Skip to content

Commit

Permalink
Fix tutorial5
Browse files Browse the repository at this point in the history
  • Loading branch information
@FilippoOlivo
FilippoOlivo committed Oct 31, 2024
1 parent 26e5e00 commit cbbf15d
Showing 1 changed file with 65 additions and 89 deletions.
154 changes: 65 additions & 89 deletions tutorials/tutorial5/tutorial.ipynb
View file
Original file line number Diff line number Diff line change
Expand Up @@ -22,8 +22,8 @@
"id": "5f2744dc",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-19T13:35:28.837348Z",
"start_time": "2024-09-19T13:35:27.611334Z"
"end_time": "2024-10-31T13:30:49.886194Z",
"start_time": "2024-10-31T13:30:48.624214Z"
}
},
"source": [
Expand Down Expand Up @@ -61,23 +61,27 @@
"id": "2ffb8a4c",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-19T13:35:28.989631Z",
"start_time": "2024-09-19T13:35:28.952744Z"
"end_time": "2024-10-31T13:30:49.920642Z",
"start_time": "2024-10-31T13:30:49.888843Z"
}
},
"source": [
"# download the dataset\n",
"data = io.loadmat(\"Data_Darcy.mat\")\n",
"\n",
"# extract data (we use only 100 data for train)\n",
"k_train = LabelTensor(torch.tensor(data['k_train'], dtype=torch.float).unsqueeze(-1), \n",
" labels={3:{'dof': ['u0'], 'name': 'k_train'}})\n",
"u_train = LabelTensor(torch.tensor(data['u_train'], dtype=torch.float).unsqueeze(-1),\n",
" labels={3:{'dof': ['u'], 'name': 'u_train'}})\n",
"k_test = LabelTensor(torch.tensor(data['k_test'], dtype=torch.float).unsqueeze(-1),\n",
" labels={3:{'dof': ['u0'], 'name': 'k_test'}})\n",
"u_test= LabelTensor(torch.tensor(data['u_test'], dtype=torch.float).unsqueeze(-1),\n",
" labels={3:{'dof': ['u'], 'name': 'u_test'}})\n",
"k_train = LabelTensor(\n",
" torch.tensor(data['k_train'], dtype=torch.float).unsqueeze(-1),\n",
" labels={3: {'dof': ['u0'], 'name': 'k_train'}})\n",
"u_train = LabelTensor(\n",
" torch.tensor(data['u_train'], dtype=torch.float).unsqueeze(-1),\n",
" labels={3: {'dof': ['u'], 'name': 'u_train'}})\n",
"k_test = LabelTensor(\n",
" torch.tensor(data['k_test'], dtype=torch.float).unsqueeze(-1),\n",
" labels={3: {'dof': ['u0'], 'name': 'k_test'}})\n",
"u_test = LabelTensor(\n",
" torch.tensor(data['u_test'], dtype=torch.float).unsqueeze(-1),\n",
" labels={3: {'dof': ['u'], 'name': 'u_test'}})\n",
"x = torch.tensor(data['x'], dtype=torch.float)[0]\n",
"y = torch.tensor(data['y'], dtype=torch.float)[0]"
],
Expand All @@ -97,8 +101,8 @@
"id": "c8501b6f",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-19T13:35:29.108381Z",
"start_time": "2024-09-19T13:35:29.031076Z"
"end_time": "2024-10-31T13:30:50.101674Z",
"start_time": "2024-10-31T13:30:50.034859Z"
}
},
"source": [
Expand All @@ -124,32 +128,6 @@
],
"execution_count": 3
},
{
"cell_type": "code",
"id": "082ab7a8-22e0-498b-b138-158dc9f2658f",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-19T13:35:29.122858Z",
"start_time": "2024-09-19T13:35:29.119985Z"
}
},
"source": [
"u_train.labels[3]['dof']"
],
"outputs": [
{
"data": {
"text/plain": [
"['u']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 4
},
{
"cell_type": "markdown",
"id": "89a77ff1",
Expand All @@ -163,33 +141,23 @@
"id": "8b27d283",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-19T13:35:29.136572Z",
"start_time": "2024-09-19T13:35:29.134124Z"
"end_time": "2024-10-31T13:30:50.191764Z",
"start_time": "2024-10-31T13:30:50.189977Z"
}
},
"source": [
"class NeuralOperatorSolver(AbstractProblem):\n",
" input_variables = k_train.labels[3]['dof']\n",
" output_variables = u_train.labels[3]['dof']\n",
" domains = {\n",
" 'pts': k_train\n",
" }\n",
" conditions = {'data' : Condition(domain='pts', \n",
" output_points=u_train)}\n",
" input_variables = k_train.labels\n",
" output_variables = u_train.labels\n",
" conditions = {'data': Condition(input_points=k_train,\n",
" output_points=u_train)}\n",
"\n",
"\n",
"# make problem\n",
"problem = NeuralOperatorSolver()"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<pina.condition.domain_output_condition.DomainOutputCondition object at 0x1259893b0>\n"
]
}
],
"execution_count": 5
"outputs": [],
"execution_count": 4
},
{
"cell_type": "markdown",
Expand All @@ -206,20 +174,21 @@
"id": "e34f18b0",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-19T13:35:31.245429Z",
"start_time": "2024-09-19T13:35:29.154937Z"
"end_time": "2024-10-31T13:30:52.528635Z",
"start_time": "2024-10-31T13:30:50.225049Z"
}
},
"source": [
"# make model\n",
"model = FeedForward(input_dimensions=1, output_dimensions=1)\n",
"\n",
"\n",
"# make solver\n",
"solver = SupervisedSolver(problem=problem, model=model)\n",
"\n",
"# make the trainer and train\n",
"trainer = Trainer(solver=solver, max_epochs=10, accelerator='cpu', enable_model_summary=False, batch_size=10) \n",
"trainer = Trainer(solver=solver, max_epochs=10, accelerator='cpu',\n",
" enable_model_summary=False, batch_size=10, train_size=1.,\n",
" test_size=0., val_size=0.)\n",
"# We train on CPU and avoid model summary at the beginning of training (optional)\n",
"trainer.train()"
],
Expand All @@ -231,14 +200,15 @@
"GPU available: True (mps), used: False\n",
"TPU available: False, using: 0 TPU cores\n",
"HPU available: False, using: 0 HPUs\n",
"/Users/filippoolivo/miniconda3/envs/PINAv0.2/lib/python3.11/site-packages/pytorch_lightning/trainer/setup.py:177: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n"
"/Users/filippoolivo/miniconda3/envs/PINAv0.2-test/lib/python3.11/site-packages/pytorch_lightning/trainer/setup.py:177: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n",
"/Users/filippoolivo/miniconda3/envs/PINAv0.2-test/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:75: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 9: 100%|██████████| 100/100 [00:00<00:00, 552.80it/s, v_num=18, mean_loss=0.113]"
"Epoch 9: 100%|██████████| 100/100 [00:00<00:00, 459.79it/s, v_num=58, mean_loss=0.108]"
]
},
{
Expand All @@ -252,11 +222,11 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 9: 100%|██████████| 100/100 [00:00<00:00, 547.37it/s, v_num=18, mean_loss=0.113]\n"
"Epoch 9: 100%|██████████| 100/100 [00:00<00:00, 456.20it/s, v_num=58, mean_loss=0.108]\n"
]
}
],
"execution_count": 6
"execution_count": 5
},
{
"cell_type": "markdown",
Expand All @@ -271,8 +241,8 @@
"id": "0e2a6aa4",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-19T13:35:31.295336Z",
"start_time": "2024-09-19T13:35:31.256308Z"
"end_time": "2024-10-31T13:30:52.600181Z",
"start_time": "2024-10-31T13:30:52.564456Z"
}
},
"source": [
Expand All @@ -282,10 +252,12 @@
"metric_err = LpLoss(relative=True)\n",
"\n",
"model = solver.models[0]\n",
"err = float(metric_err(u_train.squeeze(-1), model(k_train).squeeze(-1)).mean())*100\n",
"err = float(\n",
" metric_err(u_train.squeeze(-1), model(k_train).squeeze(-1)).mean()) * 100\n",
"print(f'Final error training {err:.2f}%')\n",
"\n",
"err = float(metric_err(u_test.squeeze(-1), model(k_test).squeeze(-1)).mean())*100\n",
"err = float(\n",
" metric_err(u_test.squeeze(-1), model(k_test).squeeze(-1)).mean()) * 100\n",
"print(f'Final error testing {err:.2f}%')"
],
"outputs": [
Expand All @@ -294,11 +266,11 @@
"output_type": "stream",
"text": [
"Final error training 56.05%\n",
"Final error testing 55.95%\n"
"Final error testing 56.02%\n"
]
}
],
"execution_count": 7
"execution_count": 6
},
{
"cell_type": "markdown",
Expand All @@ -315,8 +287,8 @@
"id": "9af523a5",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-19T13:35:44.717807Z",
"start_time": "2024-09-19T13:35:31.306689Z"
"end_time": "2024-10-31T13:31:06.537460Z",
"start_time": "2024-10-31T13:30:52.694424Z"
}
},
"source": [
Expand All @@ -330,12 +302,14 @@
" inner_size=24,\n",
" padding=8)\n",
"\n",
"\n",
"# make solver\n",
"solver = SupervisedSolver(problem=problem, model=model)\n",
"\n",
"# make the trainer and train\n",
"trainer = Trainer(solver=solver, max_epochs=10, accelerator='cpu', enable_model_summary=False, batch_size=10) # we train on CPU and avoid model summary at beginning of training (optional)\n",
"trainer = Trainer(solver=solver, max_epochs=10, accelerator='cpu',\n",
" enable_model_summary=False, batch_size=10, train_size=1.,\n",
" test_size=0.,\n",
" val_size=0.) # we train on CPU and avoid model summary at beginning of training (optional)\n",
"trainer.train()"
],
"outputs": [
Expand All @@ -352,7 +326,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 9: 100%|██████████| 100/100 [00:01<00:00, 73.04it/s, v_num=19, mean_loss=0.00215]"
"Epoch 9: 100%|██████████| 100/100 [00:01<00:00, 72.88it/s, v_num=59, mean_loss=0.00291]"
]
},
{
Expand All @@ -366,11 +340,11 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 9: 100%|██████████| 100/100 [00:01<00:00, 72.84it/s, v_num=19, mean_loss=0.00215]\n"
"Epoch 9: 100%|██████████| 100/100 [00:01<00:00, 72.69it/s, v_num=59, mean_loss=0.00291]\n"
]
}
],
"execution_count": 8
"execution_count": 7
},
{
"cell_type": "markdown",
Expand All @@ -385,30 +359,32 @@
"id": "58e2db89",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-19T13:35:45.259819Z",
"start_time": "2024-09-19T13:35:44.729042Z"
"end_time": "2024-10-31T13:31:07.134298Z",
"start_time": "2024-10-31T13:31:06.657198Z"
}
},
"source": [
"model = solver.models[0]\n",
"\n",
"err = float(metric_err(u_train.squeeze(-1), model(k_train).squeeze(-1)).mean())*100\n",
"err = float(\n",
" metric_err(u_train.squeeze(-1), model(k_train).squeeze(-1)).mean()) * 100\n",
"print(f'Final error training {err:.2f}%')\n",
"\n",
"err = float(metric_err(u_test.squeeze(-1), model(k_test).squeeze(-1)).mean())*100\n",
"err = float(\n",
" metric_err(u_test.squeeze(-1), model(k_test).squeeze(-1)).mean()) * 100\n",
"print(f'Final error testing {err:.2f}%')"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final error training 7.48%\n",
"Final error testing 7.73%\n"
"Final error training 7.21%\n",
"Final error testing 7.39%\n"
]
}
],
"execution_count": 9
"execution_count": 8
},
{
"cell_type": "markdown",
Expand All @@ -431,8 +407,8 @@
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-19T13:08:35.195331Z",
"start_time": "2024-09-19T13:08:35.193830Z"
"end_time": "2024-10-31T13:31:07.231206Z",
"start_time": "2024-10-31T13:31:07.230017Z"
}
},
"cell_type": "code",
Expand Down

0 comments on commit cbbf15d

Please sign in to comment.