 seconds)
+ **Total running time of the script:** (0 minutes 31.281 seconds)
.. _sphx_glr_download_auto_examples_plot_DISCO_convolutions.py:
diff --git a/dev/_sources/auto_examples/plot_FNO_darcy.rst.txt b/dev/_sources/auto_examples/plot_FNO_darcy.rst.txt
index 832c146..77e4a0e 100644
--- a/dev/_sources/auto_examples/plot_FNO_darcy.rst.txt
+++ b/dev/_sources/auto_examples/plot_FNO_darcy.rst.txt
@@ -248,13 +248,13 @@ Training the model
)
### SCHEDULER ###
-
+
### LOSSES ###
- * Train:
+ * Train:
- * Test: {'h1': , 'l2': }
+ * Test: {'h1': , 'l2': }
@@ -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=2.59, avg_loss=0.6956, train_err=21.7383
+ [0] time=2.57, 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=2.57, 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=2.58, avg_loss=0.1911, train_err=5.9721
+ [6] time=2.55, 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=2.64, avg_loss=0.1410, train_err=4.4073
+ [9] time=2.56, 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=2.63, avg_loss=0.1422, train_err=4.4434
+ [12] time=2.57, 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=2.61, avg_loss=0.1198, train_err=3.7424
+ [15] time=2.56, 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=2.59, avg_loss=0.1104, train_err=3.4502
+ [18] time=2.55, 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': 2.582470736999994}
+ {'train_err': 2.9605126455426216, 'avg_loss': 0.0947364046573639, 'avg_lasso_loss': None, 'epoch_train_time': 2.5690329919999613}
@@ -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 53.239 seconds)
+ **Total running time of the script:** (0 minutes 52.571 seconds)
.. _sphx_glr_download_auto_examples_plot_FNO_darcy.py:
diff --git a/dev/_sources/auto_examples/plot_SFNO_swe.rst.txt b/dev/_sources/auto_examples/plot_SFNO_swe.rst.txt
index 75afd7c..359c6f9 100644
--- a/dev/_sources/auto_examples/plot_SFNO_swe.rst.txt
+++ b/dev/_sources/auto_examples/plot_SFNO_swe.rst.txt
@@ -234,13 +234,13 @@ Creating the losses
)
### SCHEDULER ###
-
+
### LOSSES ###
- * Train:
+ * Train:
- * Test: {'l2': }
+ * Test: {'l2': }
@@ -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=3.71, avg_loss=2.5890, train_err=10.3559
- Eval: (32, 64)_l2=2.0514, (64, 128)_l2=2.5055
- [3] time=3.64, avg_loss=0.4467, train_err=1.7867
- Eval: (32, 64)_l2=0.6580, (64, 128)_l2=2.5187
- [6] time=3.60, avg_loss=0.2807, train_err=1.1226
- Eval: (32, 64)_l2=0.5458, (64, 128)_l2=2.4767
- [9] time=3.59, avg_loss=0.2355, train_err=0.9419
- Eval: (32, 64)_l2=0.5549, (64, 128)_l2=2.4920
- [12] time=3.65, avg_loss=0.2170, train_err=0.8680
- Eval: (32, 64)_l2=0.5206, (64, 128)_l2=2.4921
- [15] time=3.68, avg_loss=0.1824, train_err=0.7297
- Eval: (32, 64)_l2=0.4906, (64, 128)_l2=2.4846
- [18] time=3.62, avg_loss=0.1764, train_err=0.7054
- Eval: (32, 64)_l2=0.4640, (64, 128)_l2=2.4902
+ [0] time=3.61, avg_loss=2.5783, train_err=10.3132
+ Eval: (32, 64)_l2=1.7929, (64, 128)_l2=2.3944
+ [3] time=3.61, avg_loss=0.3960, train_err=1.5841
+ Eval: (32, 64)_l2=0.4045, (64, 128)_l2=2.6128
+ [6] time=3.61, avg_loss=0.2645, train_err=1.0581
+ Eval: (32, 64)_l2=0.2855, (64, 128)_l2=2.5574
+ [9] time=3.57, avg_loss=0.2303, train_err=0.9211
+ Eval: (32, 64)_l2=0.3216, (64, 128)_l2=2.5180
+ [12] time=3.58, avg_loss=0.1810, train_err=0.7240
+ Eval: (32, 64)_l2=0.2103, (64, 128)_l2=2.5075
+ [15] time=3.57, avg_loss=0.1538, train_err=0.6152
+ Eval: (32, 64)_l2=0.1890, (64, 128)_l2=2.4402
+ [18] time=3.57, avg_loss=0.1323, train_err=0.5294
+ Eval: (32, 64)_l2=0.1798, (64, 128)_l2=2.4265
- {'train_err': 0.6676547992229461, 'avg_loss': 0.16691369980573653, 'avg_lasso_loss': None, 'epoch_train_time': 3.599016188000064}
+ {'train_err': 0.5195050823688507, 'avg_loss': 0.12987627059221268, 'avg_lasso_loss': None, 'epoch_train_time': 3.607363260999989}
@@ -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 28.830 seconds)
+ **Total running time of the script:** (1 minutes 28.039 seconds)
.. _sphx_glr_download_auto_examples_plot_SFNO_swe.py:
diff --git a/dev/_sources/auto_examples/plot_UNO_darcy.rst.txt b/dev/_sources/auto_examples/plot_UNO_darcy.rst.txt
index 1860cb5..05ae47d 100644
--- a/dev/_sources/auto_examples/plot_UNO_darcy.rst.txt
+++ b/dev/_sources/auto_examples/plot_UNO_darcy.rst.txt
@@ -345,13 +345,13 @@ Creating the losses
)
### SCHEDULER ###
-
+
### LOSSES ###
- * Train:
+ * Train:
- * Test: {'h1': , 'l2': }
+ * Test: {'h1': , 'l2': }
@@ -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=10.19, avg_loss=0.6679, train_err=20.8720
- Eval: 16_h1=0.3981, 16_l2=0.2711, 32_h1=0.9217, 32_l2=0.6494
- [3] time=10.13, avg_loss=0.2426, train_err=7.5825
- Eval: 16_h1=0.2854, 16_l2=0.1799, 32_h1=0.7671, 32_l2=0.4978
- [6] time=10.09, avg_loss=0.2373, train_err=7.4160
- Eval: 16_h1=0.3061, 16_l2=0.1973, 32_h1=0.7619, 32_l2=0.4908
- [9] time=10.08, avg_loss=0.2195, train_err=6.8592
- Eval: 16_h1=0.2422, 16_l2=0.1575, 32_h1=0.7381, 32_l2=0.4485
- [12] time=10.11, avg_loss=0.1690, train_err=5.2798
- Eval: 16_h1=0.2518, 16_l2=0.1580, 32_h1=0.7492, 32_l2=0.4722
- [15] time=10.12, avg_loss=0.1825, train_err=5.7038
- Eval: 16_h1=0.2699, 16_l2=0.1664, 32_h1=0.7599, 32_l2=0.4589
- [18] time=10.08, avg_loss=0.1676, train_err=5.2361
- Eval: 16_h1=0.2356, 16_l2=0.1478, 32_h1=0.7352, 32_l2=0.4557
+ [0] time=10.15, avg_loss=0.6631, train_err=20.7222
+ Eval: 16_h1=0.4579, 16_l2=0.2992, 32_h1=0.9470, 32_l2=0.6763
+ [3] time=10.05, avg_loss=0.2476, train_err=7.7385
+ Eval: 16_h1=0.2439, 16_l2=0.1618, 32_h1=0.9045, 32_l2=0.6343
+ [6] time=10.04, avg_loss=0.2285, train_err=7.1392
+ Eval: 16_h1=0.2579, 16_l2=0.1738, 32_h1=0.8590, 32_l2=0.6153
+ [9] time=10.07, avg_loss=0.1985, train_err=6.2036
+ Eval: 16_h1=0.2429, 16_l2=0.1520, 32_h1=0.8739, 32_l2=0.5978
+ [12] time=10.04, avg_loss=0.1856, train_err=5.8014
+ Eval: 16_h1=0.2410, 16_l2=0.1467, 32_h1=0.8608, 32_l2=0.5604
+ [15] time=10.05, avg_loss=0.1546, train_err=4.8301
+ Eval: 16_h1=0.3156, 16_l2=0.2123, 32_h1=0.8466, 32_l2=0.6000
+ [18] time=10.03, avg_loss=0.1247, train_err=3.8961
+ Eval: 16_h1=0.2340, 16_l2=0.1354, 32_h1=0.8477, 32_l2=0.5822
- {'train_err': 4.981805760413408, 'avg_loss': 0.15941778433322906, 'avg_lasso_loss': None, 'epoch_train_time': 10.092775133000032}
+ {'train_err': 4.671443767845631, 'avg_loss': 0.14948620057106018, 'avg_lasso_loss': None, 'epoch_train_time': 10.053496939999945}
@@ -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 26.114 seconds)
+ **Total running time of the script:** (3 minutes 24.779 seconds)
.. _sphx_glr_download_auto_examples_plot_UNO_darcy.py:
diff --git a/dev/_sources/auto_examples/plot_count_flops.rst.txt b/dev/_sources/auto_examples/plot_count_flops.rst.txt
index 4536697..181286f 100644
--- a/dev/_sources/auto_examples/plot_count_flops.rst.txt
+++ b/dev/_sources/auto_examples/plot_count_flops.rst.txt
@@ -80,7 +80,7 @@ This output is organized as a defaultdict object that counts the FLOPS used in e
.. code-block:: none
- defaultdict(. at 0x7f568b61f1a0>, {'': defaultdict(, {'convolution.default': 2982150144, 'bmm.default': 138412032}), 'lifting': defaultdict(, {'convolution.default': 562036736}), 'lifting.fcs.0': defaultdict(, {'convolution.default': 25165824}), 'lifting.fcs.1': defaultdict(, {'convolution.default': 536870912}), 'fno_blocks': defaultdict(, {'convolution.default': 2147483648, 'bmm.default': 138412032}), 'fno_blocks.fno_skips.0': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.0.conv': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.convs.0': defaultdict(, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.0': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.0.fcs.0': defaultdict(, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.0.fcs.1': defaultdict(, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.1': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.1.conv': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.convs.1': defaultdict(, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.1': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.1.fcs.0': defaultdict(, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.1.fcs.1': defaultdict(, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.2': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.2.conv': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.convs.2': defaultdict(, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.2': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.2.fcs.0': defaultdict(, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.2.fcs.1': defaultdict(, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.3': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.3.conv': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.convs.3': defaultdict(, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.3': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.3.fcs.0': defaultdict(, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.3.fcs.1': defaultdict(, {'convolution.default': 134217728}), 'projection': defaultdict(, {'convolution.default': 272629760}), 'projection.fcs.0': defaultdict(, {'convolution.default': 268435456}), 'projection.fcs.1': defaultdict(, {'convolution.default': 4194304})})
+ defaultdict(. at 0x7f79193fb7e0>, {'': defaultdict(, {'convolution.default': 2982150144, 'bmm.default': 138412032}), 'lifting': defaultdict(, {'convolution.default': 562036736}), 'lifting.fcs.0': defaultdict(, {'convolution.default': 25165824}), 'lifting.fcs.1': defaultdict(, {'convolution.default': 536870912}), 'fno_blocks': defaultdict(, {'convolution.default': 2147483648, 'bmm.default': 138412032}), 'fno_blocks.fno_skips.0': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.0.conv': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.convs.0': defaultdict(, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.0': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.0.fcs.0': defaultdict(, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.0.fcs.1': defaultdict(, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.1': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.1.conv': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.convs.1': defaultdict(, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.1': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.1.fcs.0': defaultdict(, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.1.fcs.1': defaultdict(, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.2': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.2.conv': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.convs.2': defaultdict(, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.2': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.2.fcs.0': defaultdict(, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.2.fcs.1': defaultdict(, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.3': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.3.conv': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.convs.3': defaultdict(, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.3': defaultdict(, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.3.fcs.0': defaultdict(, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.3.fcs.1': defaultdict(, {'convolution.default': 134217728}), 'projection': defaultdict(, {'convolution.default': 272629760}), 'projection.fcs.0': defaultdict(, {'convolution.default': 268435456}), 'projection.fcs.1': defaultdict(, {'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 3.316 seconds)
+ **Total running time of the script:** (0 minutes 3.210 seconds)
.. _sphx_glr_download_auto_examples_plot_count_flops.py:
diff --git a/dev/_sources/auto_examples/plot_darcy_flow.rst.txt b/dev/_sources/auto_examples/plot_darcy_flow.rst.txt
index eedcee1..75f63a7 100644
--- a/dev/_sources/auto_examples/plot_darcy_flow.rst.txt
+++ b/dev/_sources/auto_examples/plot_darcy_flow.rst.txt
@@ -163,7 +163,7 @@ Visualizing the data
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.333 seconds)
+ **Total running time of the script:** (0 minutes 0.328 seconds)
.. _sphx_glr_download_auto_examples_plot_darcy_flow.py:
diff --git a/dev/_sources/auto_examples/plot_darcy_flow_spectrum.rst.txt b/dev/_sources/auto_examples/plot_darcy_flow_spectrum.rst.txt
index 4b0afdb..71c7802 100644
--- a/dev/_sources/auto_examples/plot_darcy_flow_spectrum.rst.txt
+++ b/dev/_sources/auto_examples/plot_darcy_flow_spectrum.rst.txt
@@ -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 0.204 seconds)
+ **Total running time of the script:** (0 minutes 0.200 seconds)
.. _sphx_glr_download_auto_examples_plot_darcy_flow_spectrum.py:
diff --git a/dev/_sources/auto_examples/plot_incremental_FNO_darcy.rst.txt b/dev/_sources/auto_examples/plot_incremental_FNO_darcy.rst.txt
index e9294c2..d4d537e 100644
--- a/dev/_sources/auto_examples/plot_incremental_FNO_darcy.rst.txt
+++ b/dev/_sources/auto_examples/plot_incremental_FNO_darcy.rst.txt
@@ -240,15 +240,15 @@ Set up the losses
)
### SCHEDULER ###
-
+
### LOSSES ###
### INCREMENTAL RESOLUTION + GRADIENT EXPLAINED ###
- * Train:
+ * Train:
- * Test: {'h1': , 'l2': }
+ * Test: {'h1': , 'l2': }
@@ -337,9 +337,9 @@ Train the model
Eval: 16_h1=0.7698, 16_l2=0.4235, 32_h1=0.9175, 32_l2=0.4405
[3] time=0.23, avg_loss=0.6082, train_err=8.6886
Eval: 16_h1=0.7600, 16_l2=0.4570, 32_h1=1.0126, 32_l2=0.5005
- [4] time=0.23, avg_loss=0.5604, train_err=8.0054
+ [4] time=0.22, avg_loss=0.5604, train_err=8.0054
Eval: 16_h1=0.8301, 16_l2=0.4577, 32_h1=1.1722, 32_l2=0.4987
- [5] time=0.22, avg_loss=0.5366, train_err=7.6663
+ [5] time=0.23, avg_loss=0.5366, train_err=7.6663
Eval: 16_h1=0.8099, 16_l2=0.4076, 32_h1=1.1414, 32_l2=0.4436
[6] time=0.23, avg_loss=0.5050, train_err=7.2139
Eval: 16_h1=0.7204, 16_l2=0.3888, 32_h1=0.9945, 32_l2=0.4285
@@ -352,9 +352,9 @@ Train the model
Incre Res Update: change index to 1
Incre Res Update: change sub to 1
Incre Res Update: change res to 16
- [10] time=0.29, avg_loss=0.5158, train_err=7.3681
+ [10] time=0.31, avg_loss=0.5158, train_err=7.3681
Eval: 16_h1=0.5133, 16_l2=0.3167, 32_h1=0.6120, 32_l2=0.3022
- [11] time=0.28, avg_loss=0.4536, train_err=6.4795
+ [11] time=0.27, avg_loss=0.4536, train_err=6.4795
Eval: 16_h1=0.4680, 16_l2=0.3422, 32_h1=0.6436, 32_l2=0.3659
[12] time=0.28, avg_loss=0.4155, train_err=5.9358
Eval: 16_h1=0.4119, 16_l2=0.2692, 32_h1=0.5285, 32_l2=0.2692
@@ -366,14 +366,14 @@ Train the model
Eval: 16_h1=0.3611, 16_l2=0.2323, 32_h1=0.5079, 32_l2=0.2455
[16] time=0.28, avg_loss=0.3251, train_err=4.6438
Eval: 16_h1=0.3433, 16_l2=0.2224, 32_h1=0.4757, 32_l2=0.2351
- [17] time=0.29, avg_loss=0.3072, train_err=4.3888
+ [17] time=0.28, avg_loss=0.3072, train_err=4.3888
Eval: 16_h1=0.3458, 16_l2=0.2226, 32_h1=0.4776, 32_l2=0.2371
- [18] time=0.29, avg_loss=0.2982, train_err=4.2593
+ [18] time=0.28, avg_loss=0.2982, train_err=4.2593
Eval: 16_h1=0.3251, 16_l2=0.2116, 32_h1=0.4519, 32_l2=0.2245
[19] time=0.28, avg_loss=0.2802, train_err=4.0024
Eval: 16_h1=0.3201, 16_l2=0.2110, 32_h1=0.4533, 32_l2=0.2245
- {'train_err': 4.002395851271493, 'avg_loss': 0.2801677095890045, 'avg_lasso_loss': None, 'epoch_train_time': 0.28468873500003156, '16_h1': tensor(0.3201), '16_l2': tensor(0.2110), '32_h1': tensor(0.4533), '32_l2': tensor(0.2245)}
+ {'train_err': 4.002395851271493, 'avg_loss': 0.2801677095890045, 'avg_lasso_loss': None, 'epoch_train_time': 0.2810444039999993, '16_h1': tensor(0.3201), '16_l2': tensor(0.2110), '32_h1': tensor(0.4533), '32_l2': tensor(0.2245)}
@@ -447,7 +447,7 @@ In practice we would train a Neural Operator on one or multiple GPUs
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 7.057 seconds)
+ **Total running time of the script:** (0 minutes 7.023 seconds)
.. _sphx_glr_download_auto_examples_plot_incremental_FNO_darcy.py:
diff --git a/dev/_sources/auto_examples/sg_execution_times.rst.txt b/dev/_sources/auto_examples/sg_execution_times.rst.txt
index 2461063..de1cfe5 100644
--- a/dev/_sources/auto_examples/sg_execution_times.rst.txt
+++ b/dev/_sources/auto_examples/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
Computation times
=================
-**06:30.766** total execution time for 9 files **from auto_examples**:
+**06:27.430** total execution time for 9 files **from auto_examples**:
.. container::
@@ -33,28 +33,28 @@ Computation times
- Time
- Mem (MB)
* - :ref:`sphx_glr_auto_examples_plot_UNO_darcy.py` (``plot_UNO_darcy.py``)
- - 03:26.114
+ - 03:24.779
- 0.0
* - :ref:`sphx_glr_auto_examples_plot_SFNO_swe.py` (``plot_SFNO_swe.py``)
- - 01:28.830
+ - 01:28.039
- 0.0
* - :ref:`sphx_glr_auto_examples_plot_FNO_darcy.py` (``plot_FNO_darcy.py``)
- - 00:53.239
+ - 00:52.571
- 0.0
* - :ref:`sphx_glr_auto_examples_plot_DISCO_convolutions.py` (``plot_DISCO_convolutions.py``)
- - 00:31.674
+ - 00:31.281
- 0.0
* - :ref:`sphx_glr_auto_examples_plot_incremental_FNO_darcy.py` (``plot_incremental_FNO_darcy.py``)
- - 00:07.057
+ - 00:07.023
- 0.0
* - :ref:`sphx_glr_auto_examples_plot_count_flops.py` (``plot_count_flops.py``)
- - 00:03.316
+ - 00:03.210
- 0.0
* - :ref:`sphx_glr_auto_examples_plot_darcy_flow.py` (``plot_darcy_flow.py``)
- - 00:00.333
+ - 00:00.328
- 0.0
* - :ref:`sphx_glr_auto_examples_plot_darcy_flow_spectrum.py` (``plot_darcy_flow_spectrum.py``)
- - 00:00.204
+ - 00:00.200
- 0.0
* - :ref:`sphx_glr_auto_examples_checkpoint_FNO_darcy.py` (``checkpoint_FNO_darcy.py``)
- 00:00.000
diff --git a/dev/_sources/sg_execution_times.rst.txt b/dev/_sources/sg_execution_times.rst.txt
index a586820..637751a 100644
--- a/dev/_sources/sg_execution_times.rst.txt
+++ b/dev/_sources/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
Computation times
=================
-**06:30.766** total execution time for 9 files **from all galleries**:
+**06:27.430** total execution time for 9 files **from all galleries**:
.. container::
@@ -33,28 +33,28 @@ Computation times
- Time
- Mem (MB)
* - :ref:`sphx_glr_auto_examples_plot_UNO_darcy.py` (``../../examples/plot_UNO_darcy.py``)
- - 03:26.114
+ - 03:24.779
- 0.0
* - :ref:`sphx_glr_auto_examples_plot_SFNO_swe.py` (``../../examples/plot_SFNO_swe.py``)
- - 01:28.830
+ - 01:28.039
- 0.0
* - :ref:`sphx_glr_auto_examples_plot_FNO_darcy.py` (``../../examples/plot_FNO_darcy.py``)
- - 00:53.239
+ - 00:52.571
- 0.0
* - :ref:`sphx_glr_auto_examples_plot_DISCO_convolutions.py` (``../../examples/plot_DISCO_convolutions.py``)
- - 00:31.674
+ - 00:31.281
- 0.0
* - :ref:`sphx_glr_auto_examples_plot_incremental_FNO_darcy.py` (``../../examples/plot_incremental_FNO_darcy.py``)
- - 00:07.057
+ - 00:07.023
- 0.0
* - :ref:`sphx_glr_auto_examples_plot_count_flops.py` (``../../examples/plot_count_flops.py``)
- - 00:03.316
+ - 00:03.210
- 0.0
* - :ref:`sphx_glr_auto_examples_plot_darcy_flow.py` (``../../examples/plot_darcy_flow.py``)
- - 00:00.333
+ - 00:00.328
- 0.0
* - :ref:`sphx_glr_auto_examples_plot_darcy_flow_spectrum.py` (``../../examples/plot_darcy_flow_spectrum.py``)
- - 00:00.204
+ - 00:00.200
- 0.0
* - :ref:`sphx_glr_auto_examples_checkpoint_FNO_darcy.py` (``../../examples/checkpoint_FNO_darcy.py``)
- 00:00.000
diff --git a/dev/auto_examples/plot_DISCO_convolutions.html b/dev/auto_examples/plot_DISCO_convolutions.html
index 90bb05c..09a63fb 100644
--- a/dev/auto_examples/plot_DISCO_convolutions.html
+++ b/dev/auto_examples/plot_DISCO_convolutions.html
@@ -330,7 +330,7 @@
# plt.show()
