platform-demo-scripts/notebooks/Present model predictions.ipynb

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   "id": "55ef77b9-9320-44db-862b-088d7af03112",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mkmilian\u001b[0m (\u001b[33mepos\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /Users/krystynamilian/.netrc\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from obspy.core.event import read_events\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seisbench.models as sbm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "import seisbench.data as sbd\n",
    "import seisbench.generate as sbg\n",
    "import seisbench.models as sbm\n",
    "from seisbench.util import worker_seeding\n",
    "import numpy as np\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "import wandb\n",
    "import os\n",
    "import sys\n",
    "\n",
    "from pathlib import Path\n",
    "project_path = str(Path.cwd().parent)\n",
    "sys.path.append(project_path)\n",
    "from scripts import train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "694ceb35-d2f3-4654-a6af-d84e494a4660",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "wandb version 0.15.4 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
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     "data": {
      "text/html": [
       "Tracking run with wandb version 0.15.3"
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       "<IPython.core.display.HTML object>"
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     "data": {
      "text/html": [
       "Run data is saved locally in <code>/Users/krystynamilian/Documents/praca/Cyfronet/epos/ai/repo/demo_scripts/notebooks/wandb/run-20230704_110602-ufltoqra</code>"
      ],
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      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/epos/training_seisbench_models_on_igf_data/runs/ufltoqra' target=\"_blank\">polished-totem-255</a></strong> to <a href='https://wandb.ai/epos/training_seisbench_models_on_igf_data' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/epos/training_seisbench_models_on_igf_data' target=\"_blank\">https://wandb.ai/epos/training_seisbench_models_on_igf_data</a>"
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/epos/training_seisbench_models_on_igf_data/runs/ufltoqra' target=\"_blank\">https://wandb.ai/epos/training_seisbench_models_on_igf_data/runs/ufltoqra</a>"
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       "<IPython.core.display.HTML object>"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
     ]
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   ],
   "source": [
    "run = wandb.init(project=\"training_seisbench_models_on_igf_data\", entity=\"epos\", mode=\"online\")\n",
    "artifact = run.use_artifact('epos/model-registry/phasenet_p:v0', type='model')\n",
    "artifact_dir = artifact.download()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0589f4aa-e5a4-485e-9213-a4696c38a60c",
   "metadata": {},
   "source": [
    "# Load model, get data loaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9935a11d-e8b5-4019-aafa-f031f1024a71",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = train.load_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "922e8062-e503-4958-b192-b274453d64c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./artifacts/model:v4'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "artifact_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7e100ac5-af2b-4d20-95f3-4f0a6a4fc0ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "fname = artifact_dir + \"/\" + os.listdir(artifact_dir)[0]\n",
    "# fname \n",
    "# fname = \"../models/PhaseNet_pretrained_on_iquique_finetuned_on_igf_1.pt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ffdf1280-2b25-49a3-9e6b-38a85a9c5501",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PhaseNet(\n",
       "  (inc): Conv1d(3, 8, kernel_size=(7,), stride=(1,), padding=same)\n",
       "  (in_bn): BatchNorm1d(8, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (down_branch): ModuleList(\n",
       "    (0): ModuleList(\n",
       "      (0): Conv1d(8, 8, kernel_size=(7,), stride=(1,), padding=same, bias=False)\n",
       "      (1): BatchNorm1d(8, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): Conv1d(8, 8, kernel_size=(7,), stride=(4,), padding=(3,), bias=False)\n",
       "      (3): BatchNorm1d(8, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (1): ModuleList(\n",
       "      (0): Conv1d(8, 16, kernel_size=(7,), stride=(1,), padding=same, bias=False)\n",
       "      (1): BatchNorm1d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): Conv1d(16, 16, kernel_size=(7,), stride=(4,), bias=False)\n",
       "      (3): BatchNorm1d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (2): ModuleList(\n",
       "      (0): Conv1d(16, 32, kernel_size=(7,), stride=(1,), padding=same, bias=False)\n",
       "      (1): BatchNorm1d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): Conv1d(32, 32, kernel_size=(7,), stride=(4,), bias=False)\n",
       "      (3): BatchNorm1d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (3): ModuleList(\n",
       "      (0): Conv1d(32, 64, kernel_size=(7,), stride=(1,), padding=same, bias=False)\n",
       "      (1): BatchNorm1d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): Conv1d(64, 64, kernel_size=(7,), stride=(4,), bias=False)\n",
       "      (3): BatchNorm1d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (4): ModuleList(\n",
       "      (0): Conv1d(64, 128, kernel_size=(7,), stride=(1,), padding=same, bias=False)\n",
       "      (1): BatchNorm1d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2-3): 2 x None\n",
       "    )\n",
       "  )\n",
       "  (up_branch): ModuleList(\n",
       "    (0): ModuleList(\n",
       "      (0): ConvTranspose1d(128, 64, kernel_size=(7,), stride=(4,), bias=False)\n",
       "      (1): BatchNorm1d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): Conv1d(128, 64, kernel_size=(7,), stride=(1,), padding=same, bias=False)\n",
       "      (3): BatchNorm1d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (1): ModuleList(\n",
       "      (0): ConvTranspose1d(64, 32, kernel_size=(7,), stride=(4,), bias=False)\n",
       "      (1): BatchNorm1d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): Conv1d(64, 32, kernel_size=(7,), stride=(1,), padding=same, bias=False)\n",
       "      (3): BatchNorm1d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (2): ModuleList(\n",
       "      (0): ConvTranspose1d(32, 16, kernel_size=(7,), stride=(4,), bias=False)\n",
       "      (1): BatchNorm1d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): Conv1d(32, 16, kernel_size=(7,), stride=(1,), padding=same, bias=False)\n",
       "      (3): BatchNorm1d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (3): ModuleList(\n",
       "      (0): ConvTranspose1d(16, 8, kernel_size=(7,), stride=(4,), bias=False)\n",
       "      (1): BatchNorm1d(8, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): Conv1d(16, 8, kernel_size=(7,), stride=(1,), padding=same, bias=False)\n",
       "      (3): BatchNorm1d(8, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (out): Conv1d(8, 2, kernel_size=(1,), stride=(1,), padding=same)\n",
       "  (softmax): Softmax(dim=1)\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_state_dict(torch.load(fname))\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4656e5fe-bb7b-4564-923a-8af385eb312d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train (12444, 17) 100\n",
      "using random window\n",
      "dev (2773, 17) 100\n",
      "using random window\n",
      "test (2785, 17) 100\n",
      "using random window\n"
     ]
    }
   ],
   "source": [
    "train_gen, dev_gen, test_gen = train.get_data_generators()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dfdaa857-9f2b-419f-8f28-9ce8f4b11a0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_sample(sample, model, i): \n",
    "    fig = plt.figure(figsize=(15, 10))\n",
    "    fig.suptitle(\"Predictions for test sample: \" + str(i))\n",
    "    axs = fig.subplots(2, 1, sharex=True, gridspec_kw={\"hspace\": 0, \"height_ratios\": [3,  2]})\n",
    "    axs[0].plot(sample[\"X\"][0].T, label='x')\n",
    "    plt.legend()\n",
    "    axs[1].plot(sample[\"y\"][0].T, label='y')\n",
    "    \n",
    "    model.eval()  # close the model for evaluation\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        pred = model(torch.tensor(sample[\"X\"], device=model.device).unsqueeze(0))  # Add a fake batch dimension\n",
    "        pred = pred[0].cpu().numpy()\n",
    "        pick_idx = np.argmax(pred[0])\n",
    "        print(pred.shape)\n",
    "    \n",
    "        axs[1].plot(pred[0], label='pred', color='orange')\n",
    "        plt.legend()\n",
    "        \n",
    "        plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "26ee3888-8138-4d02-a5f8-a899b8ca6436",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 3001)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = np.random.randint(len(test_gen))\n",
    "sample = test_gen[idx]\n",
    "plot_sample(sample, model, idx)\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}