diff --git a/notebook.ipynb b/notebook.ipynb index 4275ae5..5bcfd6a 100644 --- a/notebook.ipynb +++ b/notebook.ipynb @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "9e6eda08-ff32-4ee3-9c35-f34b6d3a9a41", "metadata": { "scrolled": true @@ -50,194 +50,304 @@ "name": "stdout", "output_type": "stream", "text": [ - "Processing /data/tedwar42/thelfer1/TorchGRTL\n", + "Processing /Users/tom/Desktop/TorchGRTL\n", " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hRequirement already satisfied: torch in /home/thelfer1/dev/lib/python3.8/site-packages (from GeneralRelativity==0.1) (2.3.0)\n", - "Requirement already satisfied: black in /home/thelfer1/dev/lib/python3.8/site-packages (from GeneralRelativity==0.1) (24.4.1)\n", - "Requirement already satisfied: pre-commit in /home/thelfer1/dev/lib/python3.8/site-packages (from GeneralRelativity==0.1) (3.5.0)\n", - "Requirement already satisfied: pytest in 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\u001b[?25ldone\n", - "\u001b[?25h Created wheel for GeneralRelativity: filename=GeneralRelativity-0.1-py3-none-any.whl size=3380 sha256=90b5997ae22bf9c3be7c858d1a3c03f0ac78dfcbbd223b6a2bab238c01e1f725\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-fujysbzj/wheels/67/3f/b7/b7b472c1395273a8376b6665a5562d40b224035f0c4c3d28f9\n", + "\u001b[?25h Created wheel for GeneralRelativity: filename=GeneralRelativity-0.1-py3-none-any.whl size=3399 sha256=dae5bc717b1053142db7e5015270780cc10a9244ce3543f02b9c4fe68f18e953\n", + " Stored in directory: /private/var/folders/br/rfmwms_96jb8rrxcrsp81mqh0000gn/T/pip-ephem-wheel-cache-ho8fhkbd/wheels/7e/56/3a/dbd713ac6a7a2c158d2d7cbf655d63eb4a1db21d5035d47ca4\n", "Successfully built GeneralRelativity\n", "Installing collected packages: GeneralRelativity\n", - " Attempting uninstall: GeneralRelativity\n", - " Found existing installation: GeneralRelativity 0.1\n", - " 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;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\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;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\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 /home/thelfer1/dev/lib/python3.8/site-packages (2.0.3)\n", - "Requirement already satisfied: scikit-learn in /home/thelfer1/dev/lib/python3.8/site-packages (1.3.0)\n", - "Requirement already satisfied: tensorboard in /home/thelfer1/dev/lib/python3.8/site-packages (2.14.0)\n", 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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;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\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;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\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: pyinterpx in /home/thelfer1/dev/lib/python3.8/site-packages (0.2.3)\n", - "Requirement already satisfied: torch in /home/thelfer1/dev/lib/python3.8/site-packages (from pyinterpx) (2.3.0)\n", - "Requirement already satisfied: black in /home/thelfer1/dev/lib/python3.8/site-packages (from pyinterpx) (24.4.1)\n", - "Requirement already satisfied: pre-commit in 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"\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: scikit-learn in ./dev/lib/python3.8/site-packages (1.3.2)\n", + "Requirement already satisfied: numpy<2.0,>=1.17.3 in ./dev/lib/python3.8/site-packages (from scikit-learn) (1.24.4)\n", + "Requirement already satisfied: scipy>=1.5.0 in ./dev/lib/python3.8/site-packages (from scikit-learn) (1.10.1)\n", + "Requirement already satisfied: joblib>=1.1.1 in ./dev/lib/python3.8/site-packages (from scikit-learn) (1.4.2)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in ./dev/lib/python3.8/site-packages (from scikit-learn) (3.5.0)\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;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\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" ] } @@ -245,7 +355,8 @@ "source": [ "!pip install .\n", "!pip install pandas scikit-learn tensorboard torch tqdm\n", - "!pip install pyinterpx" + "!pip install pyinterpx\n", + "!pip install scikit-learn" ] }, { @@ -291,7 +402,7 @@ "from GeneralRelativity.Constraints import constraint_equations\n", "\n", "\n", - "from SuperResolution.models import SuperResolution3DNet, check_performance, load_model\n", + "from SuperResolution.models import SuperResolution3DNet, calculate_test_loss, load_model, check_performance\n", "from SuperResolution.losses import Hamiltonian_loss\n", "\n", "time_stamp = int(time.time())" @@ -307,7 +418,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Executing the model on : cuda:0\n" + "Executing the model on : cpu\n" ] } ], @@ -322,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "id": "4c57d113-0439-4c27-bcbc-c33f963c0c94", "metadata": {}, "outputs": [ @@ -336,12 +447,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4be8168418864342ba2cf6f2c436f805", + "model_id": "6962141693124fc4a760c5a12f154cfb", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/7 [00:00 Tuple[nn.Module, Dict[str, Any]]:\n", - " \"\"\"\n", - " Load the SuperResolution3DNet model from the checkpoint with the largest index\n", - " and read the configuration from the config.yaml file.\n", - "\n", - " Args:\n", - " directory_path (str): Path to the directory containing the checkpoint files and config.yaml.\n", - "\n", - " Returns:\n", - " Tuple[nn.Module, Dict[str, Any]]: The loaded model and configuration dictionary.\n", - " \"\"\"\n", - " # Pattern to match the checkpoint files\n", - " pattern = r\"model_epoch_counter_(\\d+)_data_time_\\d+\\.pth\"\n", - "\n", - " # List to store the checkpoint file names and their indices\n", - " checkpoints = []\n", - "\n", - " # Path to the config.yaml file\n", - " config_file_path = os.path.join(directory_path, \"config.yaml\")\n", - " print(f\"Config file path: {config_file_path}\")\n", - "\n", - " # Read Yaml config file\n", - " if os.path.exists(config_file_path):\n", - " with open(config_file_path, \"r\") as config_file:\n", - " config = yaml.safe_load(config_file)\n", - " else:\n", - " raise FileNotFoundError(f\"Config file not found at path: {config_file_path}\")\n", - "\n", - " # Iterate through the files in the directory to find checkpoint files\n", - " for filename in os.listdir(directory_path):\n", - " match = re.match(pattern, filename)\n", - " if match:\n", - " index = int(match.group(1))\n", - " checkpoints.append((index, filename))\n", - "\n", - " # Initialize the model with parameters from the config\n", - " factor = config[\"factor\"]\n", - " net = SuperResolution3DNet(factor, config[\"scaling_factor\"]).to(torch.double)\n", - "\n", - " # Find the checkpoint with the largest index\n", - " if checkpoints:\n", - " largest_index_checkpoint = max(checkpoints, key=lambda x: x[0])\n", - " largest_checkpoint_file = largest_index_checkpoint[1]\n", - " else:\n", - " raise FileNotFoundError(\"No checkpoint files found.\")\n", - "\n", - " path_to_largest_checkpoint_file = os.path.join(\n", - " directory_path, largest_checkpoint_file\n", - " )\n", - " # Load the model state if restarting\n", - " if os.path.exists(path_to_largest_checkpoint_file):\n", - " net.load_state_dict(torch.load(path_to_largest_checkpoint_file))\n", - " print(f\"loaded model from {path_to_largest_checkpoint_file}\")\n", - " else:\n", - " print(\n", - " f\"No restart or checkpoint file not found at path: {path_to_largest_checkpoint_file}\"\n", - " )\n", - "\n", - " return net, config" - ] - }, { "cell_type": "code", "execution_count": 54, @@ -605,7 +650,7 @@ "if restart and load_restart_data:\n", " num_vars = 25\n", " dataX = get_box_format(config[\"filenamesX\"], num_vars)\n", - " oneoverdx = (64.0 * 2 ** config[\"res_level\"]) / 512.0 * 2.0" + " oneoverdx = (64.0 * 2**res_level) / 512.0 * float(factor) / float(downsample)" ] }, { @@ -710,45 +755,46 @@ } ], "source": [ - "# Note: it will slow down signficantly with BFGS steps, they are 10x slower, just be aware!\n", - "ADAMsteps = 3 # Will perform # steps of ADAM steps and then switch over to BFGS-L\n", - "n_steps = 1 # Total amount of steps\n", - "\n", - "net.train()\n", - "net.to(device)\n", - "\n", - "# my_loss = torch.nn.L1Loss()\n", + "# For validation error\n", + "L1Loss = torch.nn.L1Loss()\n", + "ham_loss = Hamiltonian_and_momentum_loss(oneoverdx)\n", "\n", "print(\"training\")\n", "pbar = trange(n_steps)\n", "for i in pbar:\n", " total_loss_train = 0\n", " for (y_batch,) in train_loader:\n", + " net.train()\n", " batchcounter = 0\n", " # for X_batch, y_batch in train_loader:\n", " y_batch = y_batch.to(device)\n", - " X_batch = y_batch[:, :, :, :, :].clone()\n", - " y_batch = y_batch[\n", - " :, :25, diff - 1 : -diff - 1, diff - 1 : -diff - 1, diff - 1 : -diff - 1\n", - " ]\n", + " X_batch = y_batch[:, :, ::downsample, ::downsample, ::downsample].clone()\n", + " if diff != 0:\n", + " y_batch = y_batch[\n", + " :,\n", + " :25,\n", + " diff - 1 : -diff - 1,\n", + " diff - 1 : -diff - 1,\n", + " diff - 1 : -diff - 1,\n", + " ]\n", " batchcounter += 1\n", "\n", " # This is needed for LBFGS\n", " def closure():\n", " if torch.is_grad_enabled():\n", " optimizerBFGS.zero_grad()\n", - " y_pred, _ = net(X_batch)\n", + " y_pred, y_interp = net(X_batch)\n", "\n", - " loss_train = my_loss(y_pred)\n", + " loss_train = my_loss(y_pred, y_batch)\n", " if loss_train.requires_grad:\n", " loss_train.backward()\n", " return loss_train\n", "\n", " # doing some ADAM first to warm up, sometimes BFGS fuckes up if you start too early\n", " if counter < ADAMsteps:\n", - " y_pred, _ = net(X_batch)\n", + " y_pred, y_interp = net(X_batch)\n", "\n", - " loss_train = my_loss(y_pred)\n", + " loss_train = my_loss(y_pred, y_batch)\n", " optimizerADAM.zero_grad()\n", " loss_train.backward()\n", " optimizerADAM.step()\n", @@ -760,10 +806,10 @@ "\n", " loss_train = closure()\n", " total_loss_train += loss_train.item()\n", + "\n", " # Calculate the average training loss\n", " average_loss_train = total_loss_train / len(train_loader)\n", - " print(average_loss_train)\n", - " # Log the average training loss\n", + " # Log the average training loss \n", " writer.add_scalar(\"loss/train\", average_loss_train, counter)\n", " losses_train.append(average_loss_train)\n", " if np.isnan(average_loss_train):\n", @@ -773,25 +819,47 @@ "\n", " if counter % 1 == 0:\n", " with torch.no_grad():\n", + " net.eval()\n", " total_loss_val = 0.0\n", " interp_val = 0.0\n", + " L1Loss_val = 0.0\n", + " L1Loss_val_interp = 0.0\n", + " loss_hard_base = 0.0\n", + " Ham_loss = 0.0\n", + " Ham_loss_interp = 0.0\n", " for (y_val_batch,) in test_loader:\n", " # for X_val_batch, y_val_batch in test_loader:\n", " # Transfer batch to GPU\n", " y_val_batch = y_val_batch.to(device)\n", - " X_val_batch = y_val_batch[:, :, :, :, :].clone()\n", - " y_val_batch = y_val_batch[\n", - " :,\n", - " :25,\n", - " diff - 1 : -diff - 1,\n", - " diff - 1 : -diff - 1,\n", - " diff - 1 : -diff - 1,\n", - " ]\n", - " y_val_interp = net.interpolation(X_val_batch)\n", - " y_val_pred, _ = net(X_val_batch)\n", - " loss_val = my_loss(y_val_pred)\n", + " X_val_batch = y_val_batch[\n", + " :, :, ::downsample, ::downsample, ::downsample\n", + " ].clone()\n", + " if diff != 0:\n", + " y_val_batch = y_val_batch[\n", + " :,\n", + " :25,\n", + " diff - 1 : -diff - 1,\n", + " diff - 1 : -diff - 1,\n", + " diff - 1 : -diff - 1,\n", + " ]\n", + " y_val_pred, y_val_interp = net(X_val_batch)\n", + " loss_val = my_loss(y_val_pred, y_val_batch)\n", " total_loss_val += loss_val.item()\n", - " interp_val += my_loss(y_val_interp).item()\n", + " interp_val += my_loss(y_val_interp, y_val_batch).item()\n", + " if downsample == factor:\n", + " print(y_val_pred[:, 0, :, :, :].shape)\n", + " print(y_val_batch[:, 0, :, :, :].shape)\n", + " L1Loss_val += L1Loss(\n", + " y_val_pred[:, 0, :, :, :], y_val_batch[:, 0, :, :, :]\n", + " )\n", + " L1Loss_val_interp += L1Loss(\n", + " y_val_interp[:, 0, :, :, :], y_val_batch[:, 0, :, :, :]\n", + " )\n", + " loss_hard_base += ham_loss(y_val_batch, None)\n", + "\n", + " if config[\"loss\"] == \"L1\":\n", + " Ham_loss_interp += ham_loss(y_val_interp, None)\n", + " Ham_loss += ham_loss(y_val_pred, None)\n", " # Calculate the average loss\n", " average_loss_val = total_loss_val / len(test_loader)\n", " average_interp_val = interp_val / len(test_loader)\n", @@ -799,8 +867,14 @@ " losses_val.append(average_loss_val)\n", " steps_val.append(counter)\n", " writer.add_scalar(\"loss/test\", loss_val.item(), counter)\n", - " writer.add_scalar(\"loss/test\", loss_val.item(), counter)\n", "\n", + "\n", + " if counter % write_out_freq == 0:\n", + " # Writing out network and scaler\n", + " torch.save(\n", + " net.state_dict(),\n", + " f\"{folder_name}/model_epoch_counter_{counter:010d}_data_time_{time_stamp}.pth\",\n", + " )\n", " # Advancing global counter\n", " counter += 1" ] @@ -1131,7 +1205,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.8.18" } }, "nbformat": 4,