diff --git a/notebook.ipynb b/notebook.ipynb index 4ad22f7..a3f6f31 100644 --- a/notebook.ipynb +++ b/notebook.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "id": "9e6eda08-ff32-4ee3-9c35-f34b6d3a9a41", "metadata": {}, "outputs": [ @@ -10,53 +10,53 @@ "name": "stdout", "output_type": "stream", "text": [ - "Processing /Users/tom/Desktop/temp_folder_trasnsfer/TorchGRTL\n", + "Defaulting to user installation because normal site-packages is not writeable\n", + "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n", + "Processing /jet/home/thelfer/TorchGRTL\n", " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hRequirement already satisfied: torch in ./dev/lib/python3.8/site-packages (from GeneralRelativity==0.1) (2.1.2)\n", - "Requirement already satisfied: black in ./dev/lib/python3.8/site-packages (from GeneralRelativity==0.1) (23.12.1)\n", - "Requirement already satisfied: pre-commit in 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/tmp/pip-ephem-wheel-cache-0_jpzufc/wheels/04/ec/8b/6f637a101ab24e0d90dd2562678519ec9bc290a2b910236796\n", "Successfully built GeneralRelativity\n", "Installing collected packages: GeneralRelativity\n", " Attempting uninstall: GeneralRelativity\n", @@ -64,52 +64,67 @@ " Uninstalling GeneralRelativity-0.1:\n", " Successfully uninstalled GeneralRelativity-0.1\n", "Successfully installed GeneralRelativity-0.1\n", - "Requirement already satisfied: pandas in ./dev/lib/python3.8/site-packages (2.0.3)\n", - "Requirement already satisfied: scikit-learn in ./dev/lib/python3.8/site-packages (1.3.2)\n", - "Requirement already satisfied: tensorboard in ./dev/lib/python3.8/site-packages (2.14.0)\n", - "Requirement already satisfied: python-dateutil>=2.8.2 in ./dev/lib/python3.8/site-packages (from pandas) (2.8.2)\n", - "Requirement already satisfied: pytz>=2020.1 in ./dev/lib/python3.8/site-packages (from pandas) (2023.3.post1)\n", - "Requirement already satisfied: tzdata>=2022.1 in 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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n", + "Defaulting to user installation because normal site-packages is not writeable\n", + "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (1.5.3)\n", + "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.0)\n", + "Requirement already satisfied: tensorboard in /usr/local/lib/python3.10/dist-packages (2.9.0)\n", + "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.1.0a0+32f93b1)\n", + "Requirement already satisfied: 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charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard) (3.2.0)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard) (3.4)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard) (2023.7.22)\n", + "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard) (2.1.3)\n", + "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n", + "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard) (0.5.0)\n", + "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard) (3.2.2)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n" ] } ], "source": [ "!pip install .\n", - "!pip install pandas scikit-learn tensorboard" + "!pip install pandas scikit-learn tensorboard torch tqdm" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "id": "113757c9-d051-41d3-9eab-5c65bc8c1098", "metadata": {}, "outputs": [], @@ -154,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "id": "0581229a-6848-4bde-b6db-8bc5fdff61e5", "metadata": {}, "outputs": [ @@ -162,7 +177,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Executing the model on : cpu\n" + "Executing the model on : cuda:0\n" ] } ], @@ -177,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "id": "4c57d113-0439-4c27-bcbc-c33f963c0c94", "metadata": {}, "outputs": [ @@ -189,18 +204,11 @@ ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "bf6ef09089a84f6883e7796256f4fb8a", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/3 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stderr", + "output_type": "stream", + "text": [ + " 12%|█▏ | 1247/10000 [04:42<2:09:36, 1.13it/s]" + ] } ], "source": [ "# Note: it will slow down signficantly with BFGS steps, they are 10x slower, just be aware!\n", - "ADAMsteps = 8 # Will perform # steps of ADAM steps and then switch over to BFGS-L\n", - "n_steps = 10 # Total amount of steps\n", + "ADAMsteps = 1000 # Will perform # steps of ADAM steps and then switch over to BFGS-L\n", + "n_steps = 10000 # Total amount of steps\n", "\n", "net.train()\n", "net.to(device)\n", @@ -504,7 +495,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "fdd46a1e-829c-4345-8e9b-0205aeab66a9", "metadata": { "editable": true, @@ -514,18 +505,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(6, 5), dpi=300)\n", "\n", @@ -541,10 +521,21 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 236, "id": "d1111cd8-4e85-4e05-bc75-96eb4281ac91", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([2.5000, 2.5000, 2.5000])" + ] + }, + "execution_count": 236, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Get comparison with classical methods\n", "from GeneralRelativity.Interpolation import *\n", @@ -553,25 +544,26 @@ "points = 6\n", "power = 3\n", "shape = X_batch.shape\n", - "interpolation = interp(points, power,False)\n", + "interpolation = interp(points, power,True)\n", "ghosts = int(math.ceil(points / 2))\n", "shape_higher_order = (shape[-1] - 2 * ghosts) * 2 + 2\n", "\n", - "y_interpolated, _ = interpolation(X_batch)\n", + "y_interpolated, pos = interpolation(X_batch)\n", "y_interpolated_buffer = torch.zeros_like(y_batch)\n", - "diff = (y_batch.shape[-1] - y_interpolated.shape[-1]) // 2\n", - "y_interpolated_buffer[:, :, diff:-diff, diff:-diff, diff:-diff] = y_interpolated" + "diff = (y_batch.shape[-1] - y_interpolated.shape[-1]) // 2 \n", + "y_interpolated_buffer[:, :, diff:-diff, diff:-diff, diff:-diff] = y_interpolated\n", + "pos[1,1,1]" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 237, "id": "309d8ad4-fd6d-4376-ace4-3fcb0d65e6be", "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -585,21 +577,21 @@ "channel = 0\n", "slice = 9\n", "# Note we remove some part of the grid as the interpolation needs space\n", - "max_val = torch.max(y_batch[box, channel, diff:-diff, diff:-diff, slice]).numpy()\n", - "min_val = torch.min(y_batch[box, channel, diff:-diff, diff:-diff, slice]).numpy()\n", + "max_val = torch.max(y_batch[box, channel, diff-1:-diff+1, diff-1:-diff+1, slice-1]).cpu().numpy()\n", + "min_val = torch.min(y_batch[box, channel, diff-1:-diff+1, diff-1:-diff+1, slice-1]).cpu().numpy()\n", "\n", "# Create subplots\n", "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n", "\n", "# Plot ground truth\n", "axes[0].set_title(\"Ground Truth\")\n", - "im0 = axes[0].imshow(y_batch[box, channel, diff:-diff, diff:-diff, slice].numpy(), vmin=min_val, vmax=max_val, cmap='viridis')\n", + "im0 = axes[0].imshow(y_batch[box, channel, diff-1:-diff+1,diff-1:-diff+1, slice - 1].cpu().numpy(), vmin=min_val, vmax=max_val, cmap='viridis')\n", "axes[0].set_xlabel('X-axis')\n", "axes[0].set_ylabel('Y-axis')\n", "\n", "# Plot Neural Network\n", "axes[1].set_title(\"Neural Network\")\n", - "im1 = axes[1].imshow(y_pred[box, channel, diff:-diff, diff:-diff, slice].detach().numpy(), vmin=min_val, vmax=max_val, cmap='viridis')\n", + "im1 = axes[1].imshow(y_pred[box, channel, diff-1:-diff+1,diff-1:-diff+1, slice -1 ].detach().cpu().numpy(), vmin=min_val, vmax=max_val, cmap='viridis')\n", "axes[1].set_xlabel('X-axis')\n", "axes[1].set_ylabel('Y-axis')\n", "\n", @@ -622,13 +614,54 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 238, + "id": "ed7a9e68-15b1-4f3d-964b-a770af2116c0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 238, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "box = 5\n", + "channel = 0\n", + "slice = 5\n", + "plt.plot(\n", + " y_batch[box, channel, :, slice-1, slice-1].detach().cpu().numpy(),\n", + " label=\"ground truth\"\n", + ")\n", + "plt.plot(\n", + " y_pred[box, channel, :, slice-1, slice-1].detach().cpu().numpy(),\n", + " label=\"neural network \"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 229, "id": "ddd54cde-bbdc-45ba-8ae4-487b675bdf43", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -642,19 +675,21 @@ "channel = 0\n", "slice = 5\n", "plt.plot(\n", - " y_batch[box, channel, diff:-diff, slice, slice].detach().cpu().numpy(),\n", - " label=\"ground truth\",\n", + " y_batch[box, channel, diff-1:-diff-1, slice-1, slice-1].detach().cpu().numpy(),\n", + " label=\"ground truth\"\n", ")\n", "plt.plot(\n", - " y_pred[box, channel, diff:-diff, slice, slice].detach().cpu().numpy(),\n", - " label=\"neural network \",\n", + " y_pred[box, channel, diff-1:-diff-1, slice-1, slice-1].detach().cpu().numpy(),\n", + " label=\"neural network \"\n", ")\n", "plt.plot(\n", - " y_interpolated_buffer[box, channel, diff:-diff, slice, slice]\n", + " y_interpolated[box, channel, : , slice-diff, slice-diff]\n", " .detach()\n", " .cpu()\n", " .numpy(),\n", " label=\"interpolation \",\n", + " linestyle = ':',\n", + " alpha = 0.6\n", ")\n", "plt.legend()\n", "plt.show()" @@ -662,7 +697,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 230, "id": "e2c3a78a-9a54-4772-82b0-bd8d43bcdeb6", "metadata": {}, "outputs": [ @@ -670,9 +705,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Reference data L2 1.0373636182292944e-14\n", - "Neural Network L2 loss 58302717.69780393\n", - "Interpolation 7.375914989893317e-09\n" + "Reference data L2 7.250818855990449e-15\n", + "Neural Network L2 loss 326.97444372480106\n", + "Interpolation 4.6257036956376396e-07\n" ] } ], @@ -680,17 +715,17 @@ "# Calculate L2Ham performance\n", "my_loss = Hamiltonian_loss(oneoverdx)\n", "print(\n", - " f\"Reference data L2 {my_loss(y_batch[:, :, diff:-diff, diff:-diff, diff:-diff], torch.tensor([])).detach().numpy()}\"\n", + " f\"Reference data L2 {my_loss(y_batch[:, :, diff-1:-diff-1, diff-1:-diff-1, diff-1:-diff-1], torch.tensor([])).detach().cpu().numpy()}\"\n", ")\n", "print(\n", - " f\"Neural Network L2 loss {my_loss(y_pred[:, :, diff:-diff, diff:-diff, diff:-diff], torch.tensor([])).detach().numpy()}\"\n", + " f\"Neural Network L2 loss {my_loss(y_pred[:, :, diff-1:-diff-1, diff-1:-diff-1, diff-1:-diff-1], torch.tensor([])).detach().cpu().numpy()}\"\n", ")\n", "print(f\"Interpolation {my_loss(y_interpolated, torch.tensor([])).detach().numpy()}\")" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 231, "id": "e0feb723-c91e-4288-b72b-c7345ef485ab", "metadata": {}, "outputs": [ @@ -698,8 +733,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "L1 loss Neural Network 0.1498335944290769\n", - "L1 loss interpolation 0.0006861782210489252\n" + "L1 loss Neural Network 0.0017719399180200234\n", + "L1 loss interpolation 1.2520488997390084e-07\n" ] } ], @@ -707,16 +742,16 @@ "# Calculate L1 performance\n", "my_loss = torch.nn.L1Loss()\n", "print(\n", - " f\"L1 loss Neural Network {my_loss(y_pred[:, :, diff:-diff, diff:-diff, diff:-diff], y_batch[:, :, diff:-diff, diff:-diff, diff:-diff])}\"\n", + " f\"L1 loss Neural Network {my_loss(y_pred[:, :, diff-1:-diff-1, diff-1:-diff-1, diff-1:-diff-1].cpu(), y_batch[:, :, diff-1:-diff-1, diff-1:-diff-1, diff-1:-diff-1].cpu())}\"\n", ")\n", "print(\n", - " f\"L1 loss interpolation {my_loss(y_interpolated, y_batch[:, :, diff:-diff, diff:-diff, diff:-diff])}\"\n", + " f\"L1 loss interpolation {my_loss(y_interpolated.cpu(), y_batch[:, :, diff-1:-diff-1, diff-1:-diff-1, diff-1:-diff-1].cpu())}\"\n", ")" ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 182, "id": "eb655716-a267-42b1-a6b4-e804d25507ee", "metadata": {}, "outputs": [], @@ -732,7 +767,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5233af4e-3394-429d-b02f-31d8e4cf6c8b", + "id": "ab70acd9-7aad-4002-bc28-9e4eb54b4946", "metadata": {}, "outputs": [], "source": [] @@ -740,9 +775,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "NGC PyTorch", "language": "python", - "name": "python3" + "name": "pytorch" }, "language_info": { "codemirror_mode": { @@ -754,7 +789,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.18" + "version": "3.10.12" } }, "nbformat": 4,