From 2c1637972047101e52f4960e62252323d1505013 Mon Sep 17 00:00:00 2001 From: ThomasHelfer Date: Mon, 22 Jan 2024 10:32:12 -0500 Subject: [PATCH] Hack AstroML --- notebook.ipynb | 158 +++++++++++++++++++++++-------------------------- 1 file changed, 73 insertions(+), 85 deletions(-) diff --git a/notebook.ipynb b/notebook.ipynb index ea8b2cc..4ad22f7 100644 --- a/notebook.ipynb +++ b/notebook.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "id": "9e6eda08-ff32-4ee3-9c35-f34b6d3a9a41", "metadata": {}, "outputs": [ @@ -55,8 +55,8 @@ "Requirement already satisfied: mpmath>=0.19 in ./dev/lib/python3.8/site-packages (from sympy->torch->GeneralRelativity==0.1) (1.3.0)\n", "Building wheels for collected packages: GeneralRelativity\n", " Building wheel for GeneralRelativity (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for GeneralRelativity: filename=GeneralRelativity-0.1-py3-none-any.whl size=3275 sha256=9d1d3ca6ce338ebd7c3b1817310e685dddf452dc35edb2aef5709fd6adb12a39\n", - " Stored in directory: /private/var/folders/br/rfmwms_96jb8rrxcrsp81mqh0000gn/T/pip-ephem-wheel-cache-u8naljlw/wheels/4a/14/b4/9ad481489b414caef94a9ad5da3310a1c3acfbd67da647d64b\n", + "\u001b[?25h Created wheel for GeneralRelativity: filename=GeneralRelativity-0.1-py3-none-any.whl size=3275 sha256=4d87151ba318d900e51079eba82c86c5a0690db77c7e92c83422b0d7b9cacf44\n", + " Stored in directory: /private/var/folders/br/rfmwms_96jb8rrxcrsp81mqh0000gn/T/pip-ephem-wheel-cache-84s2m0tk/wheels/4a/14/b4/9ad481489b414caef94a9ad5da3310a1c3acfbd67da647d64b\n", "Successfully built GeneralRelativity\n", "Installing collected packages: GeneralRelativity\n", " Attempting uninstall: GeneralRelativity\n", @@ -64,9 +64,6 @@ " Uninstalling GeneralRelativity-0.1:\n", " Successfully uninstalled GeneralRelativity-0.1\n", "Successfully installed GeneralRelativity-0.1\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;49m23.3.1\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;49mpip install --upgrade pip\u001b[0m\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", @@ -101,10 +98,7 @@ "Requirement already satisfied: MarkupSafe>=2.1.1 in ./dev/lib/python3.8/site-packages (from werkzeug>=1.0.1->tensorboard) (2.1.3)\n", "Requirement already satisfied: zipp>=0.5 in ./dev/lib/python3.8/site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard) (3.17.0)\n", "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in ./dev/lib/python3.8/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard) (0.5.1)\n", - "Requirement already satisfied: oauthlib>=3.0.0 in ./dev/lib/python3.8/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->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;49m23.3.1\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;49mpip install --upgrade pip\u001b[0m\n" + "Requirement already satisfied: oauthlib>=3.0.0 in ./dev/lib/python3.8/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard) (3.2.2)\n" ] } ], @@ -115,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 2, "id": "113757c9-d051-41d3-9eab-5c65bc8c1098", "metadata": {}, "outputs": [], @@ -160,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 3, "id": "0581229a-6848-4bde-b6db-8bc5fdff61e5", "metadata": {}, "outputs": [ @@ -183,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 4, "id": "4c57d113-0439-4c27-bcbc-c33f963c0c94", "metadata": {}, "outputs": [ @@ -197,7 +191,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c94ecd7653e44343a39c450622c6b927", + "model_id": "bf6ef09089a84f6883e7796256f4fb8a", "version_major": 2, "version_minor": 0 }, @@ -220,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 17, "id": "771b86ac-4f14-477b-b969-b8499b75e560", "metadata": {}, "outputs": [], @@ -250,7 +244,7 @@ " x = self.encoder(x)\n", " x = self.decoder(x)\n", " # Reusing the input data for faster learning\n", - " x[:, :, ::2, ::2, ::2] = tmp[:, :25, ...]\n", + " x[:, :, ::2, ::2, ::2] = tmp[:, :, ...]\n", " return x\n", "\n", "\n", @@ -260,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 18, "id": "456b85c7-a008-43bd-9dd5-153b043d7ee6", "metadata": {}, "outputs": [], @@ -274,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 19, "id": "823b3067-1b41-4cb4-894b-32b3f14884e1", "metadata": {}, "outputs": [ @@ -304,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 20, "id": "748ebfa6-d35a-4e65-8ca0-b7ee0e212d2b", "metadata": {}, "outputs": [], @@ -317,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 21, "id": "6999db8f-b73b-4a36-b0a0-b70caaed8b0d", "metadata": {}, "outputs": [], @@ -341,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 22, "id": "1a7058ed-a1f3-4af5-ba97-f816558320c7", "metadata": {}, "outputs": [], @@ -377,7 +371,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 23, "id": "2ceaf420-791f-40cf-8a0c-f4537bcf85c3", "metadata": {}, "outputs": [], @@ -391,14 +385,14 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 24, "id": "f8f10c9c-b601-4ba0-b51e-c8377cc54e9c", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2a2b72727fc445d3864a75686292a00d", + "model_id": "25f3a8aa4f2043abbf46f2f6a2402706", "version_major": 2, "version_minor": 0 }, @@ -410,22 +404,14 @@ "output_type": "display_data" }, { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[71], line 41\u001b[0m\n\u001b[1;32m 37\u001b[0m optimizerADAM\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m 38\u001b[0m \u001b[38;5;66;03m#print(f'ADAM {batchcounter}')\u001b[39;00m\n\u001b[1;32m 39\u001b[0m \n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 41\u001b[0m \u001b[43moptimizerBFGS\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosure\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 42\u001b[0m \u001b[38;5;66;03m#print(f'BFGS {batchcounter}')\u001b[39;00m\n\u001b[1;32m 45\u001b[0m output \u001b[38;5;241m=\u001b[39m net(X_batch)\n", - "File \u001b[0;32m~/Desktop/temp_folder_trasnsfer/TorchGRTL/dev/lib/python3.8/site-packages/torch/optim/optimizer.py:373\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 368\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 369\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 370\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 371\u001b[0m )\n\u001b[0;32m--> 373\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 374\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[1;32m 376\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n", - "File \u001b[0;32m~/Desktop/temp_folder_trasnsfer/TorchGRTL/dev/lib/python3.8/site-packages/torch/utils/_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator..decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 115\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Desktop/temp_folder_trasnsfer/TorchGRTL/dev/lib/python3.8/site-packages/torch/optim/lbfgs.py:438\u001b[0m, in \u001b[0;36mLBFGS.step\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 433\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_iter \u001b[38;5;241m!=\u001b[39m max_iter:\n\u001b[1;32m 434\u001b[0m \u001b[38;5;66;03m# re-evaluate function only if not in last iteration\u001b[39;00m\n\u001b[1;32m 435\u001b[0m \u001b[38;5;66;03m# the reason we do this: in a stochastic setting,\u001b[39;00m\n\u001b[1;32m 436\u001b[0m \u001b[38;5;66;03m# no use to re-evaluate that function here\u001b[39;00m\n\u001b[1;32m 437\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39menable_grad():\n\u001b[0;32m--> 438\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(\u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 439\u001b[0m flat_grad \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gather_flat_grad()\n\u001b[1;32m 440\u001b[0m opt_cond \u001b[38;5;241m=\u001b[39m flat_grad\u001b[38;5;241m.\u001b[39mabs()\u001b[38;5;241m.\u001b[39mmax() \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m tolerance_grad\n", - "File \u001b[0;32m~/Desktop/temp_folder_trasnsfer/TorchGRTL/dev/lib/python3.8/site-packages/torch/utils/_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator..decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 115\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[71], line 27\u001b[0m, in \u001b[0;36mclosure\u001b[0;34m()\u001b[0m\n\u001b[1;32m 25\u001b[0m loss_train \u001b[38;5;241m=\u001b[39m my_loss(y_pred,y_batch)\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m loss_train\u001b[38;5;241m.\u001b[39mrequires_grad:\n\u001b[0;32m---> 27\u001b[0m \u001b[43mloss_train\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss_train\n", - "File \u001b[0;32m~/Desktop/temp_folder_trasnsfer/TorchGRTL/dev/lib/python3.8/site-packages/torch/_tensor.py:492\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 482\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 483\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m 484\u001b[0m Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m 485\u001b[0m (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 490\u001b[0m inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m 491\u001b[0m )\n\u001b[0;32m--> 492\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 493\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[1;32m 494\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Desktop/temp_folder_trasnsfer/TorchGRTL/dev/lib/python3.8/site-packages/torch/autograd/__init__.py:251\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 246\u001b[0m retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m 248\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[1;32m 249\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m 250\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 251\u001b[0m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m 252\u001b[0m \u001b[43m \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 253\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 254\u001b[0m \u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 255\u001b[0m \u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 256\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 257\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 258\u001b[0m \u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 259\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] + "data": { + "image/png": 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", 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", 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" ] @@ -555,7 +541,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 44, "id": "d1111cd8-4e85-4e05-bc75-96eb4281ac91", "metadata": {}, "outputs": [], @@ -567,7 +553,7 @@ "points = 6\n", "power = 3\n", "shape = X_batch.shape\n", - "interpolation = interp(points, power)\n", + "interpolation = interp(points, power,False)\n", "ghosts = int(math.ceil(points / 2))\n", "shape_higher_order = (shape[-1] - 2 * ghosts) * 2 + 2\n", "\n", @@ -579,25 +565,15 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 45, "id": "309d8ad4-fd6d-4376-ace4-3fcb0d65e6be", "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "" - ] - }, - "execution_count": 92, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
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" ] }, "metadata": {}, @@ -608,40 +584,51 @@ "box = 0\n", "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", "\n", - "plt.subplot(1, 3, 1)\n", - "plt.title(\"ground truth\")\n", - "plt.imshow(y_batch[box, channel, diff:-diff, diff:-diff, slice].detach().cpu().numpy(), vmin=min_val, vmax=max_val)\n", - "plt.subplot(1, 3, 2)\n", - "plt.title(\"Neural Network\")\n", - "plt.imshow(y_pred[box, channel, diff:-diff, diff:-diff, slice].detach().cpu().numpy(), vmin=min_val, vmax=max_val)\n", - "plt.subplot(1, 3, 3)\n", - "plt.title(\"Interpolation\")\n", - "plt.imshow(y_interpolated[box, channel, :, :, slice - diff].detach().cpu().numpy(), vmin=min_val, vmax=max_val)\n", - "plt.colorbar()" + "# 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", + "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", + "axes[1].set_xlabel('X-axis')\n", + "axes[1].set_ylabel('Y-axis')\n", + "\n", + "# Plot Interpolation\n", + "axes[2].set_title(\"Interpolation\")\n", + "im2 = axes[2].imshow(y_interpolated[box, channel, :, :, slice - diff], vmin=min_val, vmax=max_val, cmap='viridis')\n", + "axes[2].set_xlabel('X-axis')\n", + "axes[2].set_ylabel('Y-axis')\n", + "\n", + "# Add colorbars\n", + "cbar0 = fig.colorbar(im0, ax=axes[0])\n", + "#cbar0.set_label('Values')\n", + "cbar1 = fig.colorbar(im1, ax=axes[1])\n", + "#cbar1.set_label('Values')\n", + "cbar2 = fig.colorbar(im2, ax=axes[2])\n", + "#cbar2.set_label('Values')\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 46, "id": "ddd54cde-bbdc-45ba-8ae4-487b675bdf43", "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -669,12 +656,13 @@ " .numpy(),\n", " label=\"interpolation \",\n", ")\n", - "plt.legend()" + "plt.legend()\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 47, "id": "e2c3a78a-9a54-4772-82b0-bd8d43bcdeb6", "metadata": {}, "outputs": [ @@ -683,7 +671,7 @@ "output_type": "stream", "text": [ "Reference data L2 1.0373636182292944e-14\n", - "Neural Network L2 loss 2577657.4189774776\n", + "Neural Network L2 loss 58302717.69780393\n", "Interpolation 7.375914989893317e-09\n" ] } @@ -702,7 +690,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 48, "id": "e0feb723-c91e-4288-b72b-c7345ef485ab", "metadata": {}, "outputs": [ @@ -710,7 +698,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "L1 loss Neural Network 0.08621386981706632\n", + "L1 loss Neural Network 0.1498335944290769\n", "L1 loss interpolation 0.0006861782210489252\n" ] } @@ -728,7 +716,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 49, "id": "eb655716-a267-42b1-a6b4-e804d25507ee", "metadata": {}, "outputs": [],