Github action: auto-update.

Author: github-actions[bot]

dev/_downloads/01111202d22a556abc06831baa4765ac/plot_darcy_flow.py

@@ -56,7 +56,7 @@
 # The first step of the default FNO model is a grid-based
 # positional embedding. We will add it manually here to
 # visualize the channels appended by this embedding.
-positional_embedding = GridEmbedding2D()
+positional_embedding = GridEmbedding2D(in_channels=1)
 # at train time, data will be collated with a batch dim.
 # we create a batch dim to pass into the embedding, then re-squeeze
 x = positional_embedding(data['x'].unsqueeze(0)).squeeze(0)

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -58,7 +58,7 @@
       },
       "outputs": [],
       "source": [
-        "for res, test_loader in test_loaders.items():\n    print(res)\n    # Get first batch\n    batch = next(iter(test_loader))\n    x = batch['x']\n    y = batch['y']\n\n    print(f'Testing samples for res {res} have shape {x.shape[1:]}')\n\n\ndata = train_dataset[0]\nx = data['x']\ny = data['y']\n\nprint(f'Training samples have shape {x.shape[1:]}')\n\n\n# Which sample to view\nindex = 0\n\ndata = train_dataset[index]\ndata = data_processor.preprocess(data, batched=False)\n\n# The first step of the default FNO model is a grid-based\n# positional embedding. We will add it manually here to\n# visualize the channels appended by this embedding.\npositional_embedding = GridEmbedding2D()\n# at train time, data will be collated with a batch dim.\n# we create a batch dim to pass into the embedding, then re-squeeze\nx = positional_embedding(data['x'].unsqueeze(0)).squeeze(0)\ny = data['y']\nfig = plt.figure(figsize=(7, 7))\nax = fig.add_subplot(2, 2, 1)\nax.imshow(x[0], cmap='gray')\nax.set_title('input x')\nax = fig.add_subplot(2, 2, 2)\nax.imshow(y.squeeze())\nax.set_title('input y')\nax = fig.add_subplot(2, 2, 3)\nax.imshow(x[1])\nax.set_title('x: 1st pos embedding')\nax = fig.add_subplot(2, 2, 4)\nax.imshow(x[2])\nax.set_title('x: 2nd pos embedding')\nfig.suptitle('Visualizing one input sample', y=0.98)\nplt.tight_layout()\nfig.show()"
+        "for res, test_loader in test_loaders.items():\n    print(res)\n    # Get first batch\n    batch = next(iter(test_loader))\n    x = batch['x']\n    y = batch['y']\n\n    print(f'Testing samples for res {res} have shape {x.shape[1:]}')\n\n\ndata = train_dataset[0]\nx = data['x']\ny = data['y']\n\nprint(f'Training samples have shape {x.shape[1:]}')\n\n\n# Which sample to view\nindex = 0\n\ndata = train_dataset[index]\ndata = data_processor.preprocess(data, batched=False)\n\n# The first step of the default FNO model is a grid-based\n# positional embedding. We will add it manually here to\n# visualize the channels appended by this embedding.\npositional_embedding = GridEmbedding2D(in_channels=1)\n# at train time, data will be collated with a batch dim.\n# we create a batch dim to pass into the embedding, then re-squeeze\nx = positional_embedding(data['x'].unsqueeze(0)).squeeze(0)\ny = data['y']\nfig = plt.figure(figsize=(7, 7))\nax = fig.add_subplot(2, 2, 1)\nax.imshow(x[0], cmap='gray')\nax.set_title('input x')\nax = fig.add_subplot(2, 2, 2)\nax.imshow(y.squeeze())\nax.set_title('input y')\nax = fig.add_subplot(2, 2, 3)\nax.imshow(x[1])\nax.set_title('x: 1st pos embedding')\nax = fig.add_subplot(2, 2, 4)\nax.imshow(x[2])\nax.set_title('x: 2nd pos embedding')\nfig.suptitle('Visualizing one input sample', y=0.98)\nplt.tight_layout()\nfig.show()"
       ]
     }
   ],