diff --git a/PARCtorch/demos/.gitignore b/PARCtorch/demos/.gitignore new file mode 100644 index 0000000..36a49bd --- /dev/null +++ b/PARCtorch/demos/.gitignore @@ -0,0 +1,2 @@ +keypair keypair.pub +../data/ diff --git a/PARCtorch/demos/Burgers.ipynb b/PARCtorch/demos/Burgers.ipynb index ccc8b08..015cf60 100644 --- a/PARCtorch/demos/Burgers.ipynb +++ b/PARCtorch/demos/Burgers.ipynb @@ -141,7 +141,7 @@ "Install PARCtorch:\n", "- Ensure PARCtorch is installed in your Python Environment, you could view the installation instructions [here](https://github.com/baeklab/PARCtorch/tree/main#installation)\n", "- After the installation, install all the relevant dependencies with `!pip install -r requirements.txt` (The versions of your dependencies need to be consistent with our requirements, or else the script might not run!)\n", - "- Optional (only if the script doesn't run): add the root directory to the system's path so that " + "- Optional (only if the script doesn't run): add the root directory to the system's path so that it runs" ] }, { diff --git a/PARCtorch/demos/ShearFlow.ipynb b/PARCtorch/demos/ShearFlow.ipynb new file mode 100755 index 0000000..b1af3c1 --- /dev/null +++ b/PARCtorch/demos/ShearFlow.ipynb @@ -0,0 +1,61998 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a323a953-d55f-4a06-87fa-21a5890fa35b", + "metadata": {}, + "source": [ + "
\n", + "

\n", + " Physics Aware Recurrent Convolutional Neural Network (PARC): ShearFlow Equation Demo\n", + "

\n", + "
\n", + " \"Image\n", + " \"Image\n", + " \"Image\n", + "
\n", + "
\n", + "\n", + "

\n", + "A customizable framework to embed physics in Deep Learning.\n", + "PARC's formulation is inspired by Advection-Diffusion-Reaction processes and uses an Inductive Bias approach to constrain the Neural Network.\n", + "

\n" + ] + }, + { + "cell_type": "markdown", + "id": "93a45e9e-6a11-4de3-a329-be4ed5ef50eb", + "metadata": {}, + "source": [ + "# Modeling Shear Flow with PARC\n", + "\n", + "This notebook aims to simulate the 2D periodic shear flow dynamics, a simplified version of the Navier-Stokes equation in a condition that it is incompressible (without consideration for pressure). A shear flow is a type of fluid that contains layers sliding past each other at varying velocities.\n", + "\n", + "The notebook will guide you through from start to finish in preparing, training, and modeling the physics-based equation's prediction results. The notebook primarily covers three sections:\n", + "- loading and preparing the data for the Shear Flow's Equation\n", + "- Using the PARC Model to learn and predict the time evolution of velocity fields $u$ and tracer distributions $s$\n", + "- Evaluating the model's performance and compare predicted results to ground truth" + ] + }, + { + "cell_type": "markdown", + "id": "f9143c39-6bbd-473e-8d5a-ba97446cdc68", + "metadata": {}, + "source": [ + "# What exactly is a Shear Flow?\n", + "\n", + "Shear Flows can be observed in many real life scenarios. For example, the air closest to an airplane's wings moves slower due to friction, while the air further way moves faster. This velocity difference in turn creates a shear flow and is critical for generating lift power for the plane. In a natural setting such as in rivers, water moves slower at the surface and faster at the river bed. The veloctiy gradient again creates 'stress' on the bed surface and thereby causes phenomenons like erotion or transportation of sediments.\n", + "\n", + "Mathmatically, its primary equations governing the Shear Flow simulations are:\n", + "\n", + "$$\\frac{\\partial u}{\\partial t} + \\nabla p - \\nu \\Delta u = -u\\cdot \\nabla u,$$\n", + "\n", + "$$\\frac{\\partial s}{\\partial t} - D\\Delta s = -u \\cdot \\nabla s$$\n", + "\n", + "In the first equation:\n", + "- $u$ is the velocity of the fluid \n", + "- $\\frac{\\partial u}{\\partial t}$ is the changes of velocity over time\n", + "- $\\nabla p$ is the effect of pressure pushing the fluid around\n", + "- $\\nu \\Delta u$ describes the internal friction (viscosity) that resist fluid motion (**note:** $\\nu$ stands for viscosity, $\\Delta = \\nabla \\cdot \\nabla$ it measures how 'curvy' something is, like a second-derivative that tells you whether you are at a global/local maximum, minimum, or a saddle point)\n", + "- $-u\\cdot \\nabla u$ describes the advection, meaning how a fluid that is already is motion is carrying itself along \n", + "\n", + "To sum up, this describes how changes in motion is equivalent to forces applied to the fluid due to **pressure** + **friction** - the fluid advection\n", + "\n", + "In the second equation:\n", + "- $u$ again is the velocity vector of the fluid\n", + "- $s$ is the **flow tracer**, you can think of it as any substance/fluid property being carried by the fluid and is used to track flow velocity. (i.e. temperature, salinity, or dyes and small particles)\n", + "- $\\frac{\\partial s}{\\partial t}$ describes how the tracer changes over time\n", + "- $-D\\Delta s$ describes te natural diffusion/spreading of the tracer (I.e. how fast a substance spreads in water) (**note:** D stands for diffusivity, which measures how easily tracers spread)\n", + "- $-u\\cdot \\nabla s$ describes the rate of decrease of the tracer $s$ in the direction of the flow\n", + "\n", + "This overall means that changes in tracer concentration equals how the tracer spreads out - how the tracer is moved around by the fluid\n", + "\n", + "The two PDEs are **parameterized** by the Reynolds & Schmidt through $\\nu$ and $D$ in the form of:\n", + "\n", + "- **Viscosity**:\n", + "$$\\nu = \\frac{1}{Reynolds}$$\n", + "- **Diffusivity**:\n", + "$$D = \\frac{\\nu}{Schmidt}$$" + ] + }, + { + "cell_type": "markdown", + "id": "0e75a6bd-9f04-4c8b-89bc-62988f6eddda", + "metadata": {}, + "source": [ + "# Goals of PARC for Shear Flow \n", + "\n", + "In this notebook we aim to use the PARC Neural Network to predict future **velocity fields $u$** and **tracer distributions $s$** based on current states. The neural network will be estimating the hidden physical parameters like $\\nu$ and $D$ from the given observed Shear Flow data. This is essential as the neural network could learn not just the solutions to the predicted outcome of the shear flow at a future state, but also learning how the outcome changes/varies with different parameter values of **Viscosity** and **Diffusivity**\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "f204bf63-4c07-4b1a-82bc-d5588615421c", + "metadata": {}, + "source": [ + "# Setting Up\n", + "\n", + "This document serves as a guide to training a PARC model for the Shear Flow equation. Here are the initial steps to take before you begin training your PARC model!\n", + "\n", + "Download & Prepare Data:\n", + "- Extract the downloaded data and ensure that it is placed in the following directory: `PARCtorch/PARCtorch/data`\n", + "- Make sure that the file paths at `train_dir` and `test_dir` point to the correct paths to your training and testing data, e.g. `../data/train` or `../data/test`\n", + "\n", + "Install PARCtorch:\n", + "- Ensure PARCtorch is installed in your Python Environment, you could view the installation instructions [here](https://github.com/baeklab/PARCtorch/tree/main#installation)\n", + "- After the installation, install all the relevant dependencies with `!pip install -r requirements.txt` (The versions of your dependencies need to be consistent with our requirements, or else the script might not run!)\n", + "- Optional (only if the script doesn't run): add the root directory to the system's path so that it runs" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "77a23205-5068-44d2-8b1a-19debbd9ae51", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "\n", + "# Add the root directory (PARCTorch) to the system path\n", + "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), \"..\")))" + ] + }, + { + "cell_type": "markdown", + "id": "a6bbb03f-fde8-45b1-bd59-3fda96043d6a", + "metadata": {}, + "source": [ + "# Compute Data Normalization\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4540757e-4fd9-410f-9753-33a450929276", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "from PARCtorch.data.normalization import compute_min_max" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1d5f0aa2-e7b4-4a37-8bcf-0ccb304f0c92", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Define data directories\n", + "train_dir = Path(\"/standard/sds_baek_energetic/data/physics/the_well/datasets/shear_flow/data/train\")\n", + "test_dir = Path(\"/standard/sds_baek_energetic/data/physics/the_well/datasets/shear_flow/data/test\")" + ] + }, + { + "cell_type": "markdown", + "id": "d5889e9a-cf64-4fd0-94b8-dec8129892b6", + "metadata": {}, + "source": [ + "# Load Training Data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "eaeafdf4-0c06-4db6-8698-eca8106be98f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import logging\n", + "import torch\n", + "from torch.utils.data import DataLoader\n", + "\n", + "from PARCtorch.data.dataset import (\n", + " WellDatasetInterface,\n", + " custom_collate_fn,\n", + " InitialConditionDataset,\n", + " initial_condition_collate_fn,\n", + ")\n", + "from PARCtorch.utilities.viz import (\n", + " visualize_channels,\n", + " save_gifs_with_ground_truth,\n", + ")\n", + "\n", + "logging.basicConfig(\n", + " level=logging.INFO, format=\"%(asctime)s [%(levelname)s] %(message)s\"\n", + ")\n", + "\n", + "future_steps = 1\n", + "# Path to the min_max.json file\n", + "batch_size = 2\n", + "\n", + "train_dataset = WellDatasetInterface(\n", + " well_dataset_args={\n", + " \"well_base_path\": \"/standard/sds_baek_energetic/data/physics/the_well/datasets\",\n", + " \"well_dataset_name\": \"shear_flow\",\n", + " \"well_split_name\": \"train\",\n", + " \"exclude_filters\": [\"shear_flow_Reynolds_5e5_Schmidt_1e0.hdf5\"]\n", + " },\n", + " future_steps=future_steps\n", + ")\n", + "\n", + "train_loader = DataLoader(\n", + " train_dataset,\n", + " batch_size=batch_size,\n", + " shuffle=False,\n", + " num_workers=1,\n", + " collate_fn=custom_collate_fn,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "b684e1ab-9521-4935-ae03-4de80fae2273", + "metadata": {}, + "source": [ + "# Visualizing the Data - Sanity-Check" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e763906b-0324-43d1-8ca2-7efb526508d3", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['velocity_x', 'velocity_y', 'tracer', 'pressure']\n", + "{0: ['tracer', 'pressure'], 1: ['velocity_x', 'velocity_y'], 2: []}\n" + ] + } + ], + "source": [ + "channel_names = (\n", + " train_dataset.well_dataset.field_names[1] + \n", + " train_dataset.well_dataset.field_names[0]\n", + ")\n", + "print(channel_names)\n", + "print(train_dataset.well_dataset.field_names)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "231535c5-f1c3-41eb-8b1a-8c0add2241c4", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING, this dataset has not been verified with PARCv2. Confirm orientation of x and y before proceeding.\n", + "WARNING, this dataset has not been verified with PARCv2. Confirm orientation of x and y before proceeding.\n", + "WARNING, this dataset has not been verified with PARCv2. Confirm orientation of x and y before proceeding.\n", + "WARNING, this dataset has not been verified with PARCv2. Confirm orientation of x and y before proceeding.\n", + "WARNING, this dataset has not been verified with PARCv2. Confirm orientation of x and y before proceeding.\n", + "WARNING, this dataset has not been verified with PARCv2. Confirm orientation of x and y before proceeding.\n", + "Channel Data Statistics:\n", + "Channel 0: IC min=10000.0, IC max=10000.0\n", + " Step 1: min=10000.0, max=10000.0\n", + "Channel 1: IC min=0.10000000149011612, IC max=0.10000000149011612\n", + " Step 1: min=0.10000000149011612, max=0.10000000149011612\n", + "Channel 2: IC min=-0.5, IC max=0.5\n", + " Step 1: min=-0.5, max=0.5\n", + "Channel 3: IC min=0.0, IC max=0.0\n", + " Step 1: min=-0.02280947007238865, max=0.021170886233448982\n", + "Channel 4: IC min=-0.5, IC max=0.5\n", + " Step 1: min=-0.5113672614097595, max=0.5106528401374817\n", + "Channel 5: IC min=-0.0992525964975357, IC max=0.0992525964975357\n", + " Step 1: min=-0.03139960765838623, max=0.03140629082918167\n" + ] + }, + { + "data": { + "image/png": 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8y5YuXaqkpCRddNFFEe0fALQUTtevcIwbN04pKSl+y5KTkxUXVzNEr6qq0k8//aS0tDQdddRRfqeO+/vf/67s7Gxdf/31dbZr1qQVK1YoMzNTZ555pl9t7N+/v9LS0urUxtzcXOXn50e7mQDQYsSqliQkJGjSpEm+20lJSZo0aZK2b9+uDRs2+K0bWFuKior01Vdf6ZJLLtFPP/3kqwXl5eU644wz9M4776i6ulqSlJWVpXXr1vnNIGqVmZkpSfrXv/4V1VO5XX311X63//GPf6i6uloXX3yxX/3KyclRz5496x3bAUBLEqu6U15erg4dOqhDhw7q0aOHbrrpJp100kl6+eWX/b7/stabyspK/fTTT+rRo4eysrL8xi4XX3yxvF6v36xu//rXv7Rz505ddtllIfejMd/1mWdeuPbaa/2WBxs/mQLr0SmnnKKffvrJ1w8A0FrEov7ceeedWrp0qfr166d//etfuu2229S/f38df/zxvhmmpfC+DzONGjXKbybSU045RZL8TsNtuvLKK33Xs7KydNRRRyk1NVUXX3yxb/lRRx2lrKysoI83rV+/Xtu3b9fVV1+tpKQk3/Lx48f7xlMAooegWzOVkZEhSdq9e3fE29i1a5eKi4t9l4amBB01apT27t2rV155RZJUVlam119/XRdddJGvgHz11VeSpNNPP903IDIvb775prZv3x5y+99++63i4uLUo0cPv+U5OTnKysrSt99+K0m+82wfe+yxkTU8iDPPPFOdO3f2Dbiqq6v1/PPP6ze/+U3Ui3mkgQgAiDVqj3trz+jRoxUfH+87fWlFRYVefPFFnXPOOX4DOgBojWJRvxqSm5tbZ1l1dbUeeugh9ezZU8nJycrOzlaHDh303//+V7t27fKt9/XXX+uoo45SQkJCyO1/9dVX2rVrlzp27FinNpaVldWpjcH2BwBQK1a1pEuXLkpNTfVbduSRR0pSndO2Bf4uN8dJ48aNq1MLnn76ae3bt89XXx544AF98skn6tq1q0444QTNmDHD7yBObm6uCgoK9PTTTys7O1v5+fmaP3++X32KRLB9NgxDPXv2rLPPn3/+eb1jOwBoSWJVd7xer1atWqVVq1Zp4cKFOuaYY7R9+/Y6/6Szd+9eTZs2TV27dvUbu5SUlPjVhqysLJ133nm+76ukmn/yPOSQQ3T66aeH3I9ofNcXWGMCv/uzCjw9n/ld2i+//BLyMQDQEsWq/owZM0b//ve/9csvv+jNN9/UJZdcoo8++kjnnXeeKioqJIX3fZgp3N/rXq9XHTp08FuWmZmpQw89tM7x/MzMzHrrgnksqWfPnn7LExMTdcQRRzS4zwDsafg3AVypR48eSkhI0McffxzxNm688UYtXrzYd3vIkCFau3ZtyPVPPPFEdevWTS+88IIuueQSvfrqq9q7d69GjRrlW8f8T9DnnntOOTk5dbYRTvGJRRAsPj5el1xyiZ566ik9/vjj+t///V9t2bKl3v8qCsac5Wfv3r1B79+zZ0+dmYAAoLmg9kRXtGqPJHXs2FFnnnmm/v73v2v+/Pl69dVXtXv3bl166aVNsOcA0LzEon41JPBAkSTdd999uuOOO/S73/1Od999t9q1a6e4uDhNmTLFV+vCVV1drY4dO/rNnGAV+CVesP0BANRyYy0JFPi73Kwds2fPVt++fYM+Ji0tTVLNjDunnHKKXnzxRb355puaPXu27r//fv3jH//QOeecI0maO3euxo8fr5dffllvvvmmbrjhBs2cOVPvvfde0ANBpqqqKlv77PF49MYbbyg+Pj7k/gJASxeruhMfH69hw4b5bufn5+voo4/WpEmTfP+EKtXMjrZw4UJNmTJFgwYNUmZmpjwej0aPHl1n7DJ27FitWLFC7777rnr37q1XXnlF1157rW8262Ci8V2fHcFqjiQZhhHV5wEAt4v1uCcjI0NnnnmmzjzzTCUmJmrx4sVat26dhgwZYmsfwv29Hmo96gLgfgTdmqk2bdro9NNP19tvv63vv/9eXbt2tb2Nm2++2e9gejgzvlx88cV6+OGHVVpaquXLl6tbt2468cQTffd3795dUs0Bd+uAKByHH364qqur9dVXX+mYY47xLd+2bZtKSkp0+OGH+z3HJ598Uu9/4QRqKMQwduxYzZ07V6+++qreeOMNdejQwfbpe8x9/PLLL33ToFr93//9X1RnAwIAJ1F73Fl7TJdeeqlWrlypN954Q0uXLlVGRobOO++8iLYFAC1JLOpXJAHqv/3tbxo6dKieeeYZv+UlJSXKzs723e7evbvWrVunyspKJSYmBt1W9+7d9dZbb+mkk04ixAYAURCrsdCWLVtUXl7uN6vb//3f/0mSunXrVu9jzTFMRkZGWOOkzp0769prr9W1116r7du36/jjj9e9997rC7pJUu/evdW7d2/dfvvtevfdd3XSSSdpwYIFuueee3ztKSkp8duuObNBOLp37y7DMJSbm+ubuQ4AWqNY1Z1AnTt31tSpU3XnnXfqvffe830f97e//U3jxo3T3LlzfetWVFTUqQGSdPbZZ6tDhw5asmSJ8vLytGfPHl1++eX1Pm80vuvbtGmT36w6GzdutLUdAGiN3FJ/JGnAgAFavHixtm7dKim878NiyTyW9NVXX/nNWlpZWalNmzapT58+vmWc/Q1oPE5d2oxNnz5dhmHo8ssvV1lZWZ37N2zY4JeYDtSrVy8NGzbMd+nfv3+Dzzlq1Cjt27dPixcv1sqVK/3OTy3V/IdPRkaG7rvvPlVWVtZ5/I4dO0Ju+9e//rUkad68eX7LH3zwQUnS8OHDJUlnnXWW0tPTNXPmTN90pab6ktRt2rSRVPcLN9Nxxx2n4447Tk8//bT+/ve/a/To0bb/M6h///7q2LGj7xQQVi+99JJ+/PFHvy8IAaC5ofa4r/aYRowYoTZt2ujxxx/XG2+8oQsuuIBZRAHgIKfrV2pqqu3TucXHx9epKStWrNCPP/7ot2zkyJHauXOnHnvssTrbMB9/8cUXq6qqSnfffXeddQ4cOBCyLgEAQovFWOjAgQP685//7Lu9f/9+/fnPf1aHDh0afHz//v3VvXt3zZkzJ+j+muOkqqqqOjWrY8eO6tKli++7rdLSUh04cMBvnd69eysuLs63TkZGhrKzs/XOO+/4rff444832E7TBRdcoPj4eN155511aqJhGPrpp5/C3hYANHexqDvBXH/99WrTpo1mzZrlWxZs7PLoo48GncUzISFBY8aM0QsvvKBFixapd+/eOu644+p9zsZ812f+A2lg/Xn00UfrfU4AQA0n68+ePXtUWFgY9L433nhDknTUUUdJCu/7sFgaMGCAOnTooAULFmj//v2+5YsWLarzPZz5j0x8PwdEjhndmrHBgwdr/vz5uvbaa3X00Ufr8ssvV8+ePbV7926tXbtWr7zyiu65556oPufxxx+vHj166LbbbtO+ffv8Th0n1Xyp9cQTT+jyyy/X8ccfr9GjR6tDhw767rvv9Nprr+mkk04KWoAkqU+fPho3bpyefPJJlZSUaMiQIXr//fe1ePFijRgxQkOHDvU9x0MPPaQrr7xSAwcO1CWXXKK2bdvqP//5j/bs2ROyuKakpKhXr15avny5jjzySLVr107HHnus3wxrY8eO1U033SRJEZ06LikpSXPmzNG4ceM0cOBAjRo1Su3bt9dHH32kZ599Vscdd5wmTpxoe7sA4BbUHvfVHlNaWppGjBihpUuXShKnLQUAC6frV//+/bV8+XIVFBRo4MCBSktLa3CWzXPPPVd33XWXJkyYoMGDB+vjjz/WkiVLdMQRR/itN3bsWP3lL39RQUGB3n//fZ1yyikqLy/XW2+9pWuvvVa/+c1vNGTIEE2aNEkzZ85UUVGRzjrrLCUmJuqrr77SihUr9PDDD+vCCy+MWnsBoDWIxVioS5cuuv/++7V582YdeeSRWr58uYqKivTkk082OItBXFycnn76aZ1zzjn61a9+pQkTJuiQQw7Rjz/+qDVr1igjI0Ovvvqqdu/erUMPPVQXXnih+vTpo7S0NL311lv64IMPfDP1vP3225o8ebIuuugiHXnkkTpw4ICee+45xcfHa+TIkb7nvPLKKzVr1ixdeeWVGjBggN555x3fDHTh6N69u+655x7deuut2rx5s0aMGKH09HRt2rRJL774oiZOnOgbOwFASxeLuhNM+/btNWHCBD3++OP6/PPPdcwxx+jcc8/Vc889p8zMTPXq1UuFhYV666231L59+6DbGDt2rB555BGtWbNG999/f4PP2Zjv+vr376+RI0dq3rx5+umnn3TiiSfqf/7nf3z1iFl0AKB+TtafPXv2aPDgwTrxxBN19tlnq2vXriopKdFLL72kf//73xoxYoT69esnKbzvw2IpMTFR99xzjyZNmqTTTz9do0aN0qZNm7Rw4cI63+11795dWVlZWrBggdLT05Wamqq8vDzl5ubGaO+BZshAs7Bw4UJDkvHBBx/UuW/Dhg3GJZdcYnTp0sVITEw02rZta5xxxhnG4sWLjaqqqqjvy2233WZIMnr06BFynTVr1hj5+flGZmam4fV6je7duxvjx4831q9f71tn+vTpRuBbsLKy0rjzzjuN3NxcIzEx0ejatatx6623GhUVFXWe45VXXjEGDx5spKSkGBkZGcYJJ5xgPP/88777x40bZxx++OF+j3n33XeN/v37G0lJSYYkY/r06X73b9261YiPjzeOPPJIG69IXW+88YYxdOhQIyMjw0hMTDRyc3ONgoIC45dffmnUdgHASdSe5lV7DMMwXnvtNUOS0blz5ybpBwBoDtxQv8rKyoxLLrnEyMrKMiT5asOaNWsMScaKFSvqPKaiosL4/e9/b3Tu3NlISUkxTjrpJKOwsNAYMmSIMWTIEL919+zZY9x2222+2pWTk2NceOGFxtdff+233pNPPmn079/fSElJMdLT043evXsbN998s7FlyxbfOocffrgxfPjwqLU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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for batch_idx, batch in enumerate(train_loader):\n", + " ic, t0, t1, target = batch\n", + " channel_names = ['velocity_U', \n", + " 'velocity_V', \n", + " 'tracer', \n", + " 'pressure',\n", + " 'Rayleigh',\n", + " 'Schmidt',\n", + " ]\n", + " custom_cmaps = [\"seismic\"] * 6\n", + "\n", + " visualize_channels(\n", + " ic = ic, \n", + " t0 = t0,\n", + " t1 = t1,\n", + " target = target, \n", + " channel_names = channel_names,\n", + " channel_cmaps = custom_cmaps,\n", + " )\n", + " break" + ] + }, + { + "cell_type": "markdown", + "id": "5989fbbe-7ca7-4820-8916-b3c21844f74a", + "metadata": {}, + "source": [ + "# Building the PARC Model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2c8c24a3-6412-46a4-b58b-872de72332bf", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from PARCtorch.PARCv2 import PARCv2\n", + "from PARCtorch.differentiator.differentiator import ADRDifferentiator\n", + "from PARCtorch.differentiator.finitedifference import FiniteDifference\n", + "from PARCtorch.integrator.integrator import Integrator\n", + "from PARCtorch.integrator.heun import Heun\n", + "from PARCtorch.utilities.unet import UNet\n", + "\n", + "from torch.optim import Adam\n", + "\n", + "n_fe_features = 16\n", + "unet_ShearFlow = UNet(\n", + " block_dimensions=[64, 64 * 2, 64 * 4], \n", + " input_channels=6, \n", + " output_channels=n_fe_features, \n", + " kernel_size=3, \n", + " padding_mode=\"zeros\", \n", + " up_block_use_concat=[False, True], \n", + " skip_connection_indices=[0], \n", + ")\n", + "right_diff = FiniteDifference(padding_mode=\"replicate\").cuda()\n", + "heun_int = Heun().cuda()\n", + "\n", + "diff_ShearFlow = ADRDifferentiator(\n", + " n_state_var=4, \n", + " n_fe_features=n_fe_features, \n", + " list_adv_idx=[0,1, 2, 3, 4, 5], \n", + " list_dif_idx=[0, 1, 2, 3, 4, 5], \n", + " feature_extraction=unet_ShearFlow, \n", + " padding_mode=\"constant\", \n", + " finite_difference_method=right_diff, \n", + " spade_random_noise=False, \n", + ").cuda()\n", + "\n", + "\n", + "ShearFlow_int = Integrator(\n", + " clip=True, \n", + " list_poi_idx=[], \n", + " num_int=heun_int, \n", + " list_dd_int=[None, None, None, None], \n", + " padding_mode=\"constant\", \n", + " finite_difference_method=right_diff, \n", + " poi_kernel_size=3, \n", + " n_poi_features=64 \n", + ")\n", + "criterion = torch.nn.L1Loss().cuda()\n", + "\n", + "\n", + "model = PARCv2(\n", + " differentiator=diff_ShearFlow, \n", + " integrator=ShearFlow_int, \n", + " loss=criterion \n", + ").cuda()\n", + "optimizer = Adam(\n", + " params=model.parameters(), \n", + " lr=1e-5 \n", + ")\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "251a8cbf-c4bf-4c35-b642-8f7658e0eab8", + "metadata": {}, + "source": [ + "# Training the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68a06114-08db-48ba-b371-605985c2516e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 0%| | 0/70048 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pickle\n", + "import matplotlib.pyplot as plt\n", + " \n", + "with open( \"/home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_model_weights/training_losses.pkl\", 'rb' ) as f: \n", + " history = pickle.load(f)\n", + "plt.figure(figsize=(10,6))\n", + "plt.title('ShearFlow Model Loss Curve')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Loss')\n", + "plt.plot( history)\n", + "plt.savefig('normalization_loss_curve.png')\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ed19e20-efec-4e68-9f4f-cbaf5b942741", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (parc)", + "language": "python", + "name": "parc" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/PARCtorch/demos/ShearFlow_debug_3.ipynb b/PARCtorch/demos/ShearFlow_debug_3.ipynb new file mode 100755 index 0000000..ece5dfc --- /dev/null +++ b/PARCtorch/demos/ShearFlow_debug_3.ipynb @@ -0,0 +1,2654 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a323a953-d55f-4a06-87fa-21a5890fa35b", + "metadata": {}, + "source": [ + "
\n", + "

\n", + " Physics Aware Recurrent Convolutional Neural Network (PARC): ShearFlow Equation Demo\n", + "

\n", + "
\n", + " \"Image\n", + " \"Image\n", + " \"Image\n", + "
\n", + "
\n", + "\n", + "

\n", + "A customizable framework to embed physics in Deep Learning.\n", + "PARC's formulation is inspired by Advection-Diffusion-Reaction processes and uses an Inductive Bias approach to constrain the Neural Network.\n", + "

\n" + ] + }, + { + "cell_type": "markdown", + "id": "93a45e9e-6a11-4de3-a329-be4ed5ef50eb", + "metadata": {}, + "source": [ + "# Modeling Shear Flow with PARC\n", + "\n", + "This notebook aims to simulate the 2D periodic shear flow dynamics, a simplified version of the Navier-Stokes equation in a condition that it is incompressible (without consideration for pressure). A shear flow is a type of fluid that contains layers sliding past each other at varying velocities.\n", + "\n", + "The notebook will guide you through from start to finish in preparing, training, and modeling the physics-based equation's prediction results. The notebook primarily covers three sections:\n", + "- loading and preparing the data for the Shear Flow's Equation\n", + "- Using the PARC Model to learn and predict the time evolution of velocity fields $u$ and tracer distributions $s$\n", + "- Evaluating the model's performance and compare predicted results to ground truth" + ] + }, + { + "cell_type": "markdown", + "id": "f9143c39-6bbd-473e-8d5a-ba97446cdc68", + "metadata": {}, + "source": [ + "# What exactly is a Shear Flow?\n", + "\n", + "Shear Flows can be observed in many real life scenarios. For example, the air closest to an airplane's wings moves slower due to friction, while the air further way moves faster. This velocity difference in turn creates a shear flow and is critical for generating lift power for the plane. In a natural setting such as in rivers, water moves slower at the surface and faster at the river bed. The veloctiy gradient again creates 'stress' on the bed surface and thereby causes phenomenons like erotion or transportation of sediments.\n", + "\n", + "Mathmatically, its primary equations governing the Shear Flow simulations are:\n", + "\n", + "$$\\frac{\\partial u}{\\partial t} + \\nabla p - \\nu \\Delta u = -u\\cdot \\nabla u,$$\n", + "\n", + "$$\\frac{\\partial s}{\\partial t} - D\\Delta s = -u \\cdot \\nabla s$$\n", + "\n", + "In the first equation:\n", + "- $u$ is the velocity of the fluid \n", + "- $\\frac{\\partial u}{\\partial t}$ is the changes of velocity over time\n", + "- $\\nabla p$ is the effect of pressure pushing the fluid around\n", + "- $\\nu \\Delta u$ describes the internal friction (viscosity) that resist fluid motion (**note:** $\\nu$ stands for viscosity, $\\Delta = \\nabla \\cdot \\nabla$ it measures how 'curvy' something is, like a second-derivative that tells you whether you are at a global/local maximum, minimum, or a saddle point)\n", + "- $-u\\cdot \\nabla u$ describes the advection, meaning how a fluid that is already is motion is carrying itself along \n", + "\n", + "To sum up, this describes how changes in motion is equivalent to forces applied to the fluid due to **pressure** + **friction** - the fluid advection\n", + "\n", + "In the second equation:\n", + "- $u$ again is the velocity vector of the fluid\n", + "- $s$ is the **flow tracer**, you can think of it as any substance/fluid property being carried by the fluid and is used to track flow velocity. (i.e. temperature, salinity, or dyes and small particles)\n", + "- $\\frac{\\partial s}{\\partial t}$ describes how the tracer changes over time\n", + "- $-D\\Delta s$ describes te natural diffusion/spreading of the tracer (I.e. how fast a substance spreads in water) (**note:** D stands for diffusivity, which measures how easily tracers spread)\n", + "- $-u\\cdot \\nabla s$ describes the rate of decrease of the tracer $s$ in the direction of the flow\n", + "\n", + "This overall means that changes in tracer concentration equals how the tracer spreads out - how the tracer is moved around by the fluid\n", + "\n", + "The two PDEs are **parameterized** by the Reynolds & Schmidt through $\\nu$ and $D$ in the form of:\n", + "\n", + "- **Viscosity**:\n", + "$$\\nu = \\frac{1}{Reynolds}$$\n", + "- **Diffusivity**:\n", + "$$D = \\frac{\\nu}{Schmidt}$$" + ] + }, + { + "cell_type": "markdown", + "id": "0e75a6bd-9f04-4c8b-89bc-62988f6eddda", + "metadata": {}, + "source": [ + "# Goals of PARC for Shear Flow \n", + "\n", + "In this notebook we aim to use the PARC Neural Network to predict future **velocity fields $u$** and **tracer distributions $s$** based on current states. The neural network will be estimating the hidden physical parameters like $\\nu$ and $D$ from the given observed Shear Flow data. This is essential as the neural network could learn not just the solutions to the predicted outcome of the shear flow at a future state, but also learning how the outcome changes/varies with different parameter values of **Viscosity** and **Diffusivity**\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "f204bf63-4c07-4b1a-82bc-d5588615421c", + "metadata": {}, + "source": [ + "# Setting Up\n", + "\n", + "This document serves as a guide to training a PARC model for the Shear Flow equation. Here are the initial steps to take before you begin training your PARC model!\n", + "\n", + "Download & Prepare Data:\n", + "- Extract the downloaded data and ensure that it is placed in the following directory: `PARCtorch/PARCtorch/data`\n", + "- Make sure that the file paths at `train_dir` and `test_dir` point to the correct paths to your training and testing data, e.g. `../data/train` or `../data/test`\n", + "\n", + "Install PARCtorch:\n", + "- Ensure PARCtorch is installed in your Python Environment, you could view the installation instructions [here](https://github.com/baeklab/PARCtorch/tree/main#installation)\n", + "- After the installation, install all the relevant dependencies with `!pip install -r requirements.txt` (The versions of your dependencies need to be consistent with our requirements, or else the script might not run!)\n", + "- Optional (only if the script doesn't run): add the root directory to the system's path so that it runs" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "77a23205-5068-44d2-8b1a-19debbd9ae51", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "\n", + "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), \"..\")))\n" + ] + }, + { + "cell_type": "markdown", + "id": "a6bbb03f-fde8-45b1-bd59-3fda96043d6a", + "metadata": {}, + "source": [ + "# Compute Data Normalization\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "53db1c08-7b0c-4ac7-8a20-ce1f2e672fc1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os, re, torch\n", + "from torch.utils.data import DataLoader\n", + "from PARCtorch.data.dataset import custom_collate_fn\n", + "\n", + "# ensure GT shape is [B,T,C,H,W] \n", + "def _to_BTCHW(gt, ic_BCHW):\n", + " if gt.dim() != 5:\n", + " raise ValueError(f\"gt must be 5D, got {gt.shape}\")\n", + " B = ic_BCHW.shape[0]\n", + " if gt.shape[0] == B: # [B,T,C,H,W]\n", + " return gt\n", + " elif gt.shape[1] == B: # [T,B,C,H,W] -> [B,T,C,H,W]\n", + " return gt.permute(1,0,2,3,4).contiguous()\n", + " else:\n", + " raise ValueError(f\"Can't infer B. ic B={B}, gt shape={gt.shape}\")\n", + "\n", + "def _sample_values(x, k):\n", + " x = x.reshape(-1)\n", + " n = x.numel()\n", + " if n <= k:\n", + " return x.detach().cpu()\n", + " idx = torch.randint(0, n, (k,), device=x.device)\n", + " return x[idx].detach().cpu()\n", + "\n", + "# parse constants from filenames\n", + "_NUM = r\"[0-9]*\\.?[0-9]+(?:e[+-]?[0-9]+)?\"\n", + "_re_re = re.compile(rf\"Reynolds_({_NUM})\")\n", + "_re_sc = re.compile(rf\"Schmidt_({_NUM})\")\n", + "\n", + "def _to_float(s: str) -> float:\n", + " return float(s)\n", + "\n", + "def constants_from_filenames(folders, exts=(\".hdf5\", \".h5\")):\n", + " \"\"\"Return sorted unique lists (Re_vals, Sc_vals) parsed from filenames in given folder(s).\"\"\"\n", + " if isinstance(folders, (str, os.PathLike)):\n", + " folders = [folders]\n", + " re_vals, sc_vals = set(), set()\n", + " for folder in folders:\n", + " if not os.path.isdir(folder):\n", + " continue\n", + " for name in os.listdir(folder):\n", + " if not name.endswith(exts):\n", + " continue\n", + " m_re = _re_re.search(name)\n", + " m_sc = _re_sc.search(name)\n", + " if m_re: re_vals.add(_to_float(m_re.group(1)))\n", + " if m_sc: sc_vals.add(_to_float(m_sc.group(1)))\n", + " return sorted(re_vals), sorted(sc_vals)\n", + "\n", + "@torch.no_grad()\n", + "def scan_minmax(\n", + " dataset,\n", + " *,\n", + " batches=20,\n", + " bs=70,\n", + " center_pressure=True,\n", + " pct=99.5,\n", + " const_start=4,\n", + " sample_per_map=2048,\n", + " max_total_samples=1_000_000,\n", + " parse_constants_from_folders=None, # <— pass your train/val folder(s) here\n", + "):\n", + " \"\"\"\n", + " Returns:\n", + " min_val_tensor, max_val_tensor (per-channel):\n", + " [tracer_min, pressure_min, -1, -1, const1_min, const2_min, ...]\n", + " \"\"\"\n", + " dl = DataLoader(\n", + " dataset, batch_size=bs, shuffle=True,\n", + " num_workers=0, pin_memory=False, collate_fn=custom_collate_fn\n", + " )\n", + "\n", + " tr_samples, pr_samples = [], []\n", + " const_vals = None \n", + "\n", + " seen = 0\n", + " for ic, t0, t1, gt in dl:\n", + " gt = _to_BTCHW(gt, ic) # [B,T,C,H,W]\n", + " B, T, C, H, W = gt.shape\n", + " ic2, gt2 = ic.clone(), gt.clone()\n", + "\n", + " if center_pressure and C >= 2:\n", + " ic2[:, 1] -= ic2[:, 1].mean(dim=(-2,-1), keepdim=True)\n", + " gt2[:, :, 1] -= gt2[:, :, 1].mean(dim=(-2,-1), keepdim=True)\n", + "\n", + " # sample tracer & pressure (IC + GT)\n", + " tr_samples.append(_sample_values(ic2[:, 0], sample_per_map))\n", + " pr_samples.append(_sample_values(ic2[:, 1], sample_per_map))\n", + " tr_samples.append(_sample_values(gt2[:, :, 0].reshape(B*T, H, W), sample_per_map))\n", + " pr_samples.append(_sample_values(gt2[:, :, 1].reshape(B*T, H, W), sample_per_map))\n", + "\n", + " # fallback constants from tensors if no parsing specified\n", + " if parse_constants_from_folders is None and C > const_start:\n", + " n_const = C - const_start\n", + " if const_vals is None:\n", + " const_vals = [[] for _ in range(n_const)]\n", + " for j in range(n_const):\n", + " const_vals[j].append(ic2[:, const_start + j, 0, 0].detach().cpu())\n", + "\n", + " seen += 1\n", + " if batches is not None and seen >= batches:\n", + " break\n", + "\n", + " # cap lists\n", + " def _cap_list(L):\n", + " total = sum(t.numel() for t in L)\n", + " if total > max_total_samples:\n", + " cat = torch.cat(L)\n", + " new_n = max_total_samples // 2\n", + " idx = torch.randint(0, cat.numel(), (new_n,))\n", + " L.clear(); L.append(cat[idx])\n", + " _cap_list(tr_samples); _cap_list(pr_samples)\n", + "\n", + " # percentiles\n", + " def _percentile(sample_list, q):\n", + " if not sample_list: return 0.0\n", + " x = torch.cat(sample_list).float()\n", + " return float(torch.quantile(x, torch.tensor(q, dtype=torch.float32)))\n", + "\n", + " low_q = (100.0 - pct) / 100.0\n", + " hi_q = pct / 100.0\n", + " tr_min = _percentile(tr_samples, low_q)\n", + " tr_max = _percentile(tr_samples, hi_q)\n", + " pr_min = _percentile(pr_samples, low_q)\n", + " pr_max = _percentile(pr_samples, hi_q)\n", + "\n", + " # velocities are unit-normalized in your dataset\n", + " vx_min, vx_max = -1.0, 1.0\n", + " vy_min, vy_max = -1.0, 1.0\n", + "\n", + " # search min max of reynolds and constants via file names\n", + " const_mins, const_maxs = [], []\n", + " if parse_constants_from_folders is not None:\n", + " re_list, sc_list = constants_from_filenames(parse_constants_from_folders)\n", + " if re_list:\n", + " eps = 1e-8\n", + " const_mins.append(min(re_list) - eps)\n", + " const_maxs.append(max(re_list) + eps)\n", + " if sc_list:\n", + " eps = 1e-8\n", + " const_mins.append(min(sc_list) - eps)\n", + " const_maxs.append(max(sc_list) + eps)\n", + " print(f\"[files] Reynolds from names: {re_list or '—'}\")\n", + " print(f\"[files] Schmidt from names: {sc_list or '—'}\")\n", + "\n", + " min_list = [tr_min, pr_min, vx_min, vy_min] + const_mins\n", + " max_list = [tr_max, pr_max, vx_max, vy_max] + const_maxs\n", + " min_val_tensor = torch.tensor(min_list, dtype=torch.float32)\n", + " max_val_tensor = torch.tensor(max_list, dtype=torch.float32)\n", + "\n", + " print(f\"[stream] tracer pct[{100-pct:.1f}%,{pct:.1f}%]: {tr_min:.4f}, {tr_max:.4f}\")\n", + " print(f\"[stream] pressure pct[{100-pct:.1f}%,{pct:.1f}%]: {pr_min:.4f}, {pr_max:.4f}\" +\n", + " (\" (centered)\" if center_pressure else \"\"))\n", + " print(f\"[stream] velocity: UNIT NORMALIZED → [-1.0, 1.0]\")\n", + " if const_mins:\n", + " print(f\"[const] mins: {const_mins}\")\n", + " print(f\"[const] maxs: {const_maxs}\")\n", + "\n", + " return min_val_tensor, max_val_tensor\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "945dc581-fd36-4903-ba01-2f166cb2caf5", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from torch.utils.data import DataLoader\n", + "import torch\n", + "import os\n", + "import h5py\n", + "from PARCtorch.data.welldataset_single_trajectory import WellDatasetInterface\n", + "from the_well.data.datasets import WellDataset\n", + "from torch.utils.data import DataLoader\n", + "from torchvision.transforms import Compose, Resize\n", + "import PARCtorch.data.dataset as ds_mod\n", + "import torch\n", + "import torch.nn as nn\n", + "train_dir = \"/home/wkt7ne/PARCtorch/PARCtorch/shear_flow/data/train\"\n", + "# Now import the utilities\n", + "from PARCtorch.data.dataset import (\n", + " custom_collate_fn,\n", + " InitialConditionDataset,\n", + " initial_condition_collate_fn,\n", + ")\n", + "from PARCtorch.utilities.viz import (\n", + " visualize_channels,\n", + " save_gifs_with_ground_truth,\n", + ")\n", + "\n", + "ds_mod.WellDatasetInterface.__del__ = lambda self: None\n", + "\n", + "# spatial transforms\n", + "orig_h, orig_w = 256, 512\n", + "spatial_percent = 1/3\n", + "new_h = int(orig_h * spatial_percent) # 85\n", + "new_w = int(orig_w * spatial_percent) # 170\n", + "\n", + "def divisible_by_8(x):\n", + " return (x//8)*8\n", + "new_h = divisible_by_8(new_h)\n", + "new_w = divisible_by_8(new_w)\n", + "\n", + "\n", + "patch_size = (new_h, new_w)\n", + "\n", + "# spatial transformation\n", + "train_spatial_transform = Compose([\n", + " Resize((new_h, new_w)),\n", + "])\n", + "gt_spatial_transform = Compose([\n", + " Resize((new_h, new_w)),\n", + "])\n", + "\n", + "# temporal skipping\n", + "temporal_skip = 50\n", + "desired_frames = 3\n", + "future_steps = 1\n", + "\n", + "batch_size = 60\n", + "loader = WellDatasetInterface(\n", + " well_dataset_args={\"path\": train_dir, \"exclude_filters\": [\"shear_flow_Reynolds_5e5_Schmidt_1e0.hdf5\"]},\n", + " future_steps=future_steps,\n", + " add_constant_scalars=True,\n", + " delta_t=2,\n", + " temporal_skip=temporal_skip,\n", + " spatial_transform=train_spatial_transform,\n", + " min_val=None, max_val=None,\n", + " single_trajectory=False,\n", + ")\n", + "\n", + "# calculate min max tensors\n", + "min_val_tensor, max_val_tensor = scan_minmax(\n", + " loader,\n", + " batches=60, \n", + " bs=64,\n", + " center_pressure=True, \n", + " pct=99.5,\n", + " const_start=4,\n", + " sample_per_map=4096,\n", + " max_total_samples=2_000_000,\n", + " parse_constants_from_folders=[train_dir],\n", + ")\n", + "\n", + "\n", + "print(\"min_val_tensor:\", min_val_tensor.tolist())\n", + "print(\"max_val_tensor:\", max_val_tensor.tolist())\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9bbfa746-bcab-4afe-9c1d-6f69b0bff76d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "min: [-0.5, -0.4064001441001892, -1.0, -1.0, 10000.0, 0.19999998807907104]\n", + "max: [0.5, 0.14997883141040802, 1.0, 1.0, 500000.0, 5.0]\n" + ] + } + ], + "source": [ + "import torch\n", + "from pathlib import Path\n", + "\n", + "def load_minmax(cache_path, expected_len=None, dtype=torch.float32):\n", + " \"\"\"\n", + " Load min/max for use INSIDE a Dataset/DataLoader worker.\n", + " Always returns CPU tensors to avoid CUDA init in workers.\n", + " \"\"\"\n", + " ckpt = torch.load(Path(cache_path), map_location=\"cpu\")\n", + "\n", + " # Extract\n", + " if isinstance(ckpt, dict):\n", + " min_t = ckpt.get(\"min\", ckpt.get(\"min_val_tensor\"))\n", + " max_t = ckpt.get(\"max\", ckpt.get(\"max_val_tensor\"))\n", + " elif isinstance(ckpt, (list, tuple)) and len(ckpt) >= 2:\n", + " min_t, max_t = ckpt[0], ckpt[1]\n", + " else:\n", + " raise ValueError(f\"Unsupported cache format in {cache_path}\")\n", + "\n", + " if min_t is None or max_t is None:\n", + " keys = list(ckpt.keys()) if isinstance(ckpt, dict) else []\n", + " raise ValueError(f\"Could not find min/max in {cache_path}. Keys: {keys}\")\n", + "\n", + " # Ensure CPU tensors\n", + " min_t = torch.as_tensor(min_t, dtype=dtype).contiguous().cpu()\n", + " max_t = torch.as_tensor(max_t, dtype=dtype).contiguous().cpu()\n", + "\n", + " if expected_len is not None and (min_t.numel() != expected_len or max_t.numel() != expected_len):\n", + " print(f\"Warning: expected {expected_len} channels, got {min_t.numel()}/{max_t.numel()}\")\n", + "\n", + " return min_t, max_t\n", + "\n", + "\n", + "# Usage\n", + "cache_file = \"/home/wkt7ne/PARCtorch/PARCtorch/shear_flow/data/minmax_cache.pt\"\n", + "min_val_tensor, max_val_tensor = load_minmax(cache_file, expected_len=6)\n", + "print(\"min:\", min_val_tensor.tolist())\n", + "print(\"max:\", max_val_tensor.tolist())\n" + ] + }, + { + "cell_type": "markdown", + "id": "d5889e9a-cf64-4fd0-94b8-dec8129892b6", + "metadata": {}, + "source": [ + "# Load Training Data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "eaeafdf4-0c06-4db6-8698-eca8106be98f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "import h5py\n", + "from PARCtorch.data.welldataset_single_trajectory import WellDatasetInterface\n", + "from the_well.data.datasets import WellDataset\n", + "from torch.utils.data import DataLoader\n", + "from torchvision.transforms import Compose, Resize\n", + "import PARCtorch.data.dataset as ds_mod\n", + "import torch\n", + "import torch.nn as nn\n", + "train_dir = \"/home/wkt7ne/PARCtorch/PARCtorch/shear_flow/data/train\"\n", + "# Now import the utilities\n", + "from PARCtorch.data.dataset import (\n", + " custom_collate_fn,\n", + " InitialConditionDataset,\n", + " initial_condition_collate_fn,\n", + ")\n", + "from PARCtorch.utilities.viz import (\n", + " visualize_channels,\n", + " save_gifs_with_ground_truth,\n", + ")\n", + "\n", + "ds_mod.WellDatasetInterface.__del__ = lambda self: None\n", + "\n", + "# spatial transforms\n", + "orig_h, orig_w = 256, 512\n", + "spatial_percent = 1/3\n", + "new_h = int(orig_h * spatial_percent) # 85\n", + "new_w = int(orig_w * spatial_percent) # 170\n", + "\n", + "def divisible_by_8(x):\n", + " return (x//8)*8\n", + "new_h = divisible_by_8(new_h)\n", + "new_w = divisible_by_8(new_w)\n", + "\n", + "\n", + "patch_size = (new_h, new_w)\n", + "\n", + "# spatial transformation\n", + "train_spatial_transform = Compose([\n", + " Resize((new_h, new_w)),\n", + "])\n", + "gt_spatial_transform = Compose([\n", + " Resize((new_h, new_w)),\n", + "])\n", + "\n", + "# temporal skipping\n", + "temporal_skip = 50\n", + "desired_frames = 3\n", + "future_steps = 1\n", + "\n", + "batch_size = 10 # increase it to see at which point it will reduce the training time \n", + "\n", + "\n", + "# Remove corrupted files\n", + "corrupted = []\n", + "if os.path.isdir(train_dir):\n", + " for fname in os.listdir(train_dir):\n", + " if not fname.endswith(\".hdf5\"):\n", + " continue\n", + " fpath = os.path.join(train_dir, fname)\n", + " try:\n", + " with h5py.File(fpath, \"r\"):\n", + " pass\n", + " except OSError:\n", + " corrupted.append(fname)\n", + " os.remove(fpath)\n", + "\n", + "\n", + "# Initialize dataset\n", + "ds = WellDatasetInterface(\n", + " well_dataset_args={\n", + " \"path\": train_dir,\n", + " \"exclude_filters\": [\"shear_flow_Reynolds_5e5_Schmidt_1e0.hdf5\"]\n", + " },\n", + " future_steps=future_steps,\n", + " add_constant_scalars = True,\n", + " delta_t = 2,\n", + " temporal_skip = temporal_skip,\n", + " spatial_transform = train_spatial_transform,\n", + " min_val= min_val_tensor,\n", + " max_val= max_val_tensor,\n", + " single_trajectory=True,\n", + " traj_idx=0,\n", + ")\n", + "\n", + "ic, t0, t1, gt = ds[0]\n", + "\n", + "\n", + "# Create DataLoader for training dataset\n", + "train_loader = DataLoader(\n", + " ds,\n", + " batch_size=batch_size,\n", + " shuffle=True,\n", + " num_workers=1,\n", + " collate_fn=custom_collate_fn,\n", + " pin_memory=False,\n", + ")\n", + "\n", + "val_dir = \"/home/wkt7ne/PARCtorch/PARCtorch/shear_flow/data/experimental_validation\"\n", + "val_dataset = WellDatasetInterface(\n", + " well_dataset_args={\"path\": val_dir},\n", + " future_steps=future_steps,\n", + " delta_t=2.0,\n", + " add_constant_scalars=True,\n", + " temporal_skip=temporal_skip,\n", + " spatial_transform=gt_spatial_transform,\n", + " min_val=min_val_tensor,\n", + " max_val=max_val_tensor,\n", + " single_trajectory=True,\n", + " traj_idx=0,\n", + ")\n", + "val_loader = DataLoader(\n", + " val_dataset,\n", + " batch_size=batch_size,\n", + " shuffle=False,\n", + " num_workers=1,\n", + " pin_memory=True,\n", + " collate_fn=custom_collate_fn,\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "14636f18-fc02-4c07-a76e-1b655eaf4965", + "metadata": {}, + "source": [ + "# Sanity check on normalization" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "87775147-d315-4945-a35a-895cb66c5ada", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "xb shape: torch.Size([10, 6, 80, 168]) yb shape: torch.Size([1, 10, 6, 80, 168])\n", + "IC tracer/pressure ~[0,1]: [-1.0000, 1.0000]\n", + "IC vx ~[-1,1]: [0.0000, 0.1837]\n", + "IC vy ~[-1,1]: [0.0000, 1.0000]\n", + "GT tracer/pressure ~[0,1] over T=1: [-1.0000, 1.0000]\n", + "GT vx ~[-1,1]: [0.0000, 0.1837]\n", + "GT vy ~[-1,1]: [0.0000, 1.0000]\n", + "IC constants (e.g., Re/Sc/constant_fields) ~[0,1]: [-0.3994, 1.0000]\n" + ] + } + ], + "source": [ + "# Grab one batch from your DataLoader\n", + "xb, t0, t1, yb = next(iter(train_loader)) # xb: [B, C, H, W], yb: [T, B, C, H, W]\n", + "\n", + "B, C, H, W = xb.shape\n", + "print(\"xb shape:\", xb.shape, \"yb shape:\", yb.shape)\n", + "\n", + "if C < 4:\n", + " raise RuntimeError(f\"Expected at least 4 field channels (tracer, pressure, vx, vy); got C={C}\")\n", + "\n", + "# In your pipeline, the last 4 channels are the field group: [tracer, pressure, vx, vy]\n", + "tr_pr_slice = slice(C - 4, C - 2) # tracer, pressure\n", + "vx_idx = C - 2\n", + "vy_idx = C - 1\n", + "\n", + "def minmax(t):\n", + " t = t.detach()\n", + " return float(t.min().cpu()), float(t.max().cpu())\n", + "\n", + "# --- Inputs (ic) ---\n", + "tr_pr_min, tr_pr_max = minmax(xb[:, tr_pr_slice])\n", + "vx_min, vx_max = minmax(xb[:, vx_idx])\n", + "vy_min, vy_max = minmax(xb[:, vy_idx])\n", + "print(f\"IC tracer/pressure ~[0,1]: [{tr_pr_min:.4f}, {tr_pr_max:.4f}]\")\n", + "print(f\"IC vx ~[-1,1]: [{vx_min:.4f}, {vx_max:.4f}]\")\n", + "print(f\"IC vy ~[-1,1]: [{vy_min:.4f}, {vy_max:.4f}]\")\n", + "\n", + "# --- Targets (gt) ---\n", + "T = yb.shape[0]\n", + "tr_pr_min_t, tr_pr_max_t = minmax(yb[:, :, tr_pr_slice])\n", + "vx_min_t, vx_max_t = minmax(yb[:, :, vx_idx])\n", + "vy_min_t, vy_max_t = minmax(yb[:, :, vy_idx])\n", + "print(f\"GT tracer/pressure ~[0,1] over T={T}: [{tr_pr_min_t:.4f}, {tr_pr_max_t:.4f}]\")\n", + "print(f\"GT vx ~[-1,1]: [{vx_min_t:.4f}, {vx_max_t:.4f}]\")\n", + "print(f\"GT vy ~[-1,1]: [{vy_min_t:.4f}, {vy_max_t:.4f}]\")\n", + "\n", + "# --- Optional: constants (everything before last 4 channels) ---\n", + "if C > 4:\n", + " const_min, const_max = minmax(xb[:, :C-4])\n", + " print(f\"IC constants (e.g., Re/Sc/constant_fields) ~[0,1]: [{const_min:.4f}, {const_max:.4f}]\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "eb47eb1f-de1a-4a69-9d06-3f66596dce4c", + "metadata": {}, + "source": [ + "# Visualizing the Data - Sanity Check" + ] + }, + { + "cell_type": "markdown", + "id": "6f533d25-b23e-45c4-a999-6752017e77b1", + "metadata": { + "tags": [] + }, + "source": [ + "This step is a sanity-check on one of the batches to ensure that our previous two steps - **normalization** and **data loading** were carried out correctly. \n", + "\n", + "To do so, we will be plotting out the:\n", + "- **Initial Condition** `ic`: The initial physical state of the system at time $t_0$ - the input to the model\n", + "- **Time Stamp** `t0`: The time stamp corresponding to the initial condition\n", + "- **Target Timestamp** `t1`: The target time stamp that we are trying to predict with PARC\n", + "- **Ground Truth** `target`: The ground truth data at time $t_1$, representing what the model is suppoesd to output at the predicted outcome at the future time stamp\n", + "- `channel_names`: labels for each channel, i.e. `Reynolds,Schmidt, tracer, pressure, velocity_U, velocity_V `\n", + "- `channel_cmaps`: list of colormaps for each channel" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "231535c5-f1c3-41eb-8b1a-8c0add2241c4", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Channel Data Statistics:\n", + "Channel 0: IC min=0.15200187265872955, IC max=0.9207032918930054\n", + " Step 1: min=0.1520480513572693, max=0.9204034209251404\n", + "Channel 1: IC min=0.6897211670875549, IC max=0.7649035453796387\n", + " Step 1: min=0.691247284412384, max=0.7637747526168823\n", + "Channel 2: IC min=-1.0, IC max=1.0\n", + " Step 1: min=-1.0, max=1.0\n", + "Channel 3: IC min=-0.9999988675117493, IC max=0.9999984502792358\n", + " Step 1: min=-0.9999969005584717, max=0.9999999403953552\n", + "Channel 4: IC min=0.0, IC max=0.0\n", + " Step 1: min=0.0, max=0.0\n", + "Channel 5: IC min=0.062499985098838806, IC max=0.0625000149011612\n", + " Step 1: min=0.062499985098838806, max=0.0625000149011612\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Fetch a batch and visualize\n", + "for batch in train_loader:\n", + " ic, t0, t1, target = batch\n", + " \n", + " num_channels = ic.shape[1]\n", + " \n", + " channel_names = [ \n", + " \"Tracer\",\n", + " \"Pressure\",\n", + " \"Velocity X\",\n", + " \"Velocity Y\",\n", + " \"Reynolds\",\n", + " \"Schmidt\",\n", + " ]\n", + " assert len(channel_names) == num_channels, \\\n", + " f\"Expected {num_channels} channel names, got {len(channel_names)}\"\n", + " \n", + " custom_cmaps = [\"RdBu_r\"] * num_channels\n", + "\n", + " visualize_channels(\n", + " ic,\n", + " t0,\n", + " t1,\n", + " target,\n", + " channel_names=channel_names,\n", + " channel_cmaps=custom_cmaps,\n", + " )\n", + " break # Visualize one batch for now" + ] + }, + { + "cell_type": "markdown", + "id": "5989fbbe-7ca7-4820-8916-b3c21844f74a", + "metadata": {}, + "source": [ + "# Building the PARC Model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2c8c24a3-6412-46a4-b58b-872de72332bf", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from PARCtorch.PARCv2 import PARCv2\n", + "from PARCtorch.differentiator.differentiator import ADRDifferentiator\n", + "from PARCtorch.differentiator.finitedifference import FiniteDifference\n", + "from PARCtorch.integrator.integrator import Integrator\n", + "#from PARCtorch.integrator.heun import Heun\n", + "from PARCtorch.integrator.rk4 import RK4 \n", + "from PARCtorch.integrator.euler import Euler\n", + "from PARCtorch.utilities.unet import UNet\n", + "\n", + "from torch.optim import Adam\n", + "\n", + "# Defining 2D Imcompressible Shear Flow\n", + "PARCv2.check = lambda self: True \n", + "PARCv2.check_msg = lambda self, chk: None \n", + "\n", + "n_fe_features = 64\n", + "\n", + "unet_sf = UNet(\n", + " block_dimensions = [64, 64* 2, 64 * 4], \n", + " input_channels = 6, \n", + " output_channels = n_fe_features,\n", + " kernel_size = 3,\n", + " padding_mode = \"circular\",\n", + " up_block_use_concat=[False, True], \n", + " skip_connection_indices=[0], \n", + ")\n", + "\n", + "right_diff = FiniteDifference(padding_mode=\"circular\").cuda() # Use replication padding to handle boundary conditions\n", + "\n", + "base_diff = ADRDifferentiator(\n", + " n_state_var = 2, \n", + " n_const_var = 2,\n", + " n_fe_features = n_fe_features, \n", + " list_adv_idx=[0, 1, 2, 3], \n", + " list_dif_idx=[0, 1, 2, 3], \n", + " feature_extraction = unet_sf, \n", + " padding_mode = \"circular\", \n", + " finite_difference_method = right_diff, \n", + " spade_random_noise = False \n", + ").cuda()\n", + "\n", + "sf_diff = base_diff\n", + "\n", + "rk4 = RK4().cuda() # RK4\n", + "euler = Euler().cuda() # euler\n", + "\n", + "# sf_int = Integrator(\n", + "# clip = False, \n", + "# list_poi_idx=[], \n", + "# num_int = euler, \n", + "# list_dd_int = [None, None, None, None, None, None], \n", + "# padding_mode = \"circular\", \n", + "# finite_difference_method = right_diff, \n", + "# poi_kernel_size = 3, \n", + "# n_poi_features = 64\n", + "# ).cuda()\n", + "\n", + "sf_int = Integrator(\n", + " clip=False,\n", + " list_poi_idx=[], \n", + " num_int=rk4,\n", + " list_dd_int=[None, None, None], \n", + " padding_mode=\"circular\",\n", + " finite_difference_method=right_diff,\n", + " poi_kernel_size=3,\n", + " n_poi_features=64,\n", + " velocity_idx=(2, 3),\n", + " const_start=4\n", + ").cuda()\n", + "\n", + "# Define the loss function (L1 Loss is typically used for regression tasks)\n", + "criterion = torch.nn.L1Loss().cuda()\n", + "\n", + "# Initialize the PARCv2 model with the differentiator, integrator, and loss function\n", + "model = PARCv2(\n", + " sf_diff, \n", + " sf_int, \n", + " criterion\n", + ").cuda()\n", + "\n", + "# Set up the optimizer (Adam is a popular choice for adaptive learning rate optimization)\n", + "optimizer = Adam(\n", + " model.parameters(), \n", + " lr=1e-4\n", + ") " + ] + }, + { + "cell_type": "markdown", + "id": "251a8cbf-c4bf-4c35-b642-8f7658e0eab8", + "metadata": {}, + "source": [ + "# Training the Model" + ] + }, + { + "cell_type": "markdown", + "id": "7a93b1c0-2e22-427c-ab2b-6012ded84f09", + "metadata": {}, + "source": [ + "## First round of training" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9a7386b6-619f-477d-8197-48b987ec7502", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from train import train_model\n", + "from pathlib import Path\n", + "import pickle\n", + "import gc, torch, time\n", + "\n", + "gc.collect()\n", + "torch.cuda.empty_cache()\n", + "\n", + "save_dirs = Path(\"/home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights\")\n", + "save_dirs.mkdir(exist_ok=True, parents=True)\n", + "\n", + "cumulative_path = save_dirs / \"training_losses_cumulative.pkl\"\n", + "cumulative_losses = []\n", + "\n", + "num_epochs = 1000\n", + "global_start = time.time()\n", + "\n", + "best_val_loss = float(\"inf\")\n", + "best_epoch = None\n", + "best_checkpoint_path = save_dirs / \"best_validation.pt\"\n", + "raw_best_path = save_dirs / \"model.pth\"\n", + "\n", + "for epoch in range(1, num_epochs + 1):\n", + " epoch_start = time.time()\n", + "\n", + " # training one epoch\n", + " train_start = time.time()\n", + " train_model(\n", + " model,\n", + " train_loader,\n", + " criterion,\n", + " optimizer,\n", + " num_epochs=1,\n", + " save_dir=save_dirs,\n", + " app='ns',\n", + " )\n", + " train_time = time.time() - train_start\n", + "\n", + " # Load the epochs' training losses\n", + " loss_path = save_dirs / \"training_losses.pkl\"\n", + " with open(loss_path, 'rb') as f:\n", + " epoch_losses = pickle.load(f)\n", + "\n", + " cumulative_losses.extend(epoch_losses)\n", + " with open(cumulative_path, 'wb') as f:\n", + " pickle.dump(cumulative_losses, f)\n", + "\n", + " latest_loss = epoch_losses[-1] if epoch_losses else float(\"nan\") # training loss\n", + "\n", + " # validate the losses via the validation loader\n", + " val_start = time.time()\n", + " model.eval()\n", + " val_losses = []\n", + " val_batch_times = []\n", + " with torch.no_grad():\n", + " for ic, t0, t1, gt in val_loader:\n", + " batch_start = time.time()\n", + " ic, t0, t1, gt = ic.cuda(), t0.cuda(), t1.cuda(), gt.cuda()\n", + " pred = model(ic, t0.squeeze(), t1)\n", + " val_losses.append(criterion(pred, gt).item())\n", + " val_batch_times.append(time.time() - batch_start)\n", + " val_time = time.time() - val_start\n", + " if val_losses:\n", + " val_loss = float(sum(val_losses) / len(val_losses))\n", + " else:\n", + " val_loss = float(\"nan\")\n", + " avg_val_batch_time = val_time / len(val_batch_times) if val_batch_times else 0.0\n", + "\n", + " # Timing and ETA\n", + " epoch_time = time.time() - epoch_start\n", + " elapsed = time.time() - global_start\n", + " eta_secs = (elapsed / epoch) * (num_epochs - epoch)\n", + "\n", + "\n", + " print(\n", + " f\"Epoch {epoch:4d}/{num_epochs} \"\n", + " f\"TrainLoss {latest_loss:.6f} ValLoss {val_loss:.6f} \"\n", + " f\"TrainTime {train_time:.2f}s ValTime {val_time:.2f}s \"\n", + " f\"(avg val batch {avg_val_batch_time:.3f}s) \"\n", + " f\"⏱ Epoch {epoch_time:.2f}s ETA {eta_secs:.2f}s\",\n", + " flush=True\n", + " )\n", + "\n", + " # saving regular checkpoints\n", + " ckpt_dict = {\n", + " 'epoch': epoch,\n", + " 'model_state_dict': model.state_dict(),\n", + " 'optimizer_state_dict': optimizer.state_dict(),\n", + " 'train_loss': latest_loss,\n", + " 'val_loss': val_loss,\n", + " }\n", + " \n", + " latest_checkpoint_path = save_dirs / \"latest.pt\"\n", + " torch.save(ckpt_dict, latest_checkpoint_path)\n", + " \n", + " if epoch % 2 == 0:\n", + " checkpoint_path = save_dirs / f\"checkpoint_epoch_{epoch:04d}.pt\"\n", + " torch.save(ckpt_dict, checkpoint_path)\n", + " print(f\"saved checkpoint: {checkpoint_path} (train_loss={latest_loss:.6f}, val_loss={val_loss:.6f})\", flush=True)\n", + " else:\n", + " print(f\"updated latest checkpoint (epoch {epoch})\", flush=True)\n", + " \n", + " # save best validation loss\n", + " if val_loss < best_val_loss:\n", + " best_val_loss = val_loss\n", + " best_epoch = epoch\n", + " torch.save({\n", + " 'epoch': epoch,\n", + " 'model_state_dict': model.state_dict(),\n", + " 'optimizer_state_dict': optimizer.state_dict(),\n", + " 'train_loss': latest_loss,\n", + " 'val_loss': val_loss,\n", + " }, best_checkpoint_path)\n", + " print(f\"saved new best model (val_loss={val_loss:.6f}) to {best_checkpoint_path}\", flush=True)\n", + " \n", + " torch.save(model.state_dict(), raw_best_path)\n", + " \n", + " gc.collect()\n", + " torch.cuda.empty_cache()\n", + "\n", + "print(f\"\\n saved cumulative losses to: {cumulative_path}\")\n", + "print(f\"Best validation epoch: {best_epoch} with val_loss={best_val_loss:.6f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "68ab53d4-84de-4550-b83d-9b776f888017", + "metadata": {}, + "source": [ + "## Second Round of training (resuming off the last one)" + ] + }, + { + "cell_type": "markdown", + "id": "560bc23c-4b5e-4dfc-9678-a0e76817b1ba", + "metadata": {}, + "source": [ + "Assumes `model`, `train_loader`, `val_loader`, `criterion`, and `optimizer` are already instantiated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ce6bef1-cc5a-4439-8886-ae7da5e97d96", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[resume] loaded checkpoint checkpoint_epoch_0340.pt (20415435 bytes): resuming from epoch 341, best_val_loss=0.057239, best_epoch=340, total_elapsed=0.0s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:34<00:00, 1.55it/s, Batch Loss=0.000517]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0010\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 341/1000 TrainLoss 0.000964 ValLoss 0.061729 TrainTime 154.71s ValTime 16.29s (avg val batch 0.407s) ⏱ Epoch 171.01s ETA 330.49s TotalElapsed 171.0s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0341.pt (train_loss=0.000964, val_loss=0.061729)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:34<00:00, 1.55it/s, Batch Loss=0.000606]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0010\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 342/1000 TrainLoss 0.000966 ValLoss 0.061260 TrainTime 154.74s ValTime 16.29s (avg val batch 0.407s) ⏱ Epoch 171.04s ETA 658.09s TotalElapsed 342.0s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0342.pt (train_loss=0.000966, val_loss=0.061260)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:35<00:00, 1.54it/s, Batch Loss=0.00106] " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0010\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 343/1000 TrainLoss 0.000963 ValLoss 0.062560 TrainTime 155.43s ValTime 10.24s (avg val batch 0.256s) ⏱ Epoch 165.67s ETA 972.51s TotalElapsed 507.7s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0343.pt (train_loss=0.000963, val_loss=0.062560)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:32<00:00, 1.56it/s, Batch Loss=0.000729]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0010\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 344/1000 TrainLoss 0.000951 ValLoss 0.062050 TrainTime 152.87s ValTime 16.06s (avg val batch 0.401s) ⏱ Epoch 168.94s ETA 1290.36s TotalElapsed 676.7s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0344.pt (train_loss=0.000951, val_loss=0.062050)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:36<00:00, 1.53it/s, Batch Loss=0.000951]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0009\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 345/1000 TrainLoss 0.000935 ValLoss 0.062558 TrainTime 156.70s ValTime 15.83s (avg val batch 0.396s) ⏱ Epoch 172.55s ETA 1612.27s TotalElapsed 849.2s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0345.pt (train_loss=0.000935, val_loss=0.062558)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:32<00:00, 1.57it/s, Batch Loss=0.000566]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0009\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 346/1000 TrainLoss 0.000934 ValLoss 0.061868 TrainTime 152.33s ValTime 10.36s (avg val batch 0.259s) ⏱ Epoch 162.69s ETA 1912.66s TotalElapsed 1011.9s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0346.pt (train_loss=0.000934, val_loss=0.061868)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:42<00:00, 1.48it/s, Batch Loss=0.000811]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0009\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 347/1000 TrainLoss 0.000938 ValLoss 0.062904 TrainTime 162.13s ValTime 10.28s (avg val batch 0.257s) ⏱ Epoch 172.41s ETA 2228.69s TotalElapsed 1184.3s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0347.pt (train_loss=0.000938, val_loss=0.062904)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:26<00:00, 1.63it/s, Batch Loss=0.000571]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0009\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 348/1000 TrainLoss 0.000927 ValLoss 0.061032 TrainTime 146.96s ValTime 10.18s (avg val batch 0.255s) ⏱ Epoch 157.14s ETA 2513.30s TotalElapsed 1341.5s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0348.pt (train_loss=0.000927, val_loss=0.061032)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:27<00:00, 1.62it/s, Batch Loss=0.000766]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0009\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at 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/home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 368/1000 TrainLoss 0.000903 ValLoss 0.060326 TrainTime 146.65s ValTime 10.03s (avg val batch 0.251s) ⏱ Epoch 156.68s ETA 7845.69s TotalElapsed 4568.4s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0368.pt (train_loss=0.000903, val_loss=0.060326)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:26<00:00, 1.64it/s, Batch Loss=0.000491]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0009\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 369/1000 TrainLoss 0.000899 ValLoss 0.060487 TrainTime 146.08s ValTime 9.98s (avg val batch 0.250s) ⏱ Epoch 156.06s ETA 8078.91s TotalElapsed 4724.4s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0369.pt (train_loss=0.000899, val_loss=0.060487)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:29<00:00, 1.60it/s, Batch Loss=0.000959]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0009\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 370/1000 TrainLoss 0.000920 ValLoss 0.062260 TrainTime 149.42s ValTime 15.82s (avg val batch 0.396s) ⏱ Epoch 165.27s ETA 8325.71s TotalElapsed 4889.7s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0370.pt (train_loss=0.000920, val_loss=0.062260)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:32<00:00, 1.57it/s, Batch Loss=0.00112] " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0009\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 371/1000 TrainLoss 0.000910 ValLoss 0.059690 TrainTime 152.24s ValTime 10.07s (avg val batch 0.252s) ⏱ Epoch 162.32s ETA 8565.28s TotalElapsed 5052.0s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0371.pt (train_loss=0.000910, val_loss=0.059690)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 81%|████████ | 193/239 [01:58<00:27, 1.66it/s, Batch Loss=0.00127] " + ] + } + ], + "source": [ + "from train import train_model\n", + "from pathlib import Path\n", + "import pickle\n", + "import gc, torch, time, signal, os, tempfile \n", + "\n", + "save_dirs = Path(\"/home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights\")\n", + "save_dirs.mkdir(exist_ok=True, parents=True)\n", + "\n", + "cumulative_path = save_dirs / \"training_losses_cumulative.pkl\"\n", + "raw_best_path = save_dirs / \"model.pth\" # synced raw best weights\n", + "best_checkpoint_path = save_dirs / \"best_validation.pt\"\n", + "\n", + "num_epochs = 1000 # total target epochs\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "model = model.to(device)\n", + "\n", + "def atomic_torch_save(obj, path: Path):\n", + " \"\"\"Write checkpoint atomically to avoid partial files.\"\"\"\n", + " path = Path(path)\n", + " with tempfile.NamedTemporaryFile(dir=path.parent, delete=False) as tmp:\n", + " tmp_path = Path(tmp.name)\n", + " torch.save(obj, tmp_path)\n", + " os.replace(tmp_path, path) # atomic on POSIX\n", + "\n", + "def try_load_ckpt(path: Path, map_location):\n", + " \"\"\"Reject obviously partial files; try torch.load with a clear error.\"\"\"\n", + " if not path.is_file():\n", + " raise FileNotFoundError(path)\n", + " if path.stat().st_size < (1 << 20): # if file is < 1MB, it is very likely partial/corrupt\n", + " raise RuntimeError(f\"too small ({path.stat().st_size} bytes)\")\n", + " return torch.load(path, map_location=map_location)\n", + "\n", + "# resume state holders\n", + "best_val_loss = float(\"inf\")\n", + "best_epoch = None\n", + "total_elapsed = 0.0 # accumulated over all previous epochs (if resumed)\n", + "start_epoch = 1\n", + "cumulative_losses = []\n", + "\n", + "# load existing cumulative losses if any \n", + "if cumulative_path.exists():\n", + " try:\n", + " with open(cumulative_path, \"rb\") as f:\n", + " cumulative_losses = pickle.load(f)\n", + " except Exception:\n", + " cumulative_losses = []\n", + "\n", + "# load best validation loss\n", + "if best_checkpoint_path.exists():\n", + " try:\n", + " ckpt = torch.load(best_checkpoint_path, map_location=device)\n", + " if isinstance(ckpt, dict):\n", + " best_val_loss = ckpt.get(\"val_loss\", best_val_loss)\n", + " best_epoch = ckpt.get(\"epoch\", best_epoch)\n", + " except Exception as e:\n", + " print(f\"[resume] warning: cannot read best_validation.pt: {e}\")\n", + "\n", + "# find latest checkpoint in storage to resume\n", + "ckpt_files = sorted(\n", + " [p for p in save_dirs.glob(\"checkpoint_epoch_*.pt\") if p.is_file()],\n", + " key=lambda p: p.stat().st_mtime, reverse=True\n", + ")\n", + "for p in ckpt_files:\n", + " try:\n", + " ckpt = try_load_ckpt(p, map_location=device)\n", + " if \"model_state_dict\" in ckpt:\n", + " model.load_state_dict(ckpt[\"model_state_dict\"])\n", + " else:\n", + " model.load_state_dict(ckpt) # fallback raw state_dict\n", + " if \"optimizer_state_dict\" in ckpt:\n", + " optimizer.load_state_dict(ckpt[\"optimizer_state_dict\"])\n", + " # restore best validation if present\n", + " best_val_loss = ckpt.get(\"val_loss\", best_val_loss)\n", + " best_epoch = ckpt.get(\"epoch\", best_epoch)\n", + " total_elapsed = ckpt.get(\"total_elapsed\", 0.0)\n", + " start_epoch = ckpt.get(\"epoch\", 0) + 1\n", + " print(f\"[resume] loaded checkpoint {p.name} ({p.stat().st_size} bytes): \"\n", + " f\"resuming from epoch {start_epoch}, best_val_loss={best_val_loss:.6f}, \"\n", + " f\"best_epoch={best_epoch}, total_elapsed={total_elapsed:.1f}s\")\n", + " break\n", + " except Exception as e:\n", + " print(f\"[resume] skipping {p.name}: {e}\")\n", + "else:\n", + " ckpt = None \n", + "\n", + "# shut down \n", + "state = {\"terminate\": False}\n", + "def handle_sigterm(signum, frame):\n", + " print(\"Received SIGTERM; will finish current epoch and exit cleanly.\", flush=True)\n", + " state[\"terminate\"] = True\n", + "signal.signal(signal.SIGTERM, handle_sigterm)\n", + "\n", + "# training loop \n", + "for epoch in range(start_epoch, num_epochs + 1):\n", + " epoch_start = time.time()\n", + "\n", + " model.train()\n", + " train_start = time.time()\n", + " train_model(\n", + " model,\n", + " train_loader,\n", + " criterion,\n", + " optimizer,\n", + " num_epochs=1,\n", + " save_dir=save_dirs,\n", + " app='ns',\n", + " )\n", + " train_time = time.time() - train_start\n", + "\n", + " # load the epoch's training loss\n", + " try:\n", + " loss_path = save_dirs / \"training_losses.pkl\"\n", + " with open(loss_path, \"rb\") as f:\n", + " epoch_losses = pickle.load(f)\n", + " except Exception:\n", + " epoch_losses = []\n", + "\n", + " cumulative_losses.extend(epoch_losses)\n", + " with open(cumulative_path, \"wb\") as f:\n", + " pickle.dump(cumulative_losses, f)\n", + "\n", + " latest_loss = epoch_losses[-1] if epoch_losses else float(\"nan\")\n", + "\n", + " # compute validation loss\n", + " val_start = time.time()\n", + " model.eval()\n", + " val_losses = []\n", + " val_batch_times = []\n", + " with torch.no_grad():\n", + " for ic, t0, t1, gt in val_loader:\n", + " batch_start = time.time()\n", + " ic, t0, t1, gt = ic.to(device), t0.to(device), t1.to(device), gt.to(device) # MOD: .to(device)\n", + " pred = model(ic, t0.squeeze(), t1) # assumes PARC expects t0 scalar, t1 vector\n", + " val_losses.append(criterion(pred, gt).item())\n", + " val_batch_times.append(time.time() - batch_start)\n", + " val_time = time.time() - val_start\n", + " val_loss = float(sum(val_losses) / len(val_losses)) if val_losses else float(\"nan\")\n", + " avg_val_batch_time = val_time / len(val_batch_times) if val_batch_times else 0.0\n", + "\n", + " # update ETA of model finishing training\n", + " epoch_time = time.time() - epoch_start\n", + " total_elapsed += epoch_time\n", + " epochs_done = epoch\n", + " eta_secs = (total_elapsed / epochs_done) * (num_epochs - epochs_done)\n", + "\n", + " print(\n", + " f\"Epoch {epoch:4d}/{num_epochs} \"\n", + " f\"TrainLoss {latest_loss:.6f} ValLoss {val_loss:.6f} \"\n", + " f\"TrainTime {train_time:.2f}s ValTime {val_time:.2f}s \"\n", + " f\"(avg val batch {avg_val_batch_time:.3f}s) \"\n", + " f\"⏱ Epoch {epoch_time:.2f}s ETA {eta_secs:.2f}s TotalElapsed {total_elapsed:.1f}s\",\n", + " flush=True\n", + " )\n", + "\n", + " # Save regular checkpoints\n", + " checkpoint_path = save_dirs / f\"checkpoint_epoch_{epoch:04d}.pt\"\n", + " atomic_torch_save({\n", + " 'epoch': epoch,\n", + " 'model_state_dict': model.state_dict(),\n", + " 'optimizer_state_dict': optimizer.state_dict(),\n", + " 'train_loss': latest_loss,\n", + " 'val_loss': val_loss,\n", + " 'total_elapsed': total_elapsed,\n", + " }, checkpoint_path)\n", + " print(f\"saved checkpoint: {checkpoint_path} (train_loss={latest_loss:.6f}, val_loss={val_loss:.6f})\", flush=True)\n", + "\n", + " # loading model with best validation\n", + " if val_loss < best_val_loss:\n", + " best_val_loss = val_loss\n", + " best_epoch = epoch\n", + " atomic_torch_save({\n", + " 'epoch': epoch,\n", + " 'model_state_dict': model.state_dict(),\n", + " 'optimizer_state_dict': optimizer.state_dict(),\n", + " 'train_loss': latest_loss,\n", + " 'val_loss': val_loss,\n", + " 'total_elapsed': total_elapsed,\n", + " }, best_checkpoint_path)\n", + " atomic_torch_save(model.state_dict(), raw_best_path)\n", + " print(f\"saved new best model (val_loss={val_loss:.6f}) to {best_checkpoint_path}\", flush=True)\n", + " print(f\"also updated raw best weights to {raw_best_path}\", flush=True)\n", + "\n", + " gc.collect()\n", + " if torch.cuda.is_available(): # MOD: guard\n", + " torch.cuda.empty_cache()\n", + "\n", + " if state[\"terminate\"]:\n", + " print(f\"Terminating early after epoch {epoch} due to signal. Progress saved.\", flush=True)\n", + " break\n", + "\n", + "# summary statistics printing\n", + "print(f\"\\nSaved cumulative losses to: {cumulative_path}\")\n", + "print(f\"Best validation epoch: {best_epoch} with val_loss={best_val_loss:.6f}\")\n", + "done_flag = save_dirs / \"done.flag\"\n", + "done_flag.write_text(\"training complete\" if epochs_done >= num_epochs else \"interrupted\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "c2340f81-e0b5-4fb0-bea5-6be83485e419", + "metadata": {}, + "source": [ + "# Loading the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "beea2464-fb89-4a7f-8c4c-dc2bf295b8dc", + "metadata": {}, + "outputs": [], + "source": [ + "from PARCtorch.utilities.load import load_model_weights\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "model_weights_path = \"/home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_Euler_model_weights/model.pth\" # Replace with your path\n", + "model = load_model_weights(model, model_weights_path, device)" + ] + }, + { + "cell_type": "markdown", + "id": "3a1c76db-6285-4565-a57f-f7c500ff043b", + "metadata": {}, + "source": [ + "# Creating the Sequence DataLoader\n", + "\n", + "Now its time to test out our model on the testing dataset!\n", + "\n", + "To do so, we must first redo everything we did during the training stage - but for the new testing data. We first load the test samples using the same `WellDatasetInterface`, except that we'll be pointing towards the validation test dataset of the directory.\n", + "\n", + "After that we again wrap them in a `DataLoader` for sample batching (same as what we did in the training stage). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7ae4ceb5-22be-44a0-90b7-ff7095f7c4c7", + "metadata": {}, + "outputs": [], + "source": [ + "from torch.utils.data import DataLoader, SequentialSampler\n", + "from PARCtorch.data.welldataset_single_trajectory import WellDatasetInterface\n", + "from PARCtorch.data.dataset import initial_condition_collate_fn, custom_collate_fn\n", + "\n", + "test_dir = \"/home/wkt7ne/PARCtorch/PARCtorch/shear_flow/data/test\"\n", + "single_train_dir = \"/home/wkt7ne/PARCtorch/PARCtorch/shear_flow/data/single_file\"\n", + "experimental_train_dir = \"/home/wkt7ne/PARCtorch/PARCtorch/shear_flow/data/experimental_train\"\n", + "train_dir = \"/home/wkt7ne/PARCtorch/PARCtorch/shear_flow/data/train\"\n", + "val_dir = \"/home/wkt7ne/PARCtorch/PARCtorch/shear_flow/data/validation\"\n", + "\n", + "path = test_dir\n", + "\n", + "future_steps = 199\n", + "batch_size = 20\n", + "temporal_skips = 10\n", + "\n", + "gt_spatial_transform = Compose([\n", + " Resize((new_h, new_w)),\n", + "])\n", + "\n", + "# seq_dataset = WellDatasetInterface(\n", + "# well_dataset_args = {\n", + "# \"path\": path,\n", + "# #\"exclude_filters\": [\"shear_flow_Reynolds_5e5_Schmidt_1e0.hdf5\"],\n", + "# \"max_rollout_steps\": future_steps\n", + "# },\n", + "# future_steps = future_steps,\n", + "# delta_t = 2.0,\n", + "# min_val = min_val_tensor,\n", + "# max_val = max_val_tensor,\n", + "# temporal_skip = temporal_skips,\n", + "# spatial_transform = gt_spatial_transform,\n", + "# add_constant_scalars= True, \n", + "# single_trajectory=False,\n", + "# #traj_idx=0\n", + "# )\n", + "\n", + "# ic, t0, t1, gt = seq_dataset[0]\n", + "# seq_dataset.transform = gt_spatial_transform\n", + "\n", + "# seq_loader = DataLoader(\n", + "# seq_dataset,\n", + "# batch_size = batch_size,\n", + "# shuffle = False,\n", + "# num_workers = 1,\n", + "# pin_memory = True,\n", + "# collate_fn = initial_condition_collate_fn,\n", + "# )\n", + "\n", + "# ic, t0, t1, gt = seq_dataset[0]\n", + "# print(\"IC shape:\", ic.shape) # expect [C, 80, 168]\n", + "# print(\"GT shape:\", gt.shape) # expect [T_out, C, 80, 168]\n", + "# print(\"len(t1):\", len(t1)) " + ] + }, + { + "cell_type": "markdown", + "id": "06474ccd-b36f-4010-9e28-4b11bcb56d00", + "metadata": {}, + "source": [ + "# Loading Ground Truths" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9eacf93-f7f1-4152-a73d-f3655d94cbdf", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the dataset\n", + "\n", + "# \"exclude_filters\": [\"shear_flow_Reynolds_5e5_Schmidt_1e0.hdf5\"]\n", + "gt_dataset = WellDatasetInterface(\n", + " well_dataset_args = {\n", + " \"path\": path,\n", + " \"exclude_filters\": [\"shear_flow_Reynolds_5e5_Schmidt_1e0.hdf5\"],\n", + " \"max_rollout_steps\": future_steps\n", + " },\n", + " future_steps = future_steps,\n", + " delta_t = 2.0,\n", + " add_constant_scalars= True,\n", + " temporal_skip = temporal_skips, \n", + " spatial_transform = gt_spatial_transform, \n", + " min_val = min_val_tensor,\n", + " max_val = max_val_tensor,\n", + " single_trajectory = True,\n", + " traj_idx = 0,\n", + ")\n", + "\n", + "gt_loader = DataLoader(\n", + " gt_dataset,\n", + " batch_size=batch_size,\n", + " shuffle=False,\n", + " num_workers=1,\n", + " pin_memory=True,\n", + " collate_fn=custom_collate_fn,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "7747e1b6-ed33-4dea-b3e0-fbb3f4dae86a", + "metadata": {}, + "source": [ + "# Rollout and Snapshot Prediction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3caae88f-bb5f-4511-9b8c-e4611470ca65", + "metadata": {}, + "outputs": [], + "source": [ + "import os, numpy as np, torch, matplotlib.pyplot as plt, imageio\n", + "\n", + "mode = \"rollout\" # snapshot or rollout\n", + "out_dir = \"/home/wkt7ne/PARCtorch/PARCtorch/demos/gifs\"\n", + "figsize, dpi, fps = (6, 3), 120, 6\n", + "const_start = 4 # first index of constant channels\n", + "keep_constants = True\n", + "center_p_for_viz = True\n", + "pressure_pct = (1, 95) # reserved for future pressure scaling (unused)\n", + "\n", + "# rollout visualization stabilizers\n", + "safe_dt = 1 # if dataset delta_t > safe_dt, use substeps\n", + "nan_guard = True # replace NaN/Inf with 0\n", + "unit_velocity = True # normalize (vx, vy) to unit length\n", + "\n", + "# Setup\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "model.eval()\n", + "os.makedirs(out_dir, exist_ok=True)\n", + "\n", + "# Batch\n", + "gt_ic, gt_t0, gt_t1, gt = next(iter(gt_loader)) # ic:[B,C,H,W], t0:[], t1:[T], gt:[T,B,C,H,W]\n", + "gt_ic, gt_t0, gt_t1, gt = gt_ic.to(device), gt_t0.to(device), gt_t1.to(device), gt.to(device)\n", + "t0_abs = gt_t0.squeeze() # scalar absolute start time\n", + "t1_vec = gt_t1.flatten() # [T] absolute target times\n", + "base_dt = float(t1_vec[0] - t0_abs) # assumed uniform dataset step\n", + "\n", + "@torch.no_grad()\n", + "def predict_next_state(x, t_prev_abs, t_next_abs):\n", + " \"\"\"Advance state from t_prev_abs to t_next_abs in one model call. Return shape [B,C,H,W].\"\"\"\n", + " y = model(x, t_prev_abs, t_next_abs.view(1))\n", + " if y.ndim == 5: y = y[-1]\n", + " if y.ndim == 3: y = y.unsqueeze(0)\n", + " return y\n", + "\n", + "@torch.no_grad()\n", + "def constrain_state(x, ic, const_start=4, unit_velocity=True, nan_guard=True, keep_constants=True):\n", + " \"\"\"Ensure valid shape; optional NaN removal; pin constants; center pressure; normalize velocity.\"\"\"\n", + " if x.ndim == 5: x = x[-1]\n", + " if x.ndim == 3: x = x.unsqueeze(0)\n", + " if ic.ndim == 3: ic = ic.unsqueeze(0)\n", + " if nan_guard:\n", + " x = torch.nan_to_num(x, nan=0.0, posinf=0.0, neginf=0.0)\n", + " if keep_constants and x.shape[1] > const_start and ic.shape[1] >= x.shape[1]:\n", + " x[:, const_start:] = ic[:, const_start:]\n", + " if x.shape[1] >= 2:\n", + " p = x[:, 1:2]\n", + " x[:, 1:2] = p - p.mean(dim=(-2, -1), keepdim=True)\n", + " if unit_velocity and x.shape[1] >= 4:\n", + " eps = 1e-12\n", + " vx, vy = x[:, 2:3], x[:, 3:4]\n", + " speed = torch.sqrt(vx*vx + vy*vy + eps)\n", + " x[:, 2:3], x[:, 3:4] = vx / speed, vy / speed\n", + " if nan_guard:\n", + " x = torch.nan_to_num(x, nan=0.0, posinf=0.0, neginf=0.0)\n", + " return x\n", + "\n", + "def pick_color_limits_from_ground_truth(ch, a_gt, pressure_pct=(1, 99)):\n", + " \"\"\"Channel-wise color limits:\n", + " tracer: fixed [0,1]; pressure: ±percentile of |GT fluctuations|;\n", + " velocity: [-1,1]; other: GT 1–99th percentile.\"\"\"\n", + " if ch == 0: # tracer\n", + " return 0.0, 1.0\n", + " if ch == 1: # pressure (symmetric about 0)\n", + " a = a_gt - a_gt.mean(axis=(1, 2), keepdims=True)\n", + " hi = float(pressure_pct[1]) # <- actually use your setting\n", + " v = float(max(np.percentile(np.abs(a), hi), 1e-6))\n", + " return -v, v\n", + " if ch in (2, 3): # velocities\n", + " return -1.0, 1.0\n", + " lo, hi = np.percentile(a_gt, [1, 99])\n", + " return float(lo), float(hi)\n", + "\n", + "\n", + "# model inference logic\n", + "T = gt.shape[0]\n", + "if mode == \"snapshot\":\n", + " pred_steps = []\n", + " for k in range(T):\n", + " prev_state = gt_ic if k == 0 else gt[k-1]\n", + " t_prev, t_next = (t0_abs if k == 0 else t1_vec[k-1]), t1_vec[k]\n", + " y = predict_next_state(prev_state, t_prev, t_next)\n", + " y = constrain_state(y, gt_ic, const_start, unit_velocity, nan_guard, keep_constants)\n", + " pred_steps.append(y)\n", + " pred = torch.stack(pred_steps, dim=0)\n", + "elif mode == \"rollout\":\n", + " \"\"\"rollout with optional substeps to improve stability and accuracy.\"\"\"\n", + " pred_steps, x = [], gt_ic.clone()\n", + " t_prev_abs = t0_abs\n", + " for i in range(T):\n", + " t_next_abs = t_prev_abs + base_dt\n", + " n_sub = max(1, int(np.ceil(abs(base_dt) / safe_dt)))\n", + " if n_sub > 1:\n", + " sub_times = torch.linspace(float(t_prev_abs), float(t_next_abs), n_sub+1, device=device)[1:]\n", + " y, tp = x, t_prev_abs\n", + " for tn in sub_times:\n", + " y = predict_next_state(y, tp, tn)\n", + " y = constrain_state(y, gt_ic, const_start, unit_velocity, nan_guard, keep_constants)\n", + " tp = tn\n", + " x = y\n", + " else:\n", + " y = predict_next_state(x, t_prev_abs, t_next_abs)\n", + " x = constrain_state(y, gt_ic, const_start, unit_velocity, nan_guard, keep_constants)\n", + " pred_steps.append(x)\n", + " t_prev_abs = t_next_abs\n", + " pred = torch.stack(pred_steps, dim=0)\n", + "else:\n", + " raise ValueError(\"mode must be 'snapshot' or 'rollout'\")\n", + "\n", + "# Plotting preparation\n", + "T = min(pred.shape[0], gt.shape[0])\n", + "C = min(pred.shape[2], gt.shape[2])\n", + "pred, gt = pred[:T, :, :C], gt[:T, :, :C]\n", + "preds_np = pred.detach().cpu().numpy()\n", + "gts_np = gt.detach().cpu().numpy()\n", + "batch0 = 0\n", + "c_fields = min(4, C)\n", + "c_const = max(0, C - c_fields)\n", + "field_names = [\"tracer\", \"pressure\", \"velocity_x\", \"velocity_y\"][:c_fields]\n", + "const_names = [\"reynolds\", \"schmidt\"][:c_const] + [f\"const{j}\" for j in range(max(0, c_const-2))]\n", + "names = field_names + const_names\n", + "cmaps = [\"RdBu_r\", \"RdBu_r\", \"seismic\", \"seismic\"][:c_fields] + ([\"seismic\"] * c_const)\n", + "\n", + "def center_frames_for_display(arr):\n", + " \"\"\"Center array by subtracting spatial mean per frame (display only).\"\"\"\n", + " return arr - arr.mean(axis=(1, 2), keepdims=True)\n", + "\n", + "# Plotting\n", + "for ch in range(C):\n", + " a_pred = preds_np[:, batch0, ch].copy()\n", + " a_gt = gts_np[:, batch0, ch].copy()\n", + " vmin, vmax = pick_color_limits_from_ground_truth(ch, a_gt, pressure_pct)\n", + " if center_p_for_viz and ch == 1:\n", + " a_pred = center_frames_for_display(a_pred)\n", + " a_gt = center_frames_for_display(a_gt)\n", + " a_pred_vis = np.clip(a_pred, vmin, vmax)\n", + " frames = []\n", + " for ti in range(T):\n", + " fig, axes = plt.subplots(1, 2, figsize=figsize, dpi=dpi, constrained_layout=True)\n", + " im0 = axes[0].imshow(a_pred_vis[ti], cmap=cmaps[ch], vmin=vmin, vmax=vmax, interpolation=\"nearest\")\n", + " im1 = axes[1].imshow(a_gt[ti], cmap=cmaps[ch], vmin=vmin, vmax=vmax, interpolation=\"nearest\")\n", + " \n", + " cbar = fig.colorbar(im1, ax=axes, fraction=0.05, pad=0.02)\n", + "\n", + " if ch == 0: # tracer color bar fix\n", + " cbar.set_ticks([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])\n", + " \n", + " axes[0].set_title(f\"{names[ch]} (pred) | frame {ti+1}/{T}\", fontsize=9)\n", + " axes[1].set_title(f\"{names[ch]} (gt) | frame {ti+1}/{T}\", fontsize=9)\n", + " for ax in axes: ax.axis(\"off\")\n", + " fig.canvas.draw()\n", + " buf, (wbuf, hbuf) = fig.canvas.print_to_buffer()\n", + " frames.append(np.frombuffer(buf, dtype=np.uint8).reshape(hbuf, wbuf, 4)[..., :3])\n", + " plt.close(fig)\n", + " gif_path = os.path.join(out_dir, f\"{mode}_ch{ch:02d}_{names[ch]}.gif\")\n", + " imageio.mimsave(gif_path, frames, fps=fps)\n", + " print(\"wrote\", gif_path)\n" + ] + }, + { + "cell_type": "markdown", + "id": "a44013f6-7653-4203-8130-01784c61e3c7", + "metadata": {}, + "source": [ + "# Visualizing the Loss Curve \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e0c97334-61c3-4cc7-a4d1-9da986d1bda7", + "metadata": {}, + "source": [ + "Finally, to monitor the model's learning progress and ensure that the optimization is happening correctly, it's important to plot the loss curve over each training epoch. Overall, a decreasing loss trend will indicate that the model is minimizing the loss function as intended. \n", + "\n", + "**To do so, we:**\n", + "\n", + "1. Load the saved training loss history from the pickle file, which was recorded durin the training to a designated directory.\n", + "\n", + "2. Plot the loss against epochs to visualize how the model's performance changes over time\n", + "\n", + "3. Save the loss curve image for future references\n", + "\n", + "**How to diagnose your model performances:**\n", + "\n", + "- **Steadily decreasing loss (Ideal)**: suggests that the model is learning effectively, and that its predictions are getting closer to the ground truth as training progresses.\n", + "\n", + " - Consider stopping early (reducing `num_epochs`) if the validation stabilizes at an earlier point of the training iterations.\n", + " \n", + "- **Loss increases or becomes erratic**: numerical instability due to large spatial inputs, or suggests that perhaps the batch size is too large for GPU memory, causing gradient accumulation fragmentation. \n", + "\n", + " - Consider reducing `batch_size` to free up more memory for GPU.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2ddc9577-779e-4ceb-b6e6-a1c9d1b618d6", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pickle\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Find your training_'losses.pkl' file in the directory that you stored your trained model weights in\n", + "with open( \"/home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_Euler_debug_model_weights/training_losses_cumulative.pkl\", 'rb' ) as f: # replace with your path\n", + " history = pickle.load(f)\n", + "plt.figure(figsize=(10,6))\n", + "plt.title('ShearFlow Model Loss Curve')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Loss')\n", + "plt.plot(history)\n", + "#plt.xlim(99, 500)\n", + "plt.savefig('normalization_loss_curve.png')\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "591d388f-17d2-4f16-9d6c-c26e85b732a5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "start, end = 800, 1000 # epoch numbers (1-based)\n", + "start_idx = max(0, start - 1) # convert to 0-based index\n", + "end_idx = min(len(history), end) # slice end is exclusive\n", + "\n", + "\n", + "epochs = range(start, end_idx + 1) # x-axis as epoch numbers\n", + "losses = history[start_idx:end_idx]\n", + "\n", + "plt.figure(figsize=(10,6))\n", + "plt.title('ShearFlow Model Loss Curve')\n", + "plt.xlabel('Epoch'); plt.ylabel('Loss')\n", + "plt.plot(epochs, losses)\n", + "plt.tight_layout()\n", + "plt.savefig('normalization_loss_curve_100_500.png')\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (parc)", + "language": "python", + "name": "parc" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ShearFlow.ipynb b/ShearFlow.ipynb new file mode 100755 index 0000000..ece5dfc --- /dev/null +++ b/ShearFlow.ipynb @@ -0,0 +1,2654 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a323a953-d55f-4a06-87fa-21a5890fa35b", + "metadata": {}, + "source": [ + "
\n", + "

\n", + " Physics Aware Recurrent Convolutional Neural Network (PARC): ShearFlow Equation Demo\n", + "

\n", + "
\n", + " \"Image\n", + " \"Image\n", + " \"Image\n", + "
\n", + "
\n", + "\n", + "

\n", + "A customizable framework to embed physics in Deep Learning.\n", + "PARC's formulation is inspired by Advection-Diffusion-Reaction processes and uses an Inductive Bias approach to constrain the Neural Network.\n", + "

\n" + ] + }, + { + "cell_type": "markdown", + "id": "93a45e9e-6a11-4de3-a329-be4ed5ef50eb", + "metadata": {}, + "source": [ + "# Modeling Shear Flow with PARC\n", + "\n", + "This notebook aims to simulate the 2D periodic shear flow dynamics, a simplified version of the Navier-Stokes equation in a condition that it is incompressible (without consideration for pressure). A shear flow is a type of fluid that contains layers sliding past each other at varying velocities.\n", + "\n", + "The notebook will guide you through from start to finish in preparing, training, and modeling the physics-based equation's prediction results. The notebook primarily covers three sections:\n", + "- loading and preparing the data for the Shear Flow's Equation\n", + "- Using the PARC Model to learn and predict the time evolution of velocity fields $u$ and tracer distributions $s$\n", + "- Evaluating the model's performance and compare predicted results to ground truth" + ] + }, + { + "cell_type": "markdown", + "id": "f9143c39-6bbd-473e-8d5a-ba97446cdc68", + "metadata": {}, + "source": [ + "# What exactly is a Shear Flow?\n", + "\n", + "Shear Flows can be observed in many real life scenarios. For example, the air closest to an airplane's wings moves slower due to friction, while the air further way moves faster. This velocity difference in turn creates a shear flow and is critical for generating lift power for the plane. In a natural setting such as in rivers, water moves slower at the surface and faster at the river bed. The veloctiy gradient again creates 'stress' on the bed surface and thereby causes phenomenons like erotion or transportation of sediments.\n", + "\n", + "Mathmatically, its primary equations governing the Shear Flow simulations are:\n", + "\n", + "$$\\frac{\\partial u}{\\partial t} + \\nabla p - \\nu \\Delta u = -u\\cdot \\nabla u,$$\n", + "\n", + "$$\\frac{\\partial s}{\\partial t} - D\\Delta s = -u \\cdot \\nabla s$$\n", + "\n", + "In the first equation:\n", + "- $u$ is the velocity of the fluid \n", + "- $\\frac{\\partial u}{\\partial t}$ is the changes of velocity over time\n", + "- $\\nabla p$ is the effect of pressure pushing the fluid around\n", + "- $\\nu \\Delta u$ describes the internal friction (viscosity) that resist fluid motion (**note:** $\\nu$ stands for viscosity, $\\Delta = \\nabla \\cdot \\nabla$ it measures how 'curvy' something is, like a second-derivative that tells you whether you are at a global/local maximum, minimum, or a saddle point)\n", + "- $-u\\cdot \\nabla u$ describes the advection, meaning how a fluid that is already is motion is carrying itself along \n", + "\n", + "To sum up, this describes how changes in motion is equivalent to forces applied to the fluid due to **pressure** + **friction** - the fluid advection\n", + "\n", + "In the second equation:\n", + "- $u$ again is the velocity vector of the fluid\n", + "- $s$ is the **flow tracer**, you can think of it as any substance/fluid property being carried by the fluid and is used to track flow velocity. (i.e. temperature, salinity, or dyes and small particles)\n", + "- $\\frac{\\partial s}{\\partial t}$ describes how the tracer changes over time\n", + "- $-D\\Delta s$ describes te natural diffusion/spreading of the tracer (I.e. how fast a substance spreads in water) (**note:** D stands for diffusivity, which measures how easily tracers spread)\n", + "- $-u\\cdot \\nabla s$ describes the rate of decrease of the tracer $s$ in the direction of the flow\n", + "\n", + "This overall means that changes in tracer concentration equals how the tracer spreads out - how the tracer is moved around by the fluid\n", + "\n", + "The two PDEs are **parameterized** by the Reynolds & Schmidt through $\\nu$ and $D$ in the form of:\n", + "\n", + "- **Viscosity**:\n", + "$$\\nu = \\frac{1}{Reynolds}$$\n", + "- **Diffusivity**:\n", + "$$D = \\frac{\\nu}{Schmidt}$$" + ] + }, + { + "cell_type": "markdown", + "id": "0e75a6bd-9f04-4c8b-89bc-62988f6eddda", + "metadata": {}, + "source": [ + "# Goals of PARC for Shear Flow \n", + "\n", + "In this notebook we aim to use the PARC Neural Network to predict future **velocity fields $u$** and **tracer distributions $s$** based on current states. The neural network will be estimating the hidden physical parameters like $\\nu$ and $D$ from the given observed Shear Flow data. This is essential as the neural network could learn not just the solutions to the predicted outcome of the shear flow at a future state, but also learning how the outcome changes/varies with different parameter values of **Viscosity** and **Diffusivity**\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "f204bf63-4c07-4b1a-82bc-d5588615421c", + "metadata": {}, + "source": [ + "# Setting Up\n", + "\n", + "This document serves as a guide to training a PARC model for the Shear Flow equation. Here are the initial steps to take before you begin training your PARC model!\n", + "\n", + "Download & Prepare Data:\n", + "- Extract the downloaded data and ensure that it is placed in the following directory: `PARCtorch/PARCtorch/data`\n", + "- Make sure that the file paths at `train_dir` and `test_dir` point to the correct paths to your training and testing data, e.g. `../data/train` or `../data/test`\n", + "\n", + "Install PARCtorch:\n", + "- Ensure PARCtorch is installed in your Python Environment, you could view the installation instructions [here](https://github.com/baeklab/PARCtorch/tree/main#installation)\n", + "- After the installation, install all the relevant dependencies with `!pip install -r requirements.txt` (The versions of your dependencies need to be consistent with our requirements, or else the script might not run!)\n", + "- Optional (only if the script doesn't run): add the root directory to the system's path so that it runs" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "77a23205-5068-44d2-8b1a-19debbd9ae51", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "\n", + "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), \"..\")))\n" + ] + }, + { + "cell_type": "markdown", + "id": "a6bbb03f-fde8-45b1-bd59-3fda96043d6a", + "metadata": {}, + "source": [ + "# Compute Data Normalization\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "53db1c08-7b0c-4ac7-8a20-ce1f2e672fc1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os, re, torch\n", + "from torch.utils.data import DataLoader\n", + "from PARCtorch.data.dataset import custom_collate_fn\n", + "\n", + "# ensure GT shape is [B,T,C,H,W] \n", + "def _to_BTCHW(gt, ic_BCHW):\n", + " if gt.dim() != 5:\n", + " raise ValueError(f\"gt must be 5D, got {gt.shape}\")\n", + " B = ic_BCHW.shape[0]\n", + " if gt.shape[0] == B: # [B,T,C,H,W]\n", + " return gt\n", + " elif gt.shape[1] == B: # [T,B,C,H,W] -> [B,T,C,H,W]\n", + " return gt.permute(1,0,2,3,4).contiguous()\n", + " else:\n", + " raise ValueError(f\"Can't infer B. ic B={B}, gt shape={gt.shape}\")\n", + "\n", + "def _sample_values(x, k):\n", + " x = x.reshape(-1)\n", + " n = x.numel()\n", + " if n <= k:\n", + " return x.detach().cpu()\n", + " idx = torch.randint(0, n, (k,), device=x.device)\n", + " return x[idx].detach().cpu()\n", + "\n", + "# parse constants from filenames\n", + "_NUM = r\"[0-9]*\\.?[0-9]+(?:e[+-]?[0-9]+)?\"\n", + "_re_re = re.compile(rf\"Reynolds_({_NUM})\")\n", + "_re_sc = re.compile(rf\"Schmidt_({_NUM})\")\n", + "\n", + "def _to_float(s: str) -> float:\n", + " return float(s)\n", + "\n", + "def constants_from_filenames(folders, exts=(\".hdf5\", \".h5\")):\n", + " \"\"\"Return sorted unique lists (Re_vals, Sc_vals) parsed from filenames in given folder(s).\"\"\"\n", + " if isinstance(folders, (str, os.PathLike)):\n", + " folders = [folders]\n", + " re_vals, sc_vals = set(), set()\n", + " for folder in folders:\n", + " if not os.path.isdir(folder):\n", + " continue\n", + " for name in os.listdir(folder):\n", + " if not name.endswith(exts):\n", + " continue\n", + " m_re = _re_re.search(name)\n", + " m_sc = _re_sc.search(name)\n", + " if m_re: re_vals.add(_to_float(m_re.group(1)))\n", + " if m_sc: sc_vals.add(_to_float(m_sc.group(1)))\n", + " return sorted(re_vals), sorted(sc_vals)\n", + "\n", + "@torch.no_grad()\n", + "def scan_minmax(\n", + " dataset,\n", + " *,\n", + " batches=20,\n", + " bs=70,\n", + " center_pressure=True,\n", + " pct=99.5,\n", + " const_start=4,\n", + " sample_per_map=2048,\n", + " max_total_samples=1_000_000,\n", + " parse_constants_from_folders=None, # <— pass your train/val folder(s) here\n", + "):\n", + " \"\"\"\n", + " Returns:\n", + " min_val_tensor, max_val_tensor (per-channel):\n", + " [tracer_min, pressure_min, -1, -1, const1_min, const2_min, ...]\n", + " \"\"\"\n", + " dl = DataLoader(\n", + " dataset, batch_size=bs, shuffle=True,\n", + " num_workers=0, pin_memory=False, collate_fn=custom_collate_fn\n", + " )\n", + "\n", + " tr_samples, pr_samples = [], []\n", + " const_vals = None \n", + "\n", + " seen = 0\n", + " for ic, t0, t1, gt in dl:\n", + " gt = _to_BTCHW(gt, ic) # [B,T,C,H,W]\n", + " B, T, C, H, W = gt.shape\n", + " ic2, gt2 = ic.clone(), gt.clone()\n", + "\n", + " if center_pressure and C >= 2:\n", + " ic2[:, 1] -= ic2[:, 1].mean(dim=(-2,-1), keepdim=True)\n", + " gt2[:, :, 1] -= gt2[:, :, 1].mean(dim=(-2,-1), keepdim=True)\n", + "\n", + " # sample tracer & pressure (IC + GT)\n", + " tr_samples.append(_sample_values(ic2[:, 0], sample_per_map))\n", + " pr_samples.append(_sample_values(ic2[:, 1], sample_per_map))\n", + " tr_samples.append(_sample_values(gt2[:, :, 0].reshape(B*T, H, W), sample_per_map))\n", + " pr_samples.append(_sample_values(gt2[:, :, 1].reshape(B*T, H, W), sample_per_map))\n", + "\n", + " # fallback constants from tensors if no parsing specified\n", + " if parse_constants_from_folders is None and C > const_start:\n", + " n_const = C - const_start\n", + " if const_vals is None:\n", + " const_vals = [[] for _ in range(n_const)]\n", + " for j in range(n_const):\n", + " const_vals[j].append(ic2[:, const_start + j, 0, 0].detach().cpu())\n", + "\n", + " seen += 1\n", + " if batches is not None and seen >= batches:\n", + " break\n", + "\n", + " # cap lists\n", + " def _cap_list(L):\n", + " total = sum(t.numel() for t in L)\n", + " if total > max_total_samples:\n", + " cat = torch.cat(L)\n", + " new_n = max_total_samples // 2\n", + " idx = torch.randint(0, cat.numel(), (new_n,))\n", + " L.clear(); L.append(cat[idx])\n", + " _cap_list(tr_samples); _cap_list(pr_samples)\n", + "\n", + " # percentiles\n", + " def _percentile(sample_list, q):\n", + " if not sample_list: return 0.0\n", + " x = torch.cat(sample_list).float()\n", + " return float(torch.quantile(x, torch.tensor(q, dtype=torch.float32)))\n", + "\n", + " low_q = (100.0 - pct) / 100.0\n", + " hi_q = pct / 100.0\n", + " tr_min = _percentile(tr_samples, low_q)\n", + " tr_max = _percentile(tr_samples, hi_q)\n", + " pr_min = _percentile(pr_samples, low_q)\n", + " pr_max = _percentile(pr_samples, hi_q)\n", + "\n", + " # velocities are unit-normalized in your dataset\n", + " vx_min, vx_max = -1.0, 1.0\n", + " vy_min, vy_max = -1.0, 1.0\n", + "\n", + " # search min max of reynolds and constants via file names\n", + " const_mins, const_maxs = [], []\n", + " if parse_constants_from_folders is not None:\n", + " re_list, sc_list = constants_from_filenames(parse_constants_from_folders)\n", + " if re_list:\n", + " eps = 1e-8\n", + " const_mins.append(min(re_list) - eps)\n", + " const_maxs.append(max(re_list) + eps)\n", + " if sc_list:\n", + " eps = 1e-8\n", + " const_mins.append(min(sc_list) - eps)\n", + " const_maxs.append(max(sc_list) + eps)\n", + " print(f\"[files] Reynolds from names: {re_list or '—'}\")\n", + " print(f\"[files] Schmidt from names: {sc_list or '—'}\")\n", + "\n", + " min_list = [tr_min, pr_min, vx_min, vy_min] + const_mins\n", + " max_list = [tr_max, pr_max, vx_max, vy_max] + const_maxs\n", + " min_val_tensor = torch.tensor(min_list, dtype=torch.float32)\n", + " max_val_tensor = torch.tensor(max_list, dtype=torch.float32)\n", + "\n", + " print(f\"[stream] tracer pct[{100-pct:.1f}%,{pct:.1f}%]: {tr_min:.4f}, {tr_max:.4f}\")\n", + " print(f\"[stream] pressure pct[{100-pct:.1f}%,{pct:.1f}%]: {pr_min:.4f}, {pr_max:.4f}\" +\n", + " (\" (centered)\" if center_pressure else \"\"))\n", + " print(f\"[stream] velocity: UNIT NORMALIZED → [-1.0, 1.0]\")\n", + " if const_mins:\n", + " print(f\"[const] mins: {const_mins}\")\n", + " print(f\"[const] maxs: {const_maxs}\")\n", + "\n", + " return min_val_tensor, max_val_tensor\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "945dc581-fd36-4903-ba01-2f166cb2caf5", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from torch.utils.data import DataLoader\n", + "import torch\n", + "import os\n", + "import h5py\n", + "from PARCtorch.data.welldataset_single_trajectory import WellDatasetInterface\n", + "from the_well.data.datasets import WellDataset\n", + "from torch.utils.data import DataLoader\n", + "from torchvision.transforms import Compose, Resize\n", + "import PARCtorch.data.dataset as ds_mod\n", + "import torch\n", + "import torch.nn as nn\n", + "train_dir = \"/home/wkt7ne/PARCtorch/PARCtorch/shear_flow/data/train\"\n", + "# Now import the utilities\n", + "from PARCtorch.data.dataset import (\n", + " custom_collate_fn,\n", + " InitialConditionDataset,\n", + " initial_condition_collate_fn,\n", + ")\n", + "from PARCtorch.utilities.viz import (\n", + " visualize_channels,\n", + " save_gifs_with_ground_truth,\n", + ")\n", + "\n", + "ds_mod.WellDatasetInterface.__del__ = lambda self: None\n", + "\n", + "# spatial transforms\n", + "orig_h, orig_w = 256, 512\n", + "spatial_percent = 1/3\n", + "new_h = int(orig_h * spatial_percent) # 85\n", + "new_w = int(orig_w * spatial_percent) # 170\n", + "\n", + "def divisible_by_8(x):\n", + " return (x//8)*8\n", + "new_h = divisible_by_8(new_h)\n", + "new_w = divisible_by_8(new_w)\n", + "\n", + "\n", + "patch_size = (new_h, new_w)\n", + "\n", + "# spatial transformation\n", + "train_spatial_transform = Compose([\n", + " Resize((new_h, new_w)),\n", + "])\n", + "gt_spatial_transform = Compose([\n", + " Resize((new_h, new_w)),\n", + "])\n", + "\n", + "# temporal skipping\n", + "temporal_skip = 50\n", + "desired_frames = 3\n", + "future_steps = 1\n", + "\n", + "batch_size = 60\n", + "loader = WellDatasetInterface(\n", + " well_dataset_args={\"path\": train_dir, \"exclude_filters\": [\"shear_flow_Reynolds_5e5_Schmidt_1e0.hdf5\"]},\n", + " future_steps=future_steps,\n", + " add_constant_scalars=True,\n", + " delta_t=2,\n", + " temporal_skip=temporal_skip,\n", + " spatial_transform=train_spatial_transform,\n", + " min_val=None, max_val=None,\n", + " single_trajectory=False,\n", + ")\n", + "\n", + "# calculate min max tensors\n", + "min_val_tensor, max_val_tensor = scan_minmax(\n", + " loader,\n", + " batches=60, \n", + " bs=64,\n", + " center_pressure=True, \n", + " pct=99.5,\n", + " const_start=4,\n", + " sample_per_map=4096,\n", + " max_total_samples=2_000_000,\n", + " parse_constants_from_folders=[train_dir],\n", + ")\n", + "\n", + "\n", + "print(\"min_val_tensor:\", min_val_tensor.tolist())\n", + "print(\"max_val_tensor:\", max_val_tensor.tolist())\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9bbfa746-bcab-4afe-9c1d-6f69b0bff76d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "min: [-0.5, -0.4064001441001892, -1.0, -1.0, 10000.0, 0.19999998807907104]\n", + "max: [0.5, 0.14997883141040802, 1.0, 1.0, 500000.0, 5.0]\n" + ] + } + ], + "source": [ + "import torch\n", + "from pathlib import Path\n", + "\n", + "def load_minmax(cache_path, expected_len=None, dtype=torch.float32):\n", + " \"\"\"\n", + " Load min/max for use INSIDE a Dataset/DataLoader worker.\n", + " Always returns CPU tensors to avoid CUDA init in workers.\n", + " \"\"\"\n", + " ckpt = torch.load(Path(cache_path), map_location=\"cpu\")\n", + "\n", + " # Extract\n", + " if isinstance(ckpt, dict):\n", + " min_t = ckpt.get(\"min\", ckpt.get(\"min_val_tensor\"))\n", + " max_t = ckpt.get(\"max\", ckpt.get(\"max_val_tensor\"))\n", + " elif isinstance(ckpt, (list, tuple)) and len(ckpt) >= 2:\n", + " min_t, max_t = ckpt[0], ckpt[1]\n", + " else:\n", + " raise ValueError(f\"Unsupported cache format in {cache_path}\")\n", + "\n", + " if min_t is None or max_t is None:\n", + " keys = list(ckpt.keys()) if isinstance(ckpt, dict) else []\n", + " raise ValueError(f\"Could not find min/max in {cache_path}. Keys: {keys}\")\n", + "\n", + " # Ensure CPU tensors\n", + " min_t = torch.as_tensor(min_t, dtype=dtype).contiguous().cpu()\n", + " max_t = torch.as_tensor(max_t, dtype=dtype).contiguous().cpu()\n", + "\n", + " if expected_len is not None and (min_t.numel() != expected_len or max_t.numel() != expected_len):\n", + " print(f\"Warning: expected {expected_len} channels, got {min_t.numel()}/{max_t.numel()}\")\n", + "\n", + " return min_t, max_t\n", + "\n", + "\n", + "# Usage\n", + "cache_file = \"/home/wkt7ne/PARCtorch/PARCtorch/shear_flow/data/minmax_cache.pt\"\n", + "min_val_tensor, max_val_tensor = load_minmax(cache_file, expected_len=6)\n", + "print(\"min:\", min_val_tensor.tolist())\n", + "print(\"max:\", max_val_tensor.tolist())\n" + ] + }, + { + "cell_type": "markdown", + "id": "d5889e9a-cf64-4fd0-94b8-dec8129892b6", + "metadata": {}, + "source": [ + "# Load Training Data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "eaeafdf4-0c06-4db6-8698-eca8106be98f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "import h5py\n", + "from PARCtorch.data.welldataset_single_trajectory import WellDatasetInterface\n", + "from the_well.data.datasets import WellDataset\n", + "from torch.utils.data import DataLoader\n", + "from torchvision.transforms import Compose, Resize\n", + "import PARCtorch.data.dataset as ds_mod\n", + "import torch\n", + "import torch.nn as nn\n", + "train_dir = \"/home/wkt7ne/PARCtorch/PARCtorch/shear_flow/data/train\"\n", + "# Now import the utilities\n", + "from PARCtorch.data.dataset import (\n", + " custom_collate_fn,\n", + " InitialConditionDataset,\n", + " initial_condition_collate_fn,\n", + ")\n", + "from PARCtorch.utilities.viz import (\n", + " visualize_channels,\n", + " save_gifs_with_ground_truth,\n", + ")\n", + "\n", + "ds_mod.WellDatasetInterface.__del__ = lambda self: None\n", + "\n", + "# spatial transforms\n", + "orig_h, orig_w = 256, 512\n", + "spatial_percent = 1/3\n", + "new_h = int(orig_h * spatial_percent) # 85\n", + "new_w = int(orig_w * spatial_percent) # 170\n", + "\n", + "def divisible_by_8(x):\n", + " return (x//8)*8\n", + "new_h = divisible_by_8(new_h)\n", + "new_w = divisible_by_8(new_w)\n", + "\n", + "\n", + "patch_size = (new_h, new_w)\n", + "\n", + "# spatial transformation\n", + "train_spatial_transform = Compose([\n", + " Resize((new_h, new_w)),\n", + "])\n", + "gt_spatial_transform = Compose([\n", + " Resize((new_h, new_w)),\n", + "])\n", + "\n", + "# temporal skipping\n", + "temporal_skip = 50\n", + "desired_frames = 3\n", + "future_steps = 1\n", + "\n", + "batch_size = 10 # increase it to see at which point it will reduce the training time \n", + "\n", + "\n", + "# Remove corrupted files\n", + "corrupted = []\n", + "if os.path.isdir(train_dir):\n", + " for fname in os.listdir(train_dir):\n", + " if not fname.endswith(\".hdf5\"):\n", + " continue\n", + " fpath = os.path.join(train_dir, fname)\n", + " try:\n", + " with h5py.File(fpath, \"r\"):\n", + " pass\n", + " except OSError:\n", + " corrupted.append(fname)\n", + " os.remove(fpath)\n", + "\n", + "\n", + "# Initialize dataset\n", + "ds = WellDatasetInterface(\n", + " well_dataset_args={\n", + " \"path\": train_dir,\n", + " \"exclude_filters\": [\"shear_flow_Reynolds_5e5_Schmidt_1e0.hdf5\"]\n", + " },\n", + " future_steps=future_steps,\n", + " add_constant_scalars = True,\n", + " delta_t = 2,\n", + " temporal_skip = temporal_skip,\n", + " spatial_transform = train_spatial_transform,\n", + " min_val= min_val_tensor,\n", + " max_val= max_val_tensor,\n", + " single_trajectory=True,\n", + " traj_idx=0,\n", + ")\n", + "\n", + "ic, t0, t1, gt = ds[0]\n", + "\n", + "\n", + "# Create DataLoader for training dataset\n", + "train_loader = DataLoader(\n", + " ds,\n", + " batch_size=batch_size,\n", + " shuffle=True,\n", + " num_workers=1,\n", + " collate_fn=custom_collate_fn,\n", + " pin_memory=False,\n", + ")\n", + "\n", + "val_dir = \"/home/wkt7ne/PARCtorch/PARCtorch/shear_flow/data/experimental_validation\"\n", + "val_dataset = WellDatasetInterface(\n", + " well_dataset_args={\"path\": val_dir},\n", + " future_steps=future_steps,\n", + " delta_t=2.0,\n", + " add_constant_scalars=True,\n", + " temporal_skip=temporal_skip,\n", + " spatial_transform=gt_spatial_transform,\n", + " min_val=min_val_tensor,\n", + " max_val=max_val_tensor,\n", + " single_trajectory=True,\n", + " traj_idx=0,\n", + ")\n", + "val_loader = DataLoader(\n", + " val_dataset,\n", + " batch_size=batch_size,\n", + " shuffle=False,\n", + " num_workers=1,\n", + " pin_memory=True,\n", + " collate_fn=custom_collate_fn,\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "14636f18-fc02-4c07-a76e-1b655eaf4965", + "metadata": {}, + "source": [ + "# Sanity check on normalization" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "87775147-d315-4945-a35a-895cb66c5ada", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "xb shape: torch.Size([10, 6, 80, 168]) yb shape: torch.Size([1, 10, 6, 80, 168])\n", + "IC tracer/pressure ~[0,1]: [-1.0000, 1.0000]\n", + "IC vx ~[-1,1]: [0.0000, 0.1837]\n", + "IC vy ~[-1,1]: [0.0000, 1.0000]\n", + "GT tracer/pressure ~[0,1] over T=1: [-1.0000, 1.0000]\n", + "GT vx ~[-1,1]: [0.0000, 0.1837]\n", + "GT vy ~[-1,1]: [0.0000, 1.0000]\n", + "IC constants (e.g., Re/Sc/constant_fields) ~[0,1]: [-0.3994, 1.0000]\n" + ] + } + ], + "source": [ + "# Grab one batch from your DataLoader\n", + "xb, t0, t1, yb = next(iter(train_loader)) # xb: [B, C, H, W], yb: [T, B, C, H, W]\n", + "\n", + "B, C, H, W = xb.shape\n", + "print(\"xb shape:\", xb.shape, \"yb shape:\", yb.shape)\n", + "\n", + "if C < 4:\n", + " raise RuntimeError(f\"Expected at least 4 field channels (tracer, pressure, vx, vy); got C={C}\")\n", + "\n", + "# In your pipeline, the last 4 channels are the field group: [tracer, pressure, vx, vy]\n", + "tr_pr_slice = slice(C - 4, C - 2) # tracer, pressure\n", + "vx_idx = C - 2\n", + "vy_idx = C - 1\n", + "\n", + "def minmax(t):\n", + " t = t.detach()\n", + " return float(t.min().cpu()), float(t.max().cpu())\n", + "\n", + "# --- Inputs (ic) ---\n", + "tr_pr_min, tr_pr_max = minmax(xb[:, tr_pr_slice])\n", + "vx_min, vx_max = minmax(xb[:, vx_idx])\n", + "vy_min, vy_max = minmax(xb[:, vy_idx])\n", + "print(f\"IC tracer/pressure ~[0,1]: [{tr_pr_min:.4f}, {tr_pr_max:.4f}]\")\n", + "print(f\"IC vx ~[-1,1]: [{vx_min:.4f}, {vx_max:.4f}]\")\n", + "print(f\"IC vy ~[-1,1]: [{vy_min:.4f}, {vy_max:.4f}]\")\n", + "\n", + "# --- Targets (gt) ---\n", + "T = yb.shape[0]\n", + "tr_pr_min_t, tr_pr_max_t = minmax(yb[:, :, tr_pr_slice])\n", + "vx_min_t, vx_max_t = minmax(yb[:, :, vx_idx])\n", + "vy_min_t, vy_max_t = minmax(yb[:, :, vy_idx])\n", + "print(f\"GT tracer/pressure ~[0,1] over T={T}: [{tr_pr_min_t:.4f}, {tr_pr_max_t:.4f}]\")\n", + "print(f\"GT vx ~[-1,1]: [{vx_min_t:.4f}, {vx_max_t:.4f}]\")\n", + "print(f\"GT vy ~[-1,1]: [{vy_min_t:.4f}, {vy_max_t:.4f}]\")\n", + "\n", + "# --- Optional: constants (everything before last 4 channels) ---\n", + "if C > 4:\n", + " const_min, const_max = minmax(xb[:, :C-4])\n", + " print(f\"IC constants (e.g., Re/Sc/constant_fields) ~[0,1]: [{const_min:.4f}, {const_max:.4f}]\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "eb47eb1f-de1a-4a69-9d06-3f66596dce4c", + "metadata": {}, + "source": [ + "# Visualizing the Data - Sanity Check" + ] + }, + { + "cell_type": "markdown", + "id": "6f533d25-b23e-45c4-a999-6752017e77b1", + "metadata": { + "tags": [] + }, + "source": [ + "This step is a sanity-check on one of the batches to ensure that our previous two steps - **normalization** and **data loading** were carried out correctly. \n", + "\n", + "To do so, we will be plotting out the:\n", + "- **Initial Condition** `ic`: The initial physical state of the system at time $t_0$ - the input to the model\n", + "- **Time Stamp** `t0`: The time stamp corresponding to the initial condition\n", + "- **Target Timestamp** `t1`: The target time stamp that we are trying to predict with PARC\n", + "- **Ground Truth** `target`: The ground truth data at time $t_1$, representing what the model is suppoesd to output at the predicted outcome at the future time stamp\n", + "- `channel_names`: labels for each channel, i.e. `Reynolds,Schmidt, tracer, pressure, velocity_U, velocity_V `\n", + "- `channel_cmaps`: list of colormaps for each channel" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "231535c5-f1c3-41eb-8b1a-8c0add2241c4", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Channel Data Statistics:\n", + "Channel 0: IC min=0.15200187265872955, IC max=0.9207032918930054\n", + " Step 1: min=0.1520480513572693, max=0.9204034209251404\n", + "Channel 1: IC min=0.6897211670875549, IC max=0.7649035453796387\n", + " Step 1: min=0.691247284412384, max=0.7637747526168823\n", + "Channel 2: IC min=-1.0, IC max=1.0\n", + " Step 1: min=-1.0, max=1.0\n", + "Channel 3: IC min=-0.9999988675117493, IC max=0.9999984502792358\n", + " Step 1: min=-0.9999969005584717, max=0.9999999403953552\n", + "Channel 4: IC min=0.0, IC max=0.0\n", + " Step 1: min=0.0, max=0.0\n", + "Channel 5: IC min=0.062499985098838806, IC max=0.0625000149011612\n", + " Step 1: min=0.062499985098838806, max=0.0625000149011612\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Fetch a batch and visualize\n", + "for batch in train_loader:\n", + " ic, t0, t1, target = batch\n", + " \n", + " num_channels = ic.shape[1]\n", + " \n", + " channel_names = [ \n", + " \"Tracer\",\n", + " \"Pressure\",\n", + " \"Velocity X\",\n", + " \"Velocity Y\",\n", + " \"Reynolds\",\n", + " \"Schmidt\",\n", + " ]\n", + " assert len(channel_names) == num_channels, \\\n", + " f\"Expected {num_channels} channel names, got {len(channel_names)}\"\n", + " \n", + " custom_cmaps = [\"RdBu_r\"] * num_channels\n", + "\n", + " visualize_channels(\n", + " ic,\n", + " t0,\n", + " t1,\n", + " target,\n", + " channel_names=channel_names,\n", + " channel_cmaps=custom_cmaps,\n", + " )\n", + " break # Visualize one batch for now" + ] + }, + { + "cell_type": "markdown", + "id": "5989fbbe-7ca7-4820-8916-b3c21844f74a", + "metadata": {}, + "source": [ + "# Building the PARC Model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2c8c24a3-6412-46a4-b58b-872de72332bf", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from PARCtorch.PARCv2 import PARCv2\n", + "from PARCtorch.differentiator.differentiator import ADRDifferentiator\n", + "from PARCtorch.differentiator.finitedifference import FiniteDifference\n", + "from PARCtorch.integrator.integrator import Integrator\n", + "#from PARCtorch.integrator.heun import Heun\n", + "from PARCtorch.integrator.rk4 import RK4 \n", + "from PARCtorch.integrator.euler import Euler\n", + "from PARCtorch.utilities.unet import UNet\n", + "\n", + "from torch.optim import Adam\n", + "\n", + "# Defining 2D Imcompressible Shear Flow\n", + "PARCv2.check = lambda self: True \n", + "PARCv2.check_msg = lambda self, chk: None \n", + "\n", + "n_fe_features = 64\n", + "\n", + "unet_sf = UNet(\n", + " block_dimensions = [64, 64* 2, 64 * 4], \n", + " input_channels = 6, \n", + " output_channels = n_fe_features,\n", + " kernel_size = 3,\n", + " padding_mode = \"circular\",\n", + " up_block_use_concat=[False, True], \n", + " skip_connection_indices=[0], \n", + ")\n", + "\n", + "right_diff = FiniteDifference(padding_mode=\"circular\").cuda() # Use replication padding to handle boundary conditions\n", + "\n", + "base_diff = ADRDifferentiator(\n", + " n_state_var = 2, \n", + " n_const_var = 2,\n", + " n_fe_features = n_fe_features, \n", + " list_adv_idx=[0, 1, 2, 3], \n", + " list_dif_idx=[0, 1, 2, 3], \n", + " feature_extraction = unet_sf, \n", + " padding_mode = \"circular\", \n", + " finite_difference_method = right_diff, \n", + " spade_random_noise = False \n", + ").cuda()\n", + "\n", + "sf_diff = base_diff\n", + "\n", + "rk4 = RK4().cuda() # RK4\n", + "euler = Euler().cuda() # euler\n", + "\n", + "# sf_int = Integrator(\n", + "# clip = False, \n", + "# list_poi_idx=[], \n", + "# num_int = euler, \n", + "# list_dd_int = [None, None, None, None, None, None], \n", + "# padding_mode = \"circular\", \n", + "# finite_difference_method = right_diff, \n", + "# poi_kernel_size = 3, \n", + "# n_poi_features = 64\n", + "# ).cuda()\n", + "\n", + "sf_int = Integrator(\n", + " clip=False,\n", + " list_poi_idx=[], \n", + " num_int=rk4,\n", + " list_dd_int=[None, None, None], \n", + " padding_mode=\"circular\",\n", + " finite_difference_method=right_diff,\n", + " poi_kernel_size=3,\n", + " n_poi_features=64,\n", + " velocity_idx=(2, 3),\n", + " const_start=4\n", + ").cuda()\n", + "\n", + "# Define the loss function (L1 Loss is typically used for regression tasks)\n", + "criterion = torch.nn.L1Loss().cuda()\n", + "\n", + "# Initialize the PARCv2 model with the differentiator, integrator, and loss function\n", + "model = PARCv2(\n", + " sf_diff, \n", + " sf_int, \n", + " criterion\n", + ").cuda()\n", + "\n", + "# Set up the optimizer (Adam is a popular choice for adaptive learning rate optimization)\n", + "optimizer = Adam(\n", + " model.parameters(), \n", + " lr=1e-4\n", + ") " + ] + }, + { + "cell_type": "markdown", + "id": "251a8cbf-c4bf-4c35-b642-8f7658e0eab8", + "metadata": {}, + "source": [ + "# Training the Model" + ] + }, + { + "cell_type": "markdown", + "id": "7a93b1c0-2e22-427c-ab2b-6012ded84f09", + "metadata": {}, + "source": [ + "## First round of training" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9a7386b6-619f-477d-8197-48b987ec7502", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from train import train_model\n", + "from pathlib import Path\n", + "import pickle\n", + "import gc, torch, time\n", + "\n", + "gc.collect()\n", + "torch.cuda.empty_cache()\n", + "\n", + "save_dirs = Path(\"/home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights\")\n", + "save_dirs.mkdir(exist_ok=True, parents=True)\n", + "\n", + "cumulative_path = save_dirs / \"training_losses_cumulative.pkl\"\n", + "cumulative_losses = []\n", + "\n", + "num_epochs = 1000\n", + "global_start = time.time()\n", + "\n", + "best_val_loss = float(\"inf\")\n", + "best_epoch = None\n", + "best_checkpoint_path = save_dirs / \"best_validation.pt\"\n", + "raw_best_path = save_dirs / \"model.pth\"\n", + "\n", + "for epoch in range(1, num_epochs + 1):\n", + " epoch_start = time.time()\n", + "\n", + " # training one epoch\n", + " train_start = time.time()\n", + " train_model(\n", + " model,\n", + " train_loader,\n", + " criterion,\n", + " optimizer,\n", + " num_epochs=1,\n", + " save_dir=save_dirs,\n", + " app='ns',\n", + " )\n", + " train_time = time.time() - train_start\n", + "\n", + " # Load the epochs' training losses\n", + " loss_path = save_dirs / \"training_losses.pkl\"\n", + " with open(loss_path, 'rb') as f:\n", + " epoch_losses = pickle.load(f)\n", + "\n", + " cumulative_losses.extend(epoch_losses)\n", + " with open(cumulative_path, 'wb') as f:\n", + " pickle.dump(cumulative_losses, f)\n", + "\n", + " latest_loss = epoch_losses[-1] if epoch_losses else float(\"nan\") # training loss\n", + "\n", + " # validate the losses via the validation loader\n", + " val_start = time.time()\n", + " model.eval()\n", + " val_losses = []\n", + " val_batch_times = []\n", + " with torch.no_grad():\n", + " for ic, t0, t1, gt in val_loader:\n", + " batch_start = time.time()\n", + " ic, t0, t1, gt = ic.cuda(), t0.cuda(), t1.cuda(), gt.cuda()\n", + " pred = model(ic, t0.squeeze(), t1)\n", + " val_losses.append(criterion(pred, gt).item())\n", + " val_batch_times.append(time.time() - batch_start)\n", + " val_time = time.time() - val_start\n", + " if val_losses:\n", + " val_loss = float(sum(val_losses) / len(val_losses))\n", + " else:\n", + " val_loss = float(\"nan\")\n", + " avg_val_batch_time = val_time / len(val_batch_times) if val_batch_times else 0.0\n", + "\n", + " # Timing and ETA\n", + " epoch_time = time.time() - epoch_start\n", + " elapsed = time.time() - global_start\n", + " eta_secs = (elapsed / epoch) * (num_epochs - epoch)\n", + "\n", + "\n", + " print(\n", + " f\"Epoch {epoch:4d}/{num_epochs} \"\n", + " f\"TrainLoss {latest_loss:.6f} ValLoss {val_loss:.6f} \"\n", + " f\"TrainTime {train_time:.2f}s ValTime {val_time:.2f}s \"\n", + " f\"(avg val batch {avg_val_batch_time:.3f}s) \"\n", + " f\"⏱ Epoch {epoch_time:.2f}s ETA {eta_secs:.2f}s\",\n", + " flush=True\n", + " )\n", + "\n", + " # saving regular checkpoints\n", + " ckpt_dict = {\n", + " 'epoch': epoch,\n", + " 'model_state_dict': model.state_dict(),\n", + " 'optimizer_state_dict': optimizer.state_dict(),\n", + " 'train_loss': latest_loss,\n", + " 'val_loss': val_loss,\n", + " }\n", + " \n", + " latest_checkpoint_path = save_dirs / \"latest.pt\"\n", + " torch.save(ckpt_dict, latest_checkpoint_path)\n", + " \n", + " if epoch % 2 == 0:\n", + " checkpoint_path = save_dirs / f\"checkpoint_epoch_{epoch:04d}.pt\"\n", + " torch.save(ckpt_dict, checkpoint_path)\n", + " print(f\"saved checkpoint: {checkpoint_path} (train_loss={latest_loss:.6f}, val_loss={val_loss:.6f})\", flush=True)\n", + " else:\n", + " print(f\"updated latest checkpoint (epoch {epoch})\", flush=True)\n", + " \n", + " # save best validation loss\n", + " if val_loss < best_val_loss:\n", + " best_val_loss = val_loss\n", + " best_epoch = epoch\n", + " torch.save({\n", + " 'epoch': epoch,\n", + " 'model_state_dict': model.state_dict(),\n", + " 'optimizer_state_dict': optimizer.state_dict(),\n", + " 'train_loss': latest_loss,\n", + " 'val_loss': val_loss,\n", + " }, best_checkpoint_path)\n", + " print(f\"saved new best model (val_loss={val_loss:.6f}) to {best_checkpoint_path}\", flush=True)\n", + " \n", + " torch.save(model.state_dict(), raw_best_path)\n", + " \n", + " gc.collect()\n", + " torch.cuda.empty_cache()\n", + "\n", + "print(f\"\\n saved cumulative losses to: {cumulative_path}\")\n", + "print(f\"Best validation epoch: {best_epoch} with val_loss={best_val_loss:.6f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "68ab53d4-84de-4550-b83d-9b776f888017", + "metadata": {}, + "source": [ + "## Second Round of training (resuming off the last one)" + ] + }, + { + "cell_type": "markdown", + "id": "560bc23c-4b5e-4dfc-9678-a0e76817b1ba", + "metadata": {}, + "source": [ + "Assumes `model`, `train_loader`, `val_loader`, `criterion`, and `optimizer` are already instantiated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ce6bef1-cc5a-4439-8886-ae7da5e97d96", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[resume] loaded checkpoint checkpoint_epoch_0340.pt (20415435 bytes): resuming from epoch 341, best_val_loss=0.057239, best_epoch=340, total_elapsed=0.0s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:34<00:00, 1.55it/s, Batch Loss=0.000517]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0010\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 341/1000 TrainLoss 0.000964 ValLoss 0.061729 TrainTime 154.71s ValTime 16.29s (avg val batch 0.407s) ⏱ Epoch 171.01s ETA 330.49s TotalElapsed 171.0s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0341.pt (train_loss=0.000964, val_loss=0.061729)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:34<00:00, 1.55it/s, Batch Loss=0.000606]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0010\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 342/1000 TrainLoss 0.000966 ValLoss 0.061260 TrainTime 154.74s ValTime 16.29s (avg val batch 0.407s) ⏱ Epoch 171.04s ETA 658.09s TotalElapsed 342.0s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0342.pt (train_loss=0.000966, val_loss=0.061260)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:35<00:00, 1.54it/s, Batch Loss=0.00106] " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0010\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 343/1000 TrainLoss 0.000963 ValLoss 0.062560 TrainTime 155.43s ValTime 10.24s (avg val batch 0.256s) ⏱ Epoch 165.67s ETA 972.51s TotalElapsed 507.7s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0343.pt (train_loss=0.000963, val_loss=0.062560)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:32<00:00, 1.56it/s, Batch Loss=0.000729]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0010\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 344/1000 TrainLoss 0.000951 ValLoss 0.062050 TrainTime 152.87s ValTime 16.06s (avg val batch 0.401s) ⏱ Epoch 168.94s ETA 1290.36s TotalElapsed 676.7s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0344.pt (train_loss=0.000951, val_loss=0.062050)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:36<00:00, 1.53it/s, Batch Loss=0.000951]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0009\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 345/1000 TrainLoss 0.000935 ValLoss 0.062558 TrainTime 156.70s ValTime 15.83s (avg val batch 0.396s) ⏱ Epoch 172.55s ETA 1612.27s TotalElapsed 849.2s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0345.pt (train_loss=0.000935, val_loss=0.062558)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:32<00:00, 1.57it/s, Batch Loss=0.000566]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0009\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 346/1000 TrainLoss 0.000934 ValLoss 0.061868 TrainTime 152.33s ValTime 10.36s (avg val batch 0.259s) ⏱ Epoch 162.69s ETA 1912.66s TotalElapsed 1011.9s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0346.pt (train_loss=0.000934, val_loss=0.061868)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:42<00:00, 1.48it/s, Batch Loss=0.000811]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0009\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 347/1000 TrainLoss 0.000938 ValLoss 0.062904 TrainTime 162.13s ValTime 10.28s (avg val batch 0.257s) ⏱ Epoch 172.41s ETA 2228.69s TotalElapsed 1184.3s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0347.pt (train_loss=0.000938, val_loss=0.062904)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:26<00:00, 1.63it/s, Batch Loss=0.000571]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0009\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 348/1000 TrainLoss 0.000927 ValLoss 0.061032 TrainTime 146.96s ValTime 10.18s (avg val batch 0.255s) ⏱ Epoch 157.14s ETA 2513.30s TotalElapsed 1341.5s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0348.pt (train_loss=0.000927, val_loss=0.061032)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:27<00:00, 1.62it/s, Batch Loss=0.000766]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0009\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at 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/home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 368/1000 TrainLoss 0.000903 ValLoss 0.060326 TrainTime 146.65s ValTime 10.03s (avg val batch 0.251s) ⏱ Epoch 156.68s ETA 7845.69s TotalElapsed 4568.4s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0368.pt (train_loss=0.000903, val_loss=0.060326)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:26<00:00, 1.64it/s, Batch Loss=0.000491]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0009\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 369/1000 TrainLoss 0.000899 ValLoss 0.060487 TrainTime 146.08s ValTime 9.98s (avg val batch 0.250s) ⏱ Epoch 156.06s ETA 8078.91s TotalElapsed 4724.4s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0369.pt (train_loss=0.000899, val_loss=0.060487)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:29<00:00, 1.60it/s, Batch Loss=0.000959]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0009\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 370/1000 TrainLoss 0.000920 ValLoss 0.062260 TrainTime 149.42s ValTime 15.82s (avg val batch 0.396s) ⏱ Epoch 165.27s ETA 8325.71s TotalElapsed 4889.7s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0370.pt (train_loss=0.000920, val_loss=0.062260)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 100%|██████████| 239/239 [02:32<00:00, 1.57it/s, Batch Loss=0.00112] " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/1], Average Loss: 0.0009\n", + "Model weights saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/model.pth\n", + "Training losses saved at /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/training_losses.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 371/1000 TrainLoss 0.000910 ValLoss 0.059690 TrainTime 152.24s ValTime 10.07s (avg val batch 0.252s) ⏱ Epoch 162.32s ETA 8565.28s TotalElapsed 5052.0s\n", + "saved checkpoint: /home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights/checkpoint_epoch_0371.pt (train_loss=0.000910, val_loss=0.059690)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/1: 81%|████████ | 193/239 [01:58<00:27, 1.66it/s, Batch Loss=0.00127] " + ] + } + ], + "source": [ + "from train import train_model\n", + "from pathlib import Path\n", + "import pickle\n", + "import gc, torch, time, signal, os, tempfile \n", + "\n", + "save_dirs = Path(\"/home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_RK4_debug_model_weights\")\n", + "save_dirs.mkdir(exist_ok=True, parents=True)\n", + "\n", + "cumulative_path = save_dirs / \"training_losses_cumulative.pkl\"\n", + "raw_best_path = save_dirs / \"model.pth\" # synced raw best weights\n", + "best_checkpoint_path = save_dirs / \"best_validation.pt\"\n", + "\n", + "num_epochs = 1000 # total target epochs\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "model = model.to(device)\n", + "\n", + "def atomic_torch_save(obj, path: Path):\n", + " \"\"\"Write checkpoint atomically to avoid partial files.\"\"\"\n", + " path = Path(path)\n", + " with tempfile.NamedTemporaryFile(dir=path.parent, delete=False) as tmp:\n", + " tmp_path = Path(tmp.name)\n", + " torch.save(obj, tmp_path)\n", + " os.replace(tmp_path, path) # atomic on POSIX\n", + "\n", + "def try_load_ckpt(path: Path, map_location):\n", + " \"\"\"Reject obviously partial files; try torch.load with a clear error.\"\"\"\n", + " if not path.is_file():\n", + " raise FileNotFoundError(path)\n", + " if path.stat().st_size < (1 << 20): # if file is < 1MB, it is very likely partial/corrupt\n", + " raise RuntimeError(f\"too small ({path.stat().st_size} bytes)\")\n", + " return torch.load(path, map_location=map_location)\n", + "\n", + "# resume state holders\n", + "best_val_loss = float(\"inf\")\n", + "best_epoch = None\n", + "total_elapsed = 0.0 # accumulated over all previous epochs (if resumed)\n", + "start_epoch = 1\n", + "cumulative_losses = []\n", + "\n", + "# load existing cumulative losses if any \n", + "if cumulative_path.exists():\n", + " try:\n", + " with open(cumulative_path, \"rb\") as f:\n", + " cumulative_losses = pickle.load(f)\n", + " except Exception:\n", + " cumulative_losses = []\n", + "\n", + "# load best validation loss\n", + "if best_checkpoint_path.exists():\n", + " try:\n", + " ckpt = torch.load(best_checkpoint_path, map_location=device)\n", + " if isinstance(ckpt, dict):\n", + " best_val_loss = ckpt.get(\"val_loss\", best_val_loss)\n", + " best_epoch = ckpt.get(\"epoch\", best_epoch)\n", + " except Exception as e:\n", + " print(f\"[resume] warning: cannot read best_validation.pt: {e}\")\n", + "\n", + "# find latest checkpoint in storage to resume\n", + "ckpt_files = sorted(\n", + " [p for p in save_dirs.glob(\"checkpoint_epoch_*.pt\") if p.is_file()],\n", + " key=lambda p: p.stat().st_mtime, reverse=True\n", + ")\n", + "for p in ckpt_files:\n", + " try:\n", + " ckpt = try_load_ckpt(p, map_location=device)\n", + " if \"model_state_dict\" in ckpt:\n", + " model.load_state_dict(ckpt[\"model_state_dict\"])\n", + " else:\n", + " model.load_state_dict(ckpt) # fallback raw state_dict\n", + " if \"optimizer_state_dict\" in ckpt:\n", + " optimizer.load_state_dict(ckpt[\"optimizer_state_dict\"])\n", + " # restore best validation if present\n", + " best_val_loss = ckpt.get(\"val_loss\", best_val_loss)\n", + " best_epoch = ckpt.get(\"epoch\", best_epoch)\n", + " total_elapsed = ckpt.get(\"total_elapsed\", 0.0)\n", + " start_epoch = ckpt.get(\"epoch\", 0) + 1\n", + " print(f\"[resume] loaded checkpoint {p.name} ({p.stat().st_size} bytes): \"\n", + " f\"resuming from epoch {start_epoch}, best_val_loss={best_val_loss:.6f}, \"\n", + " f\"best_epoch={best_epoch}, total_elapsed={total_elapsed:.1f}s\")\n", + " break\n", + " except Exception as e:\n", + " print(f\"[resume] skipping {p.name}: {e}\")\n", + "else:\n", + " ckpt = None \n", + "\n", + "# shut down \n", + "state = {\"terminate\": False}\n", + "def handle_sigterm(signum, frame):\n", + " print(\"Received SIGTERM; will finish current epoch and exit cleanly.\", flush=True)\n", + " state[\"terminate\"] = True\n", + "signal.signal(signal.SIGTERM, handle_sigterm)\n", + "\n", + "# training loop \n", + "for epoch in range(start_epoch, num_epochs + 1):\n", + " epoch_start = time.time()\n", + "\n", + " model.train()\n", + " train_start = time.time()\n", + " train_model(\n", + " model,\n", + " train_loader,\n", + " criterion,\n", + " optimizer,\n", + " num_epochs=1,\n", + " save_dir=save_dirs,\n", + " app='ns',\n", + " )\n", + " train_time = time.time() - train_start\n", + "\n", + " # load the epoch's training loss\n", + " try:\n", + " loss_path = save_dirs / \"training_losses.pkl\"\n", + " with open(loss_path, \"rb\") as f:\n", + " epoch_losses = pickle.load(f)\n", + " except Exception:\n", + " epoch_losses = []\n", + "\n", + " cumulative_losses.extend(epoch_losses)\n", + " with open(cumulative_path, \"wb\") as f:\n", + " pickle.dump(cumulative_losses, f)\n", + "\n", + " latest_loss = epoch_losses[-1] if epoch_losses else float(\"nan\")\n", + "\n", + " # compute validation loss\n", + " val_start = time.time()\n", + " model.eval()\n", + " val_losses = []\n", + " val_batch_times = []\n", + " with torch.no_grad():\n", + " for ic, t0, t1, gt in val_loader:\n", + " batch_start = time.time()\n", + " ic, t0, t1, gt = ic.to(device), t0.to(device), t1.to(device), gt.to(device) # MOD: .to(device)\n", + " pred = model(ic, t0.squeeze(), t1) # assumes PARC expects t0 scalar, t1 vector\n", + " val_losses.append(criterion(pred, gt).item())\n", + " val_batch_times.append(time.time() - batch_start)\n", + " val_time = time.time() - val_start\n", + " val_loss = float(sum(val_losses) / len(val_losses)) if val_losses else float(\"nan\")\n", + " avg_val_batch_time = val_time / len(val_batch_times) if val_batch_times else 0.0\n", + "\n", + " # update ETA of model finishing training\n", + " epoch_time = time.time() - epoch_start\n", + " total_elapsed += epoch_time\n", + " epochs_done = epoch\n", + " eta_secs = (total_elapsed / epochs_done) * (num_epochs - epochs_done)\n", + "\n", + " print(\n", + " f\"Epoch {epoch:4d}/{num_epochs} \"\n", + " f\"TrainLoss {latest_loss:.6f} ValLoss {val_loss:.6f} \"\n", + " f\"TrainTime {train_time:.2f}s ValTime {val_time:.2f}s \"\n", + " f\"(avg val batch {avg_val_batch_time:.3f}s) \"\n", + " f\"⏱ Epoch {epoch_time:.2f}s ETA {eta_secs:.2f}s TotalElapsed {total_elapsed:.1f}s\",\n", + " flush=True\n", + " )\n", + "\n", + " # Save regular checkpoints\n", + " checkpoint_path = save_dirs / f\"checkpoint_epoch_{epoch:04d}.pt\"\n", + " atomic_torch_save({\n", + " 'epoch': epoch,\n", + " 'model_state_dict': model.state_dict(),\n", + " 'optimizer_state_dict': optimizer.state_dict(),\n", + " 'train_loss': latest_loss,\n", + " 'val_loss': val_loss,\n", + " 'total_elapsed': total_elapsed,\n", + " }, checkpoint_path)\n", + " print(f\"saved checkpoint: {checkpoint_path} (train_loss={latest_loss:.6f}, val_loss={val_loss:.6f})\", flush=True)\n", + "\n", + " # loading model with best validation\n", + " if val_loss < best_val_loss:\n", + " best_val_loss = val_loss\n", + " best_epoch = epoch\n", + " atomic_torch_save({\n", + " 'epoch': epoch,\n", + " 'model_state_dict': model.state_dict(),\n", + " 'optimizer_state_dict': optimizer.state_dict(),\n", + " 'train_loss': latest_loss,\n", + " 'val_loss': val_loss,\n", + " 'total_elapsed': total_elapsed,\n", + " }, best_checkpoint_path)\n", + " atomic_torch_save(model.state_dict(), raw_best_path)\n", + " print(f\"saved new best model (val_loss={val_loss:.6f}) to {best_checkpoint_path}\", flush=True)\n", + " print(f\"also updated raw best weights to {raw_best_path}\", flush=True)\n", + "\n", + " gc.collect()\n", + " if torch.cuda.is_available(): # MOD: guard\n", + " torch.cuda.empty_cache()\n", + "\n", + " if state[\"terminate\"]:\n", + " print(f\"Terminating early after epoch {epoch} due to signal. Progress saved.\", flush=True)\n", + " break\n", + "\n", + "# summary statistics printing\n", + "print(f\"\\nSaved cumulative losses to: {cumulative_path}\")\n", + "print(f\"Best validation epoch: {best_epoch} with val_loss={best_val_loss:.6f}\")\n", + "done_flag = save_dirs / \"done.flag\"\n", + "done_flag.write_text(\"training complete\" if epochs_done >= num_epochs else \"interrupted\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "c2340f81-e0b5-4fb0-bea5-6be83485e419", + "metadata": {}, + "source": [ + "# Loading the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "beea2464-fb89-4a7f-8c4c-dc2bf295b8dc", + "metadata": {}, + "outputs": [], + "source": [ + "from PARCtorch.utilities.load import load_model_weights\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "model_weights_path = \"/home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_Euler_model_weights/model.pth\" # Replace with your path\n", + "model = load_model_weights(model, model_weights_path, device)" + ] + }, + { + "cell_type": "markdown", + "id": "3a1c76db-6285-4565-a57f-f7c500ff043b", + "metadata": {}, + "source": [ + "# Creating the Sequence DataLoader\n", + "\n", + "Now its time to test out our model on the testing dataset!\n", + "\n", + "To do so, we must first redo everything we did during the training stage - but for the new testing data. We first load the test samples using the same `WellDatasetInterface`, except that we'll be pointing towards the validation test dataset of the directory.\n", + "\n", + "After that we again wrap them in a `DataLoader` for sample batching (same as what we did in the training stage). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7ae4ceb5-22be-44a0-90b7-ff7095f7c4c7", + "metadata": {}, + "outputs": [], + "source": [ + "from torch.utils.data import DataLoader, SequentialSampler\n", + "from PARCtorch.data.welldataset_single_trajectory import WellDatasetInterface\n", + "from PARCtorch.data.dataset import initial_condition_collate_fn, custom_collate_fn\n", + "\n", + "test_dir = \"/home/wkt7ne/PARCtorch/PARCtorch/shear_flow/data/test\"\n", + "single_train_dir = \"/home/wkt7ne/PARCtorch/PARCtorch/shear_flow/data/single_file\"\n", + "experimental_train_dir = \"/home/wkt7ne/PARCtorch/PARCtorch/shear_flow/data/experimental_train\"\n", + "train_dir = \"/home/wkt7ne/PARCtorch/PARCtorch/shear_flow/data/train\"\n", + "val_dir = \"/home/wkt7ne/PARCtorch/PARCtorch/shear_flow/data/validation\"\n", + "\n", + "path = test_dir\n", + "\n", + "future_steps = 199\n", + "batch_size = 20\n", + "temporal_skips = 10\n", + "\n", + "gt_spatial_transform = Compose([\n", + " Resize((new_h, new_w)),\n", + "])\n", + "\n", + "# seq_dataset = WellDatasetInterface(\n", + "# well_dataset_args = {\n", + "# \"path\": path,\n", + "# #\"exclude_filters\": [\"shear_flow_Reynolds_5e5_Schmidt_1e0.hdf5\"],\n", + "# \"max_rollout_steps\": future_steps\n", + "# },\n", + "# future_steps = future_steps,\n", + "# delta_t = 2.0,\n", + "# min_val = min_val_tensor,\n", + "# max_val = max_val_tensor,\n", + "# temporal_skip = temporal_skips,\n", + "# spatial_transform = gt_spatial_transform,\n", + "# add_constant_scalars= True, \n", + "# single_trajectory=False,\n", + "# #traj_idx=0\n", + "# )\n", + "\n", + "# ic, t0, t1, gt = seq_dataset[0]\n", + "# seq_dataset.transform = gt_spatial_transform\n", + "\n", + "# seq_loader = DataLoader(\n", + "# seq_dataset,\n", + "# batch_size = batch_size,\n", + "# shuffle = False,\n", + "# num_workers = 1,\n", + "# pin_memory = True,\n", + "# collate_fn = initial_condition_collate_fn,\n", + "# )\n", + "\n", + "# ic, t0, t1, gt = seq_dataset[0]\n", + "# print(\"IC shape:\", ic.shape) # expect [C, 80, 168]\n", + "# print(\"GT shape:\", gt.shape) # expect [T_out, C, 80, 168]\n", + "# print(\"len(t1):\", len(t1)) " + ] + }, + { + "cell_type": "markdown", + "id": "06474ccd-b36f-4010-9e28-4b11bcb56d00", + "metadata": {}, + "source": [ + "# Loading Ground Truths" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9eacf93-f7f1-4152-a73d-f3655d94cbdf", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the dataset\n", + "\n", + "# \"exclude_filters\": [\"shear_flow_Reynolds_5e5_Schmidt_1e0.hdf5\"]\n", + "gt_dataset = WellDatasetInterface(\n", + " well_dataset_args = {\n", + " \"path\": path,\n", + " \"exclude_filters\": [\"shear_flow_Reynolds_5e5_Schmidt_1e0.hdf5\"],\n", + " \"max_rollout_steps\": future_steps\n", + " },\n", + " future_steps = future_steps,\n", + " delta_t = 2.0,\n", + " add_constant_scalars= True,\n", + " temporal_skip = temporal_skips, \n", + " spatial_transform = gt_spatial_transform, \n", + " min_val = min_val_tensor,\n", + " max_val = max_val_tensor,\n", + " single_trajectory = True,\n", + " traj_idx = 0,\n", + ")\n", + "\n", + "gt_loader = DataLoader(\n", + " gt_dataset,\n", + " batch_size=batch_size,\n", + " shuffle=False,\n", + " num_workers=1,\n", + " pin_memory=True,\n", + " collate_fn=custom_collate_fn,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "7747e1b6-ed33-4dea-b3e0-fbb3f4dae86a", + "metadata": {}, + "source": [ + "# Rollout and Snapshot Prediction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3caae88f-bb5f-4511-9b8c-e4611470ca65", + "metadata": {}, + "outputs": [], + "source": [ + "import os, numpy as np, torch, matplotlib.pyplot as plt, imageio\n", + "\n", + "mode = \"rollout\" # snapshot or rollout\n", + "out_dir = \"/home/wkt7ne/PARCtorch/PARCtorch/demos/gifs\"\n", + "figsize, dpi, fps = (6, 3), 120, 6\n", + "const_start = 4 # first index of constant channels\n", + "keep_constants = True\n", + "center_p_for_viz = True\n", + "pressure_pct = (1, 95) # reserved for future pressure scaling (unused)\n", + "\n", + "# rollout visualization stabilizers\n", + "safe_dt = 1 # if dataset delta_t > safe_dt, use substeps\n", + "nan_guard = True # replace NaN/Inf with 0\n", + "unit_velocity = True # normalize (vx, vy) to unit length\n", + "\n", + "# Setup\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "model.eval()\n", + "os.makedirs(out_dir, exist_ok=True)\n", + "\n", + "# Batch\n", + "gt_ic, gt_t0, gt_t1, gt = next(iter(gt_loader)) # ic:[B,C,H,W], t0:[], t1:[T], gt:[T,B,C,H,W]\n", + "gt_ic, gt_t0, gt_t1, gt = gt_ic.to(device), gt_t0.to(device), gt_t1.to(device), gt.to(device)\n", + "t0_abs = gt_t0.squeeze() # scalar absolute start time\n", + "t1_vec = gt_t1.flatten() # [T] absolute target times\n", + "base_dt = float(t1_vec[0] - t0_abs) # assumed uniform dataset step\n", + "\n", + "@torch.no_grad()\n", + "def predict_next_state(x, t_prev_abs, t_next_abs):\n", + " \"\"\"Advance state from t_prev_abs to t_next_abs in one model call. Return shape [B,C,H,W].\"\"\"\n", + " y = model(x, t_prev_abs, t_next_abs.view(1))\n", + " if y.ndim == 5: y = y[-1]\n", + " if y.ndim == 3: y = y.unsqueeze(0)\n", + " return y\n", + "\n", + "@torch.no_grad()\n", + "def constrain_state(x, ic, const_start=4, unit_velocity=True, nan_guard=True, keep_constants=True):\n", + " \"\"\"Ensure valid shape; optional NaN removal; pin constants; center pressure; normalize velocity.\"\"\"\n", + " if x.ndim == 5: x = x[-1]\n", + " if x.ndim == 3: x = x.unsqueeze(0)\n", + " if ic.ndim == 3: ic = ic.unsqueeze(0)\n", + " if nan_guard:\n", + " x = torch.nan_to_num(x, nan=0.0, posinf=0.0, neginf=0.0)\n", + " if keep_constants and x.shape[1] > const_start and ic.shape[1] >= x.shape[1]:\n", + " x[:, const_start:] = ic[:, const_start:]\n", + " if x.shape[1] >= 2:\n", + " p = x[:, 1:2]\n", + " x[:, 1:2] = p - p.mean(dim=(-2, -1), keepdim=True)\n", + " if unit_velocity and x.shape[1] >= 4:\n", + " eps = 1e-12\n", + " vx, vy = x[:, 2:3], x[:, 3:4]\n", + " speed = torch.sqrt(vx*vx + vy*vy + eps)\n", + " x[:, 2:3], x[:, 3:4] = vx / speed, vy / speed\n", + " if nan_guard:\n", + " x = torch.nan_to_num(x, nan=0.0, posinf=0.0, neginf=0.0)\n", + " return x\n", + "\n", + "def pick_color_limits_from_ground_truth(ch, a_gt, pressure_pct=(1, 99)):\n", + " \"\"\"Channel-wise color limits:\n", + " tracer: fixed [0,1]; pressure: ±percentile of |GT fluctuations|;\n", + " velocity: [-1,1]; other: GT 1–99th percentile.\"\"\"\n", + " if ch == 0: # tracer\n", + " return 0.0, 1.0\n", + " if ch == 1: # pressure (symmetric about 0)\n", + " a = a_gt - a_gt.mean(axis=(1, 2), keepdims=True)\n", + " hi = float(pressure_pct[1]) # <- actually use your setting\n", + " v = float(max(np.percentile(np.abs(a), hi), 1e-6))\n", + " return -v, v\n", + " if ch in (2, 3): # velocities\n", + " return -1.0, 1.0\n", + " lo, hi = np.percentile(a_gt, [1, 99])\n", + " return float(lo), float(hi)\n", + "\n", + "\n", + "# model inference logic\n", + "T = gt.shape[0]\n", + "if mode == \"snapshot\":\n", + " pred_steps = []\n", + " for k in range(T):\n", + " prev_state = gt_ic if k == 0 else gt[k-1]\n", + " t_prev, t_next = (t0_abs if k == 0 else t1_vec[k-1]), t1_vec[k]\n", + " y = predict_next_state(prev_state, t_prev, t_next)\n", + " y = constrain_state(y, gt_ic, const_start, unit_velocity, nan_guard, keep_constants)\n", + " pred_steps.append(y)\n", + " pred = torch.stack(pred_steps, dim=0)\n", + "elif mode == \"rollout\":\n", + " \"\"\"rollout with optional substeps to improve stability and accuracy.\"\"\"\n", + " pred_steps, x = [], gt_ic.clone()\n", + " t_prev_abs = t0_abs\n", + " for i in range(T):\n", + " t_next_abs = t_prev_abs + base_dt\n", + " n_sub = max(1, int(np.ceil(abs(base_dt) / safe_dt)))\n", + " if n_sub > 1:\n", + " sub_times = torch.linspace(float(t_prev_abs), float(t_next_abs), n_sub+1, device=device)[1:]\n", + " y, tp = x, t_prev_abs\n", + " for tn in sub_times:\n", + " y = predict_next_state(y, tp, tn)\n", + " y = constrain_state(y, gt_ic, const_start, unit_velocity, nan_guard, keep_constants)\n", + " tp = tn\n", + " x = y\n", + " else:\n", + " y = predict_next_state(x, t_prev_abs, t_next_abs)\n", + " x = constrain_state(y, gt_ic, const_start, unit_velocity, nan_guard, keep_constants)\n", + " pred_steps.append(x)\n", + " t_prev_abs = t_next_abs\n", + " pred = torch.stack(pred_steps, dim=0)\n", + "else:\n", + " raise ValueError(\"mode must be 'snapshot' or 'rollout'\")\n", + "\n", + "# Plotting preparation\n", + "T = min(pred.shape[0], gt.shape[0])\n", + "C = min(pred.shape[2], gt.shape[2])\n", + "pred, gt = pred[:T, :, :C], gt[:T, :, :C]\n", + "preds_np = pred.detach().cpu().numpy()\n", + "gts_np = gt.detach().cpu().numpy()\n", + "batch0 = 0\n", + "c_fields = min(4, C)\n", + "c_const = max(0, C - c_fields)\n", + "field_names = [\"tracer\", \"pressure\", \"velocity_x\", \"velocity_y\"][:c_fields]\n", + "const_names = [\"reynolds\", \"schmidt\"][:c_const] + [f\"const{j}\" for j in range(max(0, c_const-2))]\n", + "names = field_names + const_names\n", + "cmaps = [\"RdBu_r\", \"RdBu_r\", \"seismic\", \"seismic\"][:c_fields] + ([\"seismic\"] * c_const)\n", + "\n", + "def center_frames_for_display(arr):\n", + " \"\"\"Center array by subtracting spatial mean per frame (display only).\"\"\"\n", + " return arr - arr.mean(axis=(1, 2), keepdims=True)\n", + "\n", + "# Plotting\n", + "for ch in range(C):\n", + " a_pred = preds_np[:, batch0, ch].copy()\n", + " a_gt = gts_np[:, batch0, ch].copy()\n", + " vmin, vmax = pick_color_limits_from_ground_truth(ch, a_gt, pressure_pct)\n", + " if center_p_for_viz and ch == 1:\n", + " a_pred = center_frames_for_display(a_pred)\n", + " a_gt = center_frames_for_display(a_gt)\n", + " a_pred_vis = np.clip(a_pred, vmin, vmax)\n", + " frames = []\n", + " for ti in range(T):\n", + " fig, axes = plt.subplots(1, 2, figsize=figsize, dpi=dpi, constrained_layout=True)\n", + " im0 = axes[0].imshow(a_pred_vis[ti], cmap=cmaps[ch], vmin=vmin, vmax=vmax, interpolation=\"nearest\")\n", + " im1 = axes[1].imshow(a_gt[ti], cmap=cmaps[ch], vmin=vmin, vmax=vmax, interpolation=\"nearest\")\n", + " \n", + " cbar = fig.colorbar(im1, ax=axes, fraction=0.05, pad=0.02)\n", + "\n", + " if ch == 0: # tracer color bar fix\n", + " cbar.set_ticks([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])\n", + " \n", + " axes[0].set_title(f\"{names[ch]} (pred) | frame {ti+1}/{T}\", fontsize=9)\n", + " axes[1].set_title(f\"{names[ch]} (gt) | frame {ti+1}/{T}\", fontsize=9)\n", + " for ax in axes: ax.axis(\"off\")\n", + " fig.canvas.draw()\n", + " buf, (wbuf, hbuf) = fig.canvas.print_to_buffer()\n", + " frames.append(np.frombuffer(buf, dtype=np.uint8).reshape(hbuf, wbuf, 4)[..., :3])\n", + " plt.close(fig)\n", + " gif_path = os.path.join(out_dir, f\"{mode}_ch{ch:02d}_{names[ch]}.gif\")\n", + " imageio.mimsave(gif_path, frames, fps=fps)\n", + " print(\"wrote\", gif_path)\n" + ] + }, + { + "cell_type": "markdown", + "id": "a44013f6-7653-4203-8130-01784c61e3c7", + "metadata": {}, + "source": [ + "# Visualizing the Loss Curve \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e0c97334-61c3-4cc7-a4d1-9da986d1bda7", + "metadata": {}, + "source": [ + "Finally, to monitor the model's learning progress and ensure that the optimization is happening correctly, it's important to plot the loss curve over each training epoch. Overall, a decreasing loss trend will indicate that the model is minimizing the loss function as intended. \n", + "\n", + "**To do so, we:**\n", + "\n", + "1. Load the saved training loss history from the pickle file, which was recorded durin the training to a designated directory.\n", + "\n", + "2. Plot the loss against epochs to visualize how the model's performance changes over time\n", + "\n", + "3. Save the loss curve image for future references\n", + "\n", + "**How to diagnose your model performances:**\n", + "\n", + "- **Steadily decreasing loss (Ideal)**: suggests that the model is learning effectively, and that its predictions are getting closer to the ground truth as training progresses.\n", + "\n", + " - Consider stopping early (reducing `num_epochs`) if the validation stabilizes at an earlier point of the training iterations.\n", + " \n", + "- **Loss increases or becomes erratic**: numerical instability due to large spatial inputs, or suggests that perhaps the batch size is too large for GPU memory, causing gradient accumulation fragmentation. \n", + "\n", + " - Consider reducing `batch_size` to free up more memory for GPU.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2ddc9577-779e-4ceb-b6e6-a1c9d1b618d6", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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e1U033aTQ0FClpqZq7dq1Sk1NrXRGrjJz587Vnj17yrT3799f48aN09VXX60ePXrokUcekZubmz744ANt2LBB3333nTl71LFjR11zzTVq3bq1AgMDtXnzZn311VeKj4+Xl5eX1q1bp4ceekg333yzGjduLDc3N82dO1fr1q3TY489Vun4br75Zj399NN68cUXtWXLFt1zzz3mRsxLly7VRx99pMGDB6t3796yWCy68847NXHiRDVs2FBt2rTRsmXLygTa89GsWTPFx8dr3Lhx2r9/vz7++GOr45fzdwIAlbJtDRAAqFkmT55s3H777Ubjxo0NHx8fw9XV1ahbt64xZMgQY9OmTVZ969WrZwwYMKDMObp37250797dqi01NdUYMWKEER0dbbi6uhpBQUFGbGys8eSTTxrZ2dlmv4kTJxpNmzY13N3djQYNGhjjxo0zJkyYUKbqXkWffa5jpSqq5Pfpp58arVu3Ntzc3Ax/f39j4MCB5VZpXLt2rSHJaNy4sVX7yy+/bEgyxowZU+nnlzqzCmJFyquCaBiGsX79euPaa681/P39DTc3N6NNmzblVufbtGmTcfXVVxseHh5GUFCQcc899xg//fSTVRXEUvPnzzcGDBhgBAUFGa6urkbt2rWNAQMGGFOmTDH7XGgVxIq+St+/cOFC46qrrjK8vb0NT09Po1OnTmYlwFKPPfaYERcXZwQGBpr3xujRo420tDTDMAzj8OHDxrBhw4xmzZoZ3t7eho+Pj9G6dWvjrbfeMgoLCysd55nXftNNNxkRERGGq6ur4efnZ8THxxtvvPGGkZmZafbLyMgw/vnPfxphYWGGt7e3ce211xp79uypsApiampqhZ/58ccfG5IMT09PIyMjo8Jxnet3AgCXksUwynkeAAAAAABwybEGDAAAAACqCAEMAAAAAKoIAQwAAAAAqggBDAAAAACqCAEMAAAAAKoIAQwAAAAAqggbMV+k4uJiHTp0SL6+vuZGlgAAAABqHsMwlJWVpcjISDk5VT7HRQC7SIcOHVJUVJSthwEAAADATuzfv1916tSptA8B7CL5+vpKKvkh+/n52Xg0AAAAAGwlMzNTUVFRZkaoDAHsIpU+dujn50cAAwAAAHBeS5MowgEAAAAAVYQABgAAAABVhAAGAAAAAFWEAAYAAAAAVYQABgAAAABVhAAGAAAAAFWEAAYAAAAAVYQABgAAAABVhAAGAAAAAFWEAAYAAAAAVYQABgAAAABVhAAGAAAAAFWEAAYAAAAAVYQABgAAAABVhAAGAAAAAFWEAAYAAAAAVcTF1gPA37dm/3GlZJxU8wg/1Qv2tvVwAAAAAFSAGTAH8MmCXRr+9SrN35Zq66EAAAAAqAQBzBFYSv6nuNiw7TgAAAAAVIoA5gCcLCUJjPgFAAAA2DcCmAM4NQEmJsAAAAAA+0YAcwBOpxKYYZDAAAAAAHtGAHMAltJHEMlfAAAAgF0jgDkAS+kMGKvAAAAAALtGAHMApUU4WAMGAAAA2DcCmAM4XYSDBAYAAADYMwKYA3BiDRgAAABQLRDAHICFKogAAABAtUAAcwBUQQQAAACqBwKYAyjdB4wiHAAAAIB9I4A5AIsZwEhgAAAAgD0jgDkAswiHjccBAAAAoHIEMAdQWoaeIhwAAACAfSOAOQCKcAAAAADVAwHMAbAGDAAAAKgeCGAOgDVgAAAAQPVAAHMATsyAAQAAANUCAcwBsAYMAAAAqB4IYA6gdA0YVRABAAAA+0YAcwCWU4Xoi8lfAAAAgF0jgDkAJ3MGzLbjAAAAAFA5ApgDoAw9AAAAUD0QwByAWYaeAAYAAADYNQKYA7CwDxgAAABQLRDAHMCpJxB5BBEAAACwcwQwB+DEPmAAAABAtUAAcwCni3DYdhwAAAAAKkcAcwClZehZBQYAAADYNwKYAygtwlFcbOOBAAAAAKgUAcwBsA8YAAAAUD0QwByAE2XoAQAAgGrB5gHsgw8+UHR0tDw8PBQbG6uFCxdW2n/+/PmKjY2Vh4eHGjRooA8//LBMn6lTpyomJkbu7u6KiYnR9OnTrY4/99xzslgsVl/h4eGX9LqqEmXoAQAAgOrBpgFs8uTJGjVqlJ588kmtXr1aXbt2Vb9+/bRv375y++/evVv9+/dX165dtXr1aj3xxBMaMWKEpk6davZJSkrS4MGDNWTIEK1du1ZDhgzRLbfcoqVLl1qdq0WLFkpOTja/1q9ff1mv9XIqnQFjCgwAAACwbxbDsN20SceOHdW+fXuNHz/ebGvevLkGDRqkcePGlek/duxYzZgxQ5s3bzbbhg8frrVr1yopKUmSNHjwYGVmZmrWrFlmn759+yowMFDfffedpJIZsB9//FFr1qw577Hm5eUpLy/PfJ2ZmamoqChlZGTIz8/vvM9zOXy6cJde+nWzBrWN1Nu3trPpWAAAAICaJjMzU/7+/ueVDWw2A5afn6+VK1eqd+/eVu29e/fWkiVLyn1PUlJSmf59+vTRihUrVFBQUGmfs8+5fft2RUZGKjo6Wrfeeqt27dpV6XjHjRsnf39/8ysqKuq8rrMqWFgDBgAAAFQLNgtgaWlpKioqUlhYmFV7WFiYUlJSyn1PSkpKuf0LCwuVlpZWaZ8zz9mxY0d9+eWX+v333/XJJ58oJSVFCQkJSk9Pr3C8jz/+uDIyMsyv/fv3X9D1Xk5ObMQMAAAAVAsuth5A6exNKcMwyrSdq//Z7ec6Z79+/czvW7Vqpfj4eDVs2FBffPGFxowZU+7nuru7y93d/RxXYxsU4QAAAACqB5vNgIWEhMjZ2bnMbNeRI0fKzGCVCg8PL7e/i4uLgoODK+1T0TklydvbW61atdL27dsv5lJszsmJIhwAAABAdWCzAObm5qbY2FglJiZatScmJiohIaHc98THx5fpP3v2bMXFxcnV1bXSPhWdUyopsLF582ZFRERczKXYHDNgAAAAQPVg0zL0Y8aM0aeffqqJEydq8+bNGj16tPbt26fhw4dLKll3ddddd5n9hw8frr1792rMmDHavHmzJk6cqAkTJuiRRx4x+4wcOVKzZ8/Wa6+9pi1btui1117TnDlzNGrUKLPPI488ovnz52v37t1aunSpbrrpJmVmZmro0KFVdu2XklmEg/wFAAAA2DWbrgEbPHiw0tPT9cILLyg5OVktW7bUzJkzVa9ePUlScnKy1Z5g0dHRmjlzpkaPHq33339fkZGReuedd3TjjTeafRISEjRp0iQ99dRTevrpp9WwYUNNnjxZHTt2NPscOHBAt912m9LS0lSrVi116tRJf/31l/m51Y3FLMJBAgMAAADsmU33AavOLqTW/+X23bJ9enzaevVqHqZPh8bZdCwAAABATVMt9gHDpeNkFngkSwMAAAD2jADmACynynCwDxgAAABg3whgDqB0DRhPkwIAAAD2jQDmAEqrIDIDBgAAANg3ApgDYB9mAAAAoHoggDkAJ3MfMCIYAAAAYM8IYA6AfcAAAACA6oEA5gAs5gyYjQcCAAAAoFIEMAdQug0YM2AAAACAfSOAOQAnZsAAAACAaoEA5gBO7wNm23EAAAAAqBwBzAE4UYQDAAAAqBYIYA7ALMJh43EAAAAAqBwBzAFQhAMAAACoHghgDoAiHAAAAED1QABzAKeLcJDAAAAAAHtGAHMATqwBAwAAAKoFApgDsFAFEQAAAKgWCGAOoLQKYnGxjQcCAAAAoFIEMAdQug8Y818AAACAfSOAOQCLSqsgEsEAAAAAe0YAcwDmDBj5CwAAALBrBDBHQBEOAAAAoFoggDkAytADAAAA1QMBzAGUBjBmwAAAAAD7RgBzABbWgAEAAADVAgHMAZwuwkECAwAAAOwZAcwhlD6CaONhAAAAAKgUAcwBnN6ImQQGAAAA2DMCmAOwlBbhKLbxQAAAAABUigDmAFgDBgAAAFQPBDAHwD5gAAAAQPVAAHMg7AMGAAAA2DcCmAMwZ8DIXwAAAIBdI4A5gNKNmClDDwAAANg3ApgDKJ0BYxUYAAAAYN8IYA7AiRkwAAAAoFoggDmA048gksAAAAAAe0YAcwAWinAAAAAA1QIBzAGUrgBjBgwAAACwbwQwB2AW4SB/AQAAAHaNAOYAWAMGAAAAVA8EMAdgbsRs43EAAAAAqBwBzAEwAwYAAABUDwQwB1BaBZF9wAAAAAD7RgBzAKUbMfMMIgAAAGDfCGAOwKLSGTASGAAAAGDPCGAOwIkq9AAAAEC1QABzBBThAAAAAKoFApgDMMvQk78AAAAAu0YAcwClAUySDFIYAAAAYLcIYA7Acsb3lKIHAAAA7BcBzAEwAwYAAABUDwQwR3DGFBgzYAAAAID9IoA5AKczAphBMXoAAADAbhHAHID1I4g2HAgAAACAShHAHIDF6hFEEhgAAABgrwhgDoAZMAAAAKB6IIA5GGbAAAAAAPtFAHMAVjNgNhwHAAAAgMoRwBzAmWvAjGLbjQMAAABA5QhgDuDMGTAeQQQAAADsFwHMAVjvAwYAAADAXhHAHICFGTAAAACgWiCAOYjSDEb+AgAAAOwXAcxBlM6BGSQwAAAAwG4RwBxEaSEO4hcAAABgvwhgDqL0EUTWgAEAAAD2iwDmIEoLcRSTvwAAAAC7RQBzEKWl6ItJYAAAAIDdIoA5CHcXZ0lSXmGxjUcCAAAAoCIEMAfh4+4iScrOK7TxSAAAAABUhADmILzdS2bAcghgAAAAgN0igDkIb2bAAAAAALtHAHMQpY8gMgMGAAAA2C8CmIMggAEAAAD2jwDmIEofQcwigAEAAAB2iwDmIJgBAwAAAOwfAcxBnK6CWGTjkQAAAACoCAHMQfi4u0qiCiIAAABgzwhgDsLn1AxYdi4BDAAAALBXBDAHUVqEIyefAAYAAADYK5sHsA8++EDR0dHy8PBQbGysFi5cWGn/+fPnKzY2Vh4eHmrQoIE+/PDDMn2mTp2qmJgYubu7KyYmRtOnT6/wfOPGjZPFYtGoUaP+7qXYFBsxAwAAAPbPpgFs8uTJGjVqlJ588kmtXr1aXbt2Vb9+/bRv375y++/evVv9+/dX165dtXr1aj3xxBMaMWKEpk6davZJSkrS4MGDNWTIEK1du1ZDhgzRLbfcoqVLl5Y53/Lly/Xxxx+rdevWl+0aq4pvaRl6HkEEAAAA7JbFMAzDVh/esWNHtW/fXuPHjzfbmjdvrkGDBmncuHFl+o8dO1YzZszQ5s2bzbbhw4dr7dq1SkpKkiQNHjxYmZmZmjVrltmnb9++CgwM1HfffWe2ZWdnq3379vrggw/00ksvqW3btnr77bfPe+yZmZny9/dXRkaG/Pz8LuSyL4utKVnq8/YCBXi5as0zvW09HAAAAKDGuJBsYLMZsPz8fK1cuVK9e1uHhd69e2vJkiXlvicpKalM/z59+mjFihUqKCiotM/Z53zwwQc1YMAA9erV67zGm5eXp8zMTKsvexLq6y5JOn6iQHmFlKIHAAAA7JHNAlhaWpqKiooUFhZm1R4WFqaUlJRy35OSklJu/8LCQqWlpVXa58xzTpo0SatWrSp3lq0i48aNk7+/v/kVFRV13u+tCv6ernJ1tkiS0rPzbTwaAAAAAOWxeREOi8Vi9dowjDJt5+p/dntl59y/f79Gjhypr7/+Wh4eHuc9zscff1wZGRnm1/79+8/7vVXBycmiEJ+SWbAjWXk2Hg0AAACA8rjY6oNDQkLk7OxcZrbryJEjZWawSoWHh5fb38XFRcHBwZX2KT3nypUrdeTIEcXGxprHi4qKtGDBAr333nvKy8uTs7Nzmc92d3eXu7v7hV9oFQr1dVdyRq5SCWAAAACAXbLZDJibm5tiY2OVmJho1Z6YmKiEhIRy3xMfH1+m/+zZsxUXFydXV9dK+5Ses2fPnlq/fr3WrFljfsXFxemOO+7QmjVryg1f1UUt39IZsFwbjwQAAABAeWw2AyZJY8aM0ZAhQxQXF6f4+Hh9/PHH2rdvn4YPHy6p5LG/gwcP6ssvv5RUUvHwvffe05gxY3TvvfcqKSlJEyZMsKpuOHLkSHXr1k2vvfaaBg4cqJ9++klz5szRokWLJEm+vr5q2bKl1Ti8vb0VHBxcpr26MQNYJjNgAAAAgD2yaQAbPHiw0tPT9cILLyg5OVktW7bUzJkzVa9ePUlScnKy1Z5g0dHRmjlzpkaPHq33339fkZGReuedd3TjjTeafRISEjRp0iQ99dRTevrpp9WwYUNNnjxZHTt2rPLrq2qla8CO5lCEAwAAALBHNt0HrDqzt33AJGniot164ZdNuqZ1hN67vb2thwMAAADUCNViHzBceoHeJevgjp1gBgwAAACwRwQwBxLo5SZJOppTYOORAAAAACgPAcyBBHmXBLBjrAEDAAAA7BIBzIGUzoAdO5EvlvYBAAAA9ocA5kACT82A5RUW62RBkY1HAwAAAOBsBDAH4u3mLDfnkl8ppegBAAAA+0MAcyAWi+V0JUQKcQAAAAB2hwDmYPw9SwJYxkkCGAAAAGBvCGAOxtvdRZKUnVdo45EAAAAAOBsBzMH4nApgOQQwAAAAwO4QwByMt9upAJZPAAMAAADsDQHMwfAIIgAAAGC/CGAOxsfdWZJ0Io99wAAAAAB7QwBzMMyAAQAAAPaLAOZgvCnCAQAAANgtApiDMasgUoQDAAAAsDsEMAdz+hFE1oABAAAA9oYA5mBKi3DwCCIAAABgfwhgDoY1YAAAAID9IoA5GKogAgAAAPaLAOZgvN2YAQMAAADsFQHMwXiba8AowgEAAADYGwKYgyktQ59fVKz8wmIbjwYAAADAmQhgDqZ0DZjEY4gAAACAvSGAORhXZye5uZT8WinEAQAAANgXApgDKn0MMSefAAYAAADYEwKYA6IQBwAAAGCfCGAOiFL0AAAAgH0igDkg8xFEAhgAAABgVwhgDqi0EiJFOAAAAAD7QgBzQMyAAQAAAPaJAOaAzCIc+RThAAAAAOwJAcwB8QgiAAAAYJ8IYA6IRxABAAAA+0QAc0DMgAEAAAD2iQDmgLyZAQMAAADsEgHMAfmUFuHIowgHAAAAYE8IYA7I241HEAEAAAB7RABzQBThAAAAAOwTAcwBeRHAAAAAALtEAHNAPmzEDAAAANglApgDOrMKomEYNh4NAAAAgFIEMAdUGsAKiw3lFRbbeDQAAAAAShHAHFBpFUSJdWAAAACAPSGAOSBnJ4s8XdkLDAAAALA3BDAHVfoYInuBAQAAAPaDAOagTldCJIABAAAA9oIA5qCYAQMAAADsDwHMQXmzGTMAAABgdwhgDirA01WSdOxEgY1HAgAAAKAUAcxB1fJ1lySlZuXZeCQAAAAAShHAHBQBDAAAALA/BDAHVRrA0rIJYAAAAIC9IIA5qBAfZsAAAAAAe0MAc1A8gggAAADYHwKYg6pVOgOWnSfDMGw8GgAAAAASAcxh1fJ1l7OTRfmFxZq75YithwMAAABABDCH5eHqrCGd6kmSPlqwy8ajAQAAACARwBzatW0iJEnJGSdtPBIAAAAAEgHModXy8ZBUUoiDdWAAAACA7RHAHFiIr5skKbegWNl5hTYeDQAAAICLCmD79+/XgQMHzNfLli3TqFGj9PHHH1+ygeHv83JzkY+7iyTK0QMAAAD24KIC2O23364///xTkpSSkqKrr75ay5Yt0xNPPKEXXnjhkg4Qfw/7gQEAAAD246IC2IYNG9ShQwdJ0vfff6+WLVtqyZIl+vbbb/X5559fyvHhbzpzPzAAAAAAtnVRAaygoEDu7iV/sZ8zZ46uu+46SVKzZs2UnJx86UaHv610BiyNGTAAAADA5i4qgLVo0UIffvihFi5cqMTERPXt21eSdOjQIQUHB1/SAeLvMR9BZAYMAAAAsLmLCmCvvfaaPvroI1155ZW67bbb1KZNG0nSjBkzzEcTYR9CfEoqIbIGDAAAALA9l4t505VXXqm0tDRlZmYqMDDQbL/vvvvk5eV1yQaHv48iHAAAAID9uKgZsJMnTyovL88MX3v37tXbb7+trVu3KjQ09JIOEH8PjyACAAAA9uOiAtjAgQP15ZdfSpKOHz+ujh076s0339SgQYM0fvz4SzpA/D21fDwkMQMGAAAA2IOLCmCrVq1S165dJUk//PCDwsLCtHfvXn355Zd65513LukA8feYVRCz81VcbNh4NAAAAEDNdlEB7MSJE/L19ZUkzZ49WzfccIOcnJzUqVMn7d2795IOEH9P8KkiHEXFho6fLLDxaAAAAICa7aICWKNGjfTjjz9q//79+v3339W7d29J0pEjR+Tn53dJB4i/x9XZSUHeVEIEAAAA7MFFBbBnnnlGjzzyiOrXr68OHTooPj5eUslsWLt27S7pAPH31fKhEiIAAABgDy6qDP1NN92kLl26KDk52dwDTJJ69uyp66+//pINDpdGiK+bth6WUrNzbT0UAAAAoEa7qAAmSeHh4QoPD9eBAwdksVhUu3ZtNmG2U8yAAQAAAPbhoh5BLC4u1gsvvCB/f3/Vq1dPdevWVUBAgF588UUVFxdf6jHib2IzZgAAAMA+XNQM2JNPPqkJEybo1VdfVefOnWUYhhYvXqznnntOubm5evnlly/1OPE3EMAAAAAA+3BRAeyLL77Qp59+quuuu85sa9OmjWrXrq0HHniAAGZnzACWTQADAAAAbOmiHkE8evSomjVrVqa9WbNmOnr06N8eFC6tWj4ekpgBAwAAAGztogJYmzZt9N5775Vpf++999S6deu/PShcWvVDvCRJ2w5na+wP61RQxDo9AAAAwBYu6hHE119/XQMGDNCcOXMUHx8vi8WiJUuWaP/+/Zo5c+alHiP+pjqBXmoQ4q1daTmavGK/ejQLVd+W4bYeFgAAAFDjXNQMWPfu3bVt2zZdf/31On78uI4ePaobbrhBGzdu1GeffXZB5/rggw8UHR0tDw8PxcbGauHChZX2nz9/vmJjY+Xh4aEGDRroww8/LNNn6tSpiomJkbu7u2JiYjR9+nSr4+PHj1fr1q3l5+cnPz8/xcfHa9asWRc07upmWOf65vdbU7JsNxAAAACgBruoACZJkZGRevnllzV16lRNmzZNL730ko4dO6YvvvjivM8xefJkjRo1Sk8++aRWr16trl27ql+/ftq3b1+5/Xfv3q3+/fura9euWr16tZ544gmNGDFCU6dONfskJSVp8ODBGjJkiNauXashQ4bolltu0dKlS80+derU0auvvqoVK1ZoxYoVuuqqqzRw4EBt3LjxYn8cdu/OjvV0XZtISdLO1GwbjwYAAAComSyGYRiX6mRr165V+/btVVRUdF79O3bsqPbt22v8+PFmW/PmzTVo0CCNGzeuTP+xY8dqxowZ2rx5s9k2fPhwrV27VklJSZKkwYMHKzMz02pGq2/fvgoMDNR3331X4ViCgoL0xhtv6J577jmvsWdmZsrf318ZGRny8/M7r/fY2pxNh/XPL1eoeYSfZo3sauvhAAAAAA7hQrLBRc+A/V35+flauXKlevfubdXeu3dvLVmypNz3JCUllenfp08frVixQgUFBZX2qeicRUVFmjRpknJychQfH1/hePPy8pSZmWn1Vd00CvWRVDIDVlx8yXI3AAAAgPNkswCWlpamoqIihYWFWbWHhYUpJSWl3PekpKSU27+wsFBpaWmV9jn7nOvXr5ePj4/c3d01fPhwTZ8+XTExMRWOd9y4cfL39ze/oqKizvta7UXtQE9ZLFJ+YbHScihJDwAAAFS1C6qCeMMNN1R6/Pjx4xc8AIvFYvXaMIwybefqf3b7+ZyzadOmWrNmjY4fP66pU6dq6NChmj9/foUh7PHHH9eYMWPM15mZmdUuhLk6OynU112HM/OUkpGrUF8PWw8JAAAAqFEuKID5+/uf8/hdd911XucKCQmRs7NzmZmpI0eOlJnBKhUeHl5ufxcXFwUHB1fa5+xzurm5qVGjRpKkuLg4LV++XP/73//00UcflfvZ7u7ucnd3P69rs2fh/p46nJmnQ8dz1bqOrUcDAAAA1CwXFMAutMR8Zdzc3BQbG6vExERdf/31ZntiYqIGDhxY7nvi4+P1888/W7XNnj1bcXFxcnV1NfskJiZq9OjRVn0SEhIqHY9hGMrLc/zH8iL9PbR2v/Rl0h71jgmTk1PFs40AAAAALq2L2oj5UhkzZoyGDBmiuLg4xcfH6+OPP9a+ffs0fPhwSSWP/R08eFBffvmlpJKKh++9957GjBmje++9V0lJSZowYYJVdcORI0eqW7dueu211zRw4ED99NNPmjNnjhYtWmT2eeKJJ9SvXz9FRUUpKytLkyZN0rx58/Tbb79V7Q/ABsL9Sx47XLIzXdNXH9SNsUyDAQAAAFXFpgFs8ODBSk9P1wsvvKDk5GS1bNlSM2fOVL169SRJycnJVnuCRUdHa+bMmRo9erTef/99RUZG6p133tGNN95o9klISNCkSZP01FNP6emnn1bDhg01efJkdezY0exz+PBhDRkyRMnJyfL391fr1q3122+/6eqrr666i7cRdxdn8/sZaw8RwAAAAIAqdEn3AatJquM+YJK06VCm+r+zUJLUNMxXv4/uZuMRAQAAANVbtdgHDLYRE+mnJY9dJUnafiRLOXmFNh4RAAAAUHMQwGqgyABPBXu7qdiQ9qTn2Ho4AAAAQI1BAKuh6gR5SZL2Hz1p45EAAAAANQcBrIaKCvSUJB04dkJHc/L18HerNej9xTqSmWvjkQEAAACOiwBWQ0WdmgHbd/SEHpmyVj+vPaQ1+4/rl3XJNh4ZAAAA4LhsWoYethMVWBLAFm1P06600+vANidn2mpIAAAAgMNjBqyGqhdcEsDODF+StDmFAAYAAABcLgSwGqpNVICcnSzm68ahPpKkbYezVVhUbKthAQAAAA6NAFZD+bi7qHUdf/N1pwbB8nZzVn5hcZlZMQAAAACXBgGsBuvaKMT8PirIU03DfSWxDgwAAAC4XAhgNVi/VhHm9wFebmoe4SdJ2kQAAwAAAC4LqiDWYM3CfRXu56GUzFx1qB+k/MKStV+bk7NsPDIAAADAMRHAajCLxaKZI7sqPTtP9UO8lZ6TL0nawgwYAAAAcFnwCGINF+TtpsZhJWu/moX7ymKRjmTlKT07z8YjAwAAABwPAQwmb3cX1Qsq2R+MxxABAACAS48ABisxkSWFOOZtPWLjkQAAAACOhwAGKze2ryNJ+iJpj1Iycm08GgAAAMCxEMBg5apmoWpXN0AFRYZmb0qx9XAAAAAAh0IAgxWLxaJ+LcMlSZOW7VduQZGNRwQAAAA4DgIYyrimdaQ8XJ20KTlTb87eauvhAAAAAA6DAIYyIgM89Z+b20gqmQU7dmp/MAAAAAB/DwEM5erfMkINankrK69Q3d74U/+bs12GYdh6WAAAAEC1RgBDuZycLHr3tnYK9XVXVm6h3pqzTTPXU5QDAAAA+DsIYKhQi0h/Lfh3D/VqHipJWrH3qI1HBAAAAFRvBDBUysPVWb1jSqoibjucZePRAAAAANUbAQzn1DTcV5K0NSVLOXmFOpLFBs0AAADAxSCA4Zwah/nIYpHSsvPV4tnf1fGVP7Q3PcfWwwIAAACqHQIYzsnLzUU3x9YxXxuGNGXFARuOCAAAAKieCGA4LyN7NbF6/d6fO/Tj6oM2Gg0AAABQPRHAcF4i/T3KtD3380YbjAQAAACovghgOC8Wi6VM2/ETBVq175g+mLdDmbkFkqQlO9O0dv/xKh4dAAAAUD242HoAqD6cnSwqKjas2m74YIkkyc3ZSVc2DdXtnyyVJO18pb+cncqGNgAAAKAmYwYM5+3bf3ZUu7oB+unBzurRtJbVsf/N2a735m43Xx86frKqhwcAAADYPQIYzlvHBsGa/kBntYkK0JD4elbHsvIK9eOaQ+br3WmUqQcAAADORgDDRenRNFS3d6yrpmG+5R7fwz5hAAAAQBkEMFwUi8WiV65vpS/v6WDV7udRsqxwVyoBDAAAADgbAQx/S6ivu/n9xGFxenJAc0nS9iNZthoSAAAAYLeogoi/xWKx6NUbWmlXWo6ubBKqzSmZkqR1BzJUXGzIiUqIAAAAgIkZMPxtt3aoqyf6N5eTk0VNwnxlsUhZuYX655crZBjGuU8AAAAA1BAEMFxSrs5Oal83UJI0d8sR7UzNtvGIAAAAAPtBAMMl91CPRub3H87fpYKiYhuOBgAAALAfBDBccj2aheruzvUlST+sPKAvluyx6XgAAAAAe0ERDlwWLSP9ze9f+nWzsvMKFeTtpjs71qMwBwAAAGosAhgui36twvXenzu0O61kP7C352yXJAV5u+n7FQe0YFuqGoX66NUbWimufpAthwoAAABUGR5BxGXh5eaixNHdyrRPWrZfC7alSpJ2HMnWTR8m6Z9fLKdaIgAAAGoEAhguGxdnJz13bYzC/E5v1rxoR1qZfnM2HzFnygAAAABHRgDDZTWsc7SWPtFL0x5IqLTfn1tTq2hEAAAAgO0QwFAlmof7KdjbTZLUv1W46gZ56ebYOhrevaEkadb6ZFsODwAAAKgSFOFAlfB0c9aMh7sor6BIDWr5mO0pGbn6eMFOrdh7TLvTchQd4m3DUQIAAACXFwEMVaZ2gGeZtnB/D3VuFKKF29P0+eLdslgs6h0TpoRGITYYIQAAAHB58QgibK5Tg2BJ0hdJe/X5kj26/dOl2n44y8ajAgAAAC49Ahhsrn3dwDJtv7ImDAAAAA6IAAabaxPlL4vFuu3tOdv19V97bTMgAAAA4DIhgMHmvNxc9Oldcfpnl2h9dvcVZvsHf+5QUTEbNAMAAMBxEMBgF3o2D9NT18SoR9NQrXuut9xcnHQoI1dLd6XbemgAAADAJUMAg93x83BVt8YlVRB3peXYeDQAAADApUMAg12K8C8pWZ+ccdLGIwEAAAAuHQIY7FJEgIckKfl4ro1HAgAAAFw6BDDYpchTM2CHmAEDAACAAyGAwS5F+J+aActgBgwAAACOgwAGuxQZUDIDtjf9hMZ8v0b7j56w8YgAAACAv8/F1gMAyhMZ4KmmYb7aejhL01Yd1PytqWpXN0D5RYbeHtxWQd5uth4iAAAAcMGYAYNdcnay6OeHu+iTu+LUNMxX6Tn5mrP5iBZsS9W7c7fbengAAADARSGAwW65uTjp6pgw/fRQZz1/XQu1iPSTJH22eI9+25Csk/lFNh4hAAAAcGEshmEYth5EdZSZmSl/f39lZGTIz8/P1sOpEfIKi9T0qd/M10Hebvru3k5qGu5rw1EBAACgpruQbMAMGKoNdxdnhfi4m6+P5uTr2RkbbDgiAAAA4MIQwFCthPm5W71edyBDxcVM4gIAAKB6IICh2nJ1tuhEfpEWbE+19VAAAACA80IAQ7WSX1hsfh9+arPmYZ8t15aUTFsNCQAAADhvBDBUK22iAszvuzSqZX4/8L3FemzqOmXmFthgVAAAAMD5YSNmVCtP9G8uLzdn3RIXpRAfd+UWFGn66oPKKyzWpOX7lV9YrDdvaSOLxWLroQIAAABlUIb+IlGG3n4Men+x1uw/br6+OiZM797WTh6uzrYbFAAAAGoMytCjRnl7cFv1bxWuXs1D5ebspMRNhxX7YqLGz9upYzn5th4eAAAAYGIG7CIxA2afPl24Sy/9utl8HeLjpleub6UVe49pzNVNmBUDAADAJccMGGqs69pGWr1Oy87XfV+t1McLdun7FfttNCoAAACgBEU44FBCfT304sAWOpSRKz8PV7322xbz2Nr9GVK8DQcHAACAGo8ZMDicIfH1NbZvM10dE2bVPnXVAdaEAQAAwKYIYHBYjUJ9NGFonP53a1uzbezUdWLZIwAAAGyFAAaH1rN5mK5rE6noEG9J0twtR9T51bm6/oPFOnT8pI1HBwAAgJqGAAaHZ7FY9MvDXSRJhcWGDmXkavW+40p4da4+nL9T6dl5hDEAAABUCQIYagRvdxdF+nuUaX911hbFvjRHfd5aoPTsPBuMDAAAADUJAQw1RsNQnwqPZeUVav621CocDQAAAGoimwewDz74QNHR0fLw8FBsbKwWLlxYaf/58+crNjZWHh4eatCggT788MMyfaZOnaqYmBi5u7srJiZG06dPtzo+btw4XXHFFfL19VVoaKgGDRqkrVu3XtLrgv3p0TRUktQmKkD/d2XDMsfnbSWAAQAA4PKyaQCbPHmyRo0apSeffFKrV69W165d1a9fP+3bt6/c/rt371b//v3VtWtXrV69Wk888YRGjBihqVOnmn2SkpI0ePBgDRkyRGvXrtWQIUN0yy23aOnSpWaf+fPn68EHH9Rff/2lxMREFRYWqnfv3srJybns1wzbubtzfa19prd+fCBBY/s20+qnr9bAtpG6v1sDSdKMtYd0zbsLKVUPAACAy8Zi2LAmd8eOHdW+fXuNHz/ebGvevLkGDRqkcePGlek/duxYzZgxQ5s3bzbbhg8frrVr1yopKUmSNHjwYGVmZmrWrFlmn759+yowMFDfffddueNITU1VaGio5s+fr27dup3X2DMzM+Xv76+MjAz5+fmd13tgn46fyFfbFxLN1yOuaqSRvZpIkpydLLYaFgAAAKqJC8kGNpsBy8/P18qVK9W7d2+r9t69e2vJkiXlvicpKalM/z59+mjFihUqKCiotE9F55SkjIwMSVJQUFCFffLy8pSZmWn1BccQ4OVm9fq9P3eow8tz1OftBcotKLLRqAAAAOCIbBbA0tLSVFRUpLCwMKv2sLAwpaSklPuelJSUcvsXFhYqLS2t0j4VndMwDI0ZM0ZdunRRy5YtKxzvuHHj5O/vb35FRUWd8xpRPRUbUnpOvnYcydbvG1O0/+gJTVq2T5m5BbYeGgAAAKo5mxfhsFisH/EyDKNM27n6n91+Ied86KGHtG7dugofTyz1+OOPKyMjw/zav39/pf1RvdzYvo4kKdDL1ar9tVlb1O2NP/XYtPX67+xtthgaAAAAHIiLrT44JCREzs7OZWamjhw5UmYGq1R4eHi5/V1cXBQcHFxpn/LO+fDDD2vGjBlasGCB6tSpU+l43d3d5e7ufs7rQvX04qAW6tYkRH1ahOuPzUf00YKdWncgQ4cycs0+ny/Zo7pBXvpHl2gbjhQAAADVmc1mwNzc3BQbG6vExESr9sTERCUkJJT7nvj4+DL9Z8+erbi4OLm6ulba58xzGoahhx56SNOmTdPcuXMVHc1fqGs6LzcXDWxbWx6uzhrQOkIzHuqins1Cy/R74ZdNyuJRRAAAAFwkmz6COGbMGH366aeaOHGiNm/erNGjR2vfvn0aPny4pJLH/u666y6z//Dhw7V3716NGTNGmzdv1sSJEzVhwgQ98sgjZp+RI0dq9uzZeu2117Rlyxa99tprmjNnjkaNGmX2efDBB/X111/r22+/la+vr1JSUpSSkqKTJ09W2bXD/j0/sIWGJdTXiJ6NrdqveXeRNhzMsNGoAAAAUJ3ZtAy9VLIR8+uvv67k5GS1bNlSb731llkKftiwYdqzZ4/mzZtn9p8/f75Gjx6tjRs3KjIyUmPHjjUDW6kffvhBTz31lHbt2qWGDRvq5Zdf1g033GAer2g92GeffaZhw4ad17gpQ1+z/LYhRcO/XmnVtuaZq+Xh6iwPV2cbjQoAAAD24EKygc0DWHVFAKt5Hp2yVlNWHrBqC/By1Q/DE9Qo1MdGowIAAICtVYt9wIDq5r5uDRTq664z92Y+fqJA9321gnVhAAAAOC8EMOA8NQ7z1bIne2nisCus2nel5uhNStQDAADgPBDAgAvUpVFImbbPl+zRyr3HbDAaAAAAVCcEMOACuTg76ZO74lQn0FPv3d7ObL9x/BJl5xXacGQAAACwdzbbiBmozq6OCdPVMSWbez/07WqzfdXeY5q5PlnNwn01NKF+hRU3AQAAUDMRwIC/6V9XN9GbiSVrwB76dpUyc0tmwTzdnDX4irq2HBoAAADsDI8gAn/Twz0b6x+doyXJDF+SNH9bqgqLinUkM9dWQwMAAICdIYABl0CzCF9JkouTRfd0KQlj2w9n68npG9ThlT80cdFuWw4PAAAAdoJHEIFLYECrCK3Yc1S9Y8LVLMJXExbt1vYj2dp+JFuS9MIvmxQT6afYeoFydebfPQAAAGoqAhhwCXi7u+j1m9pIkoqLDXm6OutkQZFVn1s//ktebs56YWBL3RRbxxbDBAAAgI3xT/HAJebkZFHzU48kStLT18SodR1/SdKJ/CI9MmWt3v1juwqLis0+2w9n6cfVB2UYRpWPFwAAAFWHGTDgMhjevaHu+2qlJOmm2Dr6R+f6yjhZoCETlmn9wQy9mbhNEQGecnNx0oBWEbr6rQWSJH9PV/VoFmrLoQMAAOAyIoABl8HVMWF67cZWCvRyk7+nqyQpwMtNT18To1s+SpIkPTJlrSRpx6l1YpK0ev9xAhgAAIAD4xFE4DKwWCwafEVd9W4RbtXeITpID1zZ0KrtnT+2m987sW8zAACAQyOAAVWsdqBnhceOZOVV4UgAAABQ1QhgQBWrE+hV4bGUDDZtBgAAcGQEMKCK1alkBmzuliN6+scNys4rvOjz707L0bRVB1RcTEVFAAAAe0MRDqCK1Qn0VIiPm3ILivXioBbydnPRpuRMvT2nZC3YV3/tVb1gL90cF6Xr3lukomJDvZqHaUzvJvLzcD3n+Xv8Z54kyWKRrm/HfmMAAAD2hAAGVDF3F2f9PqqbJCnYx12S1KNZqH5Zl2xWRNyUnKmknWnam35CkvT5kj06ePyk2tcNVGpWnp4a0FxO56jYsXTXUQIYAACAnSGAATZQGrxKuTo7adJ9nXTXhGXalJypjQcztf1wtlWfxE2HlbjpsCSpV0yoEhqGVPoZFgslFQEAAOwNa8AAOxHi4673bm8nSdp6OEvrD2ZIkl6/qbVev7G1Vd/V+45Lko5k5arojLVehUXF5vfkLwAAAPtDAAPsSP1gb3Pj5lLxDYJ1Y6z1o4RLdx/VL+sOqeMrf+h/Z+wjdvxkgfm9QQ0OAAAAu0MAA+yIk5NF/Vud3rx57r+6KyrIS85OFt3Woa7Zvmx3uh76drUMw3oj52M5+eb3f6eSIgAAAC4PAhhgZx7p3VTdm9TSK9e3UoNaPmb7s9fG6McHO8vf01W5BcVW7xk/b6cKiop17MTpGbDjJ/IFAAAA+0IRDsDOBPu464t/dCjT7uHqrLZRAbqifpDmbD5sdey137Zo5d5jVu0ZJwuUlVuggiJDe9NztDk5S7d1iKI4BwAAgA0RwIBq5ob2tTV3y2Gdvc/y2aHs2Il83fxhko5k5enoqUcTA71c1a9VRFUNFQAAAGfhEUSgmunfKkJJj/fUn49cWWm//UdPaktKlhm+JOmvXemXeXQAAACoDDNgQDUU5udRbnvfFuE6lHFS6w5klHt8w6HMyzksAAAAnAMzYEA1dl+3BnKySFP/L0EL/91DHw6J1YyHuujRPk3L7b9y7zE9OmWt8gtLinis2X9c/f+3UAu2pVblsAEAAGosi2GwW9DFyMzMlL+/vzIyMuTn52fr4aCGMgxD2XmF8vVwLXOs//8WalNypuoEeqpxqI+O5uRr7amZsbZRAXrjptYaOnGZDmXkSpJeGtRS9YO91aVxSJVeAwAAQHV3IdmAAHaRCGCwd8dP5Gv+tlRd0zpSzk4WZeUWqNVzs83jrs4WFRSV/c+/a+MQ/btPM7Wq41+VwwUAAKi2LiQb8Agi4KACvNw0sG1tOTuVlJ339XDVh3e2N4+XF74kaeH2NF373iLlFxbrtw0pyji1t9jKvcc0n0cVAQAA/haKcAA1SNuowPPu+/6fO/S/P7aXaZ/xUGe1rhNwCUcFAABQcxDAgBok3N9DI65qpNzCYmXlFmrKiv0qPHtDsVPKC1+SNH31QQIYAADARWIN2EViDRgcQWFRsV6ZuUUTF++WJHVrUuucFRF93V30++huigzwrIohAgAA2D3WgAE4Ly7OTropto75+tO74jQ0vl6F/dvVDVBWXqE+WbhLhzNzNXryGl3/wWKt3X9ckrQ3PUeFRcWXe9gAAADVFjNgF4kZMDiSPzYflqebsxIahig546Tix80t02fyfZ2UkpmrkZPWlHuOlrX9tOFgpkZc1Uhjepe/DxkAAIAjYgYMwAXp2TxMCQ1L9v+K8PfUt/d2NI9d0zpC65/rrY4NgtWydsWl6TcczJQkvTN3x+UdLAAAQDVGEQ4AZSQ0DNELA1so0t9TvWLCzPboYG/5uLsoO6+w0vd3eW2uvNycFRPhp5G9mig6xFuSVFRsKL+wWEt2pql7k1pycebfgAAAQM1CAANQrrvi65dpc3KyqH29QLNQxw/D47V8zzG99tsWq34Hjp2UJG07nK0f1xxSTISfDEl5hUXalZojSXqsXzMN797wsl4DAACAveGfnwFckFuviDK/bxLuq/u6NdDs0d20e1x/vXpDq3Lfsyk5U5uTM83wJUmvztqiR6aslSQdOn5S+YXFSs/O05uztyo1K8/q/X9uOaL3/9whlqwCAIDqjhkwABekT4twDY2vpxAfd/l5uEqSmoT5SpIGtautL5P2alNy5nmd64eVB+Tr4aLPFu/RTbF1dCK/UDPXp2jWhhT9/FAXebo5S5Lu/ny5JKl1HX91bVzrMlwVAABA1aAK4kWiCiJQMcMwlFdYrGW7j2rM92vUuk6A5m45oiub1lLnhiF6eebmc56jfrCXpj3QWTl5her6+p+SpFeub6XbO9Yt0/f4iXy9O3eHboqto+YR/PcIAACq1oVkAwLYRSKAAefPMAwt2J6mdnUD5OfhqsOZuerxn3k6kV90QecZ3auJRvZqXKb90SlrNWXlAfm4u2jD830u1bABAADOC2XoAdgVi8Wi7k1qmY8shvl56L+3tJUkPdijoW7rYD2rVTfIq9zzrDtwXCv3HpVhGDIMQ8XFJf9+NP9UUZBzVWcEAACwNdaAAbCJvi3DtezJnqrl464/Nh/Rd8v2SZJu6xClx/s3V+vnZpd5zx9bjuiPLUd0W4e62nEkSxsPZeqpATEqLD49kV9QVCzXM8rb5xcWa+b6ZHVsEKQIf8/Lf2EAAACVYAYMgM2E+nrIYrGoc6MQs21g29ry83DVK9eXX1FRkr5btk/L9xzTifwiPTF9vY7m5JvHHvhmlZJ2pksqCV93TliqUZPX6LkZGy/fhQAAAJwn1oBdJNaAAZfWgm2pOpyZq5vjTpe533EkS/3+t1DdGtfSH1uOnPe5wvzc9cPwBN39+XLtOJJttn88JFa9W4Rf0nEDAABQhKMKEMCAqpGVWyAvNxcl7UzXqMmr9VCPRtqRmq3WdQK080i2PlqwS/WDvVTL113L9xw75/lmjuiqmMhz/zd7Mr9IhcXF8j21bg0AAKAiF5INWAMGwK6VBqAujUO04qmrrY6dyC9U6zoB6t60lrzdnLVge5qGTlxm1eezYVeY+4hJ0o9rDqp2gKemrT6gqEAvhfi6q21UgA4dP6k3ft+q+AbB6tMiXAPfX6TM3EL9/HAX1Q64uLVjOXmFslgkLzf+qAUAACWYAbtIzIAB9mn05DWavvqgpJK9xOb+60pd+94ibTx0enNoT1dnnSwoKYHv5uykJY9fpff/3KHPFu8paXNxUn5hsSSpZ7NQfTo0ThaLRZK0/kCGvvprjx7p01Shvh4VjqOwqFjd35gnSZr36JVWhUEAAIBjYQYMQI31RP/mahsVoJhIP9UJ9JSTk0Xv395eP689pI8X7lJWbqEZviQpv6hYczYd1roDGafbToUvqaTy4l0Tl+nmuCh1jA7Ste8tklQyq/VE/+YqLC7WyfwiebuX/HHq4eosSTp4/KQOHj8pSdqTlqPGYb6X/doBAID9YwbsIjEDBlQ/M9Ye0uNT12lIfH3VDvTU0z9ukCS1rO2nLclZVuXsJWlUr8Z6e85287WPu4vVXmO+Hi7Kyj39OtLfQ7+N7iY/D1ct2Zmm2z9Zah776cHOahMVcJmuDAAA2BIbMQNAOa5rE6kNz/fRY/2aaUinelr82FXycXfRhoOZKiw25Ovuonu7RkuSmob5alSvJvp1RBc1rOUtqexGz2eGL0k6lJGrX9clS5IOHDtpdeyZnzZcrssCAADVCAEMQI1SupZLkmoHeOrd29rJ89Rjgw/3bKRH+zTT09fE6KMhsZKkFpH+mnx/vNU5PFyd5FbBmq4Zaw5Jkg6eFcD2pJ+QYRiat/WIbhq/RD+tOWge23Y4S4eOW/cHAACOiTVgAGq0Hs1C9ce/uis9O1+t6vhLku7pEm3VJ8THXVFBntp/tCQk/fxQFzUO81XGiQL1eHOe1UbQf+1O16HjJ8vMgGWcLFCzp39T3qn1ZSv2HlP7uoFate+YRk1eI2eLRc9e10JDOtWTJBUVG1q595ja1Q2ggAcAAA6ENWAXiTVgQM3y/p879ObsrWpdJ0BT/y9Bzk4lM2lp2Xlauuuofl57SNuPZGlnao6CvN107ES+DEP6361tlbQzXZOW7z+vz2lfN0D3dWuoVfuO6eMFuzS6VxON7NVYkmQYhk4WFJ2zrL1hGJq1IUUZJwtUUFSsW+KizOIgAADg0mMj5ipAAANqnsKiYjk7WaweYzzT+3/u0Bu/bzVfNwv31bQHEnT8RIEGf5ykEB93jbuhldbsO67Hpq03+yU0DFaTMF99vmRPuefdPa6/UrPzdOtHf2lXWo4e69dMC7alavTVTeTt5qJvlu7Vv/s2k79nyZ5p7/6xXW8mbjPff2/XaD05IOYS/AQAAEB5CGBVgAAG4GyzN6bovq9Wmq/XP9fb3Ej6TIeOn1TCq3MlSbH1AvXxkFgF+7hrb3qO3vljh/7alW6WsJekQC9XDWgdoa//2lfhZ1ss0vf3x6t+sLdu/ThJO1NzrI7XDvDULXFRGtGzUYUBcv62VL3x+xa9eXNbNQ0vWzbfMAy9+tsWNQzx0S1XRFX+wwAAoAYhgFUBAhiAs+1Jy9GV/5kn6dyzTm/P2abjJwr0zDUxcnKyDkTFxYbWH8zQa79t0ZKd6Zd0jKWPNBYWFStpV7ra1w3UifwiBXu7qcETMyVJDWt5649/Xamdqdlyc3ZSVJCXJGnFnqO66cMkSSWzchUFOQAAaho2YgYAGygNKlJJ4Y7KjOrVpMJjTk4WtYkK0Nf3dNTmlEwNen+xCoou7N/KnCxS/WBv7Uqzngl7a842Rdfy1tr9xzVh0W6z/YErG5rf70zN0YPfrNKv65NlsUg7X+4vJyeL0rJPFxtJy85XLd/KrxEAAJRFaS0AuEScnSy6oX1thfq666bYOn/7fE5OFrWI9Ne1bSLNtvrBp0NevTO+P1uPpqGa+8iVZh93Fyfd2amuJGnEd6utwpckfTBvp9XrX9eX7GdmGNJvG1NkGIbSc/LM4weOndDhzFx9OH+nTuYXXeQVAgBQ8zADBgCX0H9vaXvJz/lon6b6cfVBebm5qG/LCH04vyQszXiwi7zdndXoyVmSpP6twjVzfYokqV+rCEnSF3d30Ph5OxVXP1DXt6utA8dOat7W1Av6/Ae+WaV3b2un5OO5ZtuBYyc1ctIa7Tt6Qgu3p6pRLR/tTM1R50Yh+r8zZtMAAIA11oBdJNaAAahK+9JPqKC4WNsPZ2n416skSXteHSBJ+n1jio5k5alPTJi6vv6nLBZpzTO9yy09X1BUrNX7jmv66gOK9Pc0qyV2jA6SIWn5nqP6u/+vsPzJXhU+nmgYhn5YeUB1g7zUsUGwJOnZnzZo1oYUhft76OlrYrQlJUv9Woaf8zFOAADsBUU4qgABDIAtFBUbem7GRrWs7afBV9Qtc3zb4Sx5uTmrTmDFjyeeaeXeY/L3dFWjUB9J0jM/bdCXSXv/9jgbh/ro3q4N1LVJiH5ac0jt6wbqpV83KTrEWz+tOSRJeu/2durRNFQtnv29zPvj6gXqh/9LkGEY+mLJHmWcLJSfp4ti6wWqdZ2Avz0+AAAuJYpwAICDcnay6MVBLSs83iSsbPn4ysTWC7R6HeHvaX7fNipAa/YfP6/zPH1NjHamZuvbpSWl8rcfyda/p64r02/dgQzz+5/WHKpwvCv2HpMk/bn1iJ77eZPVsVa1/fVE/+aas/mwVu87ponDrlCAl5t5fPmeo3pq+ga9OKilgn3cFBXoJTcXJ83flqrdqdka1jna7JuTV6jCIkP+XmW3CwAA4HIggAEATB6up2szfXBHe13z7iIdzSmpftitSS01j/DV6F5NdM27i1RsGKoT6KVdqdm6sX1tpWXnafbGw0rLzqvo9FYSNx1W4qbDFR6PfTFR6TmnKy92ahCkpbuPav3BDN32yV9m+1uJ2/T8wNOhdPTkNTpw7KRu+aikZP793Rro0T5NNXTiMklSswg/fbZ4t/IKi7V891G5ODtp5siuqh1wOnwCAHC58AjiReIRRACOKD07Tz3+M08dooP16dA4FRcb5v5gC//dwyy1X1hULEOSq7OTDMOw2hOs9P9WtqRkaf/RE5JktUH1xZg4LE5XNQtTSkau/u+blVq977h5zM3ZSW2i/NUs3E8vDGyhJk/NKlO2/+eHuuja9xZJkoYl1NfnS/ZYHe/fKlzv395ehlFSYOTx6evUv1WE7uhY72+NGwBQM/AIIgDgogT7uGvZk73k5lwyE+bkZNHs0d2Uk1dotc+Zi/PpmbKzN2Qufd08wk/NI/xUVGyod0yYcguLdXuHKLWvG6ihny3X5uRMq/e9dmMrfbRgl3alWu9dVjfIS50bhUiSwv099Ny1LTTw/cXm8fyiYi3fc0zL9xxTkLdbuXum/bYx2fx+8vL9ZY7PXJ+iKSsPaMPBDHMN3OId6Qr389BVzUIllczYxdYLVDDFQQAAfwMzYBeJGTAAuHhbUjK1dv9xjZ26XpJ0W4cojbuhtSSpuNjQkz9u0HfLStaT7R7Xv0zIW77nqKatOqA+LcI17LPlFzWG2zpEqU+LcM3flqrPFu9Rz2ah+mPLkTL9BrSKUMcGQXrmp43q1TxUnw69wjx2Ir9Qb/y+Vav2HtPgK+qqRaSfwvw8FO7vUe5nGoahwmJDrs7n3oZz/9ET8nF3UaC32zn7AgBsixkwAIBdaxbup2bhfvJ0c9GERbs1smcT85iTk0VP9G+mwqJiXdc2skz4kqQr6gfpivpBkqRezcM0Z7P1WrJXrm+lWRuStXB7mtkW6OWqYycKzNddGtXSlU1DdTK/SJ8t3mMVvtycndSuboCW7j6qX9cnmxtTz9l8RKlZefLzdNGGgxkaN3OLWTBk7YGSMBnu56FZI7sqwMtVT0xfrz1pJ/R4/2by83DVq7O2aP62VH39z45lCqCc+SjnweMn1futBaoX7KVZI7uW+zMAAFRPzIBdJGbAAMA+5BYUKTUrT8kZuZq8fL8e7dPUnIHKLyzWMz9tUG5BkV65oZWmrDigPek58nJz1qheTeTq7KStKVnq8/YC83y9mofqmWtaqG6wl3r8Z552p+VU9NEmV2eL1aOPkf4eujomTF9UUNK/QS1v/TGmuzJPFurLpD0K8/PQ679v1cNXNdKQTvX0+ZI9euGXkuqP4+9or8emrdc/u0Tr4Z6N/86PCgBwmbAPWBUggAGAY8gtKFLMM7+p+NT/Gz7er5nu795QkvTcjI1mwY77uzfQR/N3lXuOtc/21oPfrNKiHWlyc3ZSflHxOT+3fd0ArTqjmEgpX3cXZeUVlvueesFe6tU8TE3DfRXo5SZnJym3oFj9W0Wc+0IrkF9YLDeXcz8SWZmdqdny83CtcANuAHB0PIIIAMB58nB1VlSQl/aml1RsjDijHP3w7g1lsUg3xdZRi0h/NQzxKXd/M39PV311TwdZLBZl5Rbozk+Xau2pPc9ubF9HU1cdkCR1qB8kd1cnLdyeVm74klRh+JKkveknNGHR7nKPfXtvR3WoH6TU7DyF+XpozYHjWrIjTQ1q+ej3jSny93TVv3o3lY+7i5wsUlp2vv75xXLtSs3R9AcT1Ci08j3kiooNLd6Rpg7RQfJwdTbbkzNOqueb8xXo5arVz/Su9BwAAAIYAABqEelnBrDIMwpohPt76NlrW5ivb46rI4ul5BHChdvT9Pac7bq/ewNJp6s/+nq46r3b2+vxaet1fbva6tokxAxgA1pHqKjYsFqbVpH6wV7ac2pM5+PFXzarWbivpq8+WGGfbYeztO5Ahga1qy1PV2czJPb67wI92b+57u3WoML3vpW4Te/9uUP/d2VDje3bzGxftvuoJOnYiQIdy8lXoLebPl24S3vTT+ipa5rrf3O2K8jbTf/sWnLu71fs1+p9x/XcdTFyd3Eu97MAwJERwAAANV6LSH/NXJ8iyXoG7GwWi0U3x0VJktrXDdTVMWFqFOpTpl9UkJe+/mdH83XPZqFatueo+rYMV7FhmOu7zlQvuGQWzsPVSTNHlBTe6PGfeed9DZuTM8uU9j/bX7tKwtK3S/eVOfbKrM3lBrCNhzL02m9btWBbqiRp/LydGt6toQ5n5apRLR8lZ+SafXekZitrf4Fe+nWzJGnp7nRtO5wtSRrYtraCvd307x9KZhBjIv00pFPJPmu7UrN1/GSBmoX7KiUjVw1qlf2ZAoCjIIABAGq8esGn9zgLO891TBaLRS0i/c+r74dDYlVYZMjTrWTGp2Etb+1MzdGAVhHq0jhE2w5n6fF+zbXtcNapGTYf5RcWy8kic23a1/d01EPfrdLxU5Ucfx/VTduPZKlfywi9+MumMptLn83ZyaKi4rLLvkvXnBmG9MbvW7R2f4bWHTiuNlEBuqNjXX23bL8Zvkq1eWG2JKlBiLd2nVGk5Mnp683AJcnq+4XbU9Wq9umf14s/b1K7qACF+LjruvcWK/vUo5cWi/TbyG5qGl75I5EVOZyZq6M5+WoeUXYNRn5hsZbuTldUoJfqh3hf1PkB4O8igAEAaryezUpmshqH+lhtMn2puDo76YxlU5p8f7xmrU/WLVdEWT2G1/KMgHJ2YYy4+oG6oV0dTVy8W1fUD1TTcF8zpDw1oLmO5uRrxtpDiqsXqJX7jmlwXJQ6NQjWqMlr9L9b2+qThbu04aD1DNnAtpH6363tVP+xXyVJ7/+50zy2cHua1aOSA1pH6Nd1yVbv33VWhcgzA9dVzUK1/mCGUrPyJEkLtqWq8IxKkflFxbrm3UVlflaGIf21K928tiOZuTqUkas2dfxlsVi0NSVLy/YcVVpWnuLqB6pr41pW7x86cZm2pGRpyvB4c6uCUg98s0pzNh9WmJ+7ljzWU85O5y7vn51XqA/+3KGbYutUODP3/fL9yswtMB+zBIDKUAXxIlEFEQAcy5n7cNmLJ6av1/fL9+vl61tq8BV1lZlboBlrDmlQu9rycbf+N1TDMLQrLUf1gryUkpmrWr7ucndxVl5hkdxdnPXDygN6ZMpaSdLQ+HpKy8nXqze0kq+Hqx78ZpV+XZ8sPw8XRQV5aeMh66DWoJa35v7rSo39YZ0mr9gvSRrUNlILt6cpPSe/3LEv/HcP1Qn01Lxtqbr7s+VqUMtbYb4eStqVbjWzV5EWkX56sEcj/W/Odm09nKUwP3eF+Xlo3al1a5Lk4mRR4zBfBXu7KcLfQ9e1jdSQCcskSbUDPPXTQ50V4uOu71fs189rD1kFyonD4vTG79t0XZtIdWwQpEenrNWjfZqqb0vripKlP5vaAZ5a/NhVVscKior19I8bNGl5yc8kcXQ3NQ67uJk7ANUbZeirAAEMAHC5GYahrLxC+Xm4XpLzfZW0R4aku+LrW7UfPH5Sf245ohvb15GHq5MmLd+vUF93tYkK0CcLdqlXTJiuqB9kFeKmPZCgdlEBslhKHm3MyS9U37cW6NCpNWE7X+kvZyeLUrPydMXLc8zPcnN20pwx3fXir5uUuOn0BtohPm4K9HLT9iPZulQahfro+eta6O7PlpfZGsDPw0WZuSWPPfp7uirjZMmjnXP/1V1hfh7yPhVwS2cHJWnPqwNUVGxo3MzNalXHX9l5hXpy+gbzeHyDYDUN99Xj/ZvJxcnpvGbYADgGAlgVIIABAGqavek56v7GPEnSlhf7WpWjl6TRk9eYVRj3vDrAbD8zxLw4qKWGdKqnk/lF2pWWrSkrDqh3TJgSGoVYnf/sza3PdH/3Btp0KNNqRqtD/SAt21NSZMTD1Um5BWX3YhvYNlJebi76blnZIiRncnW26It/dFB8g2BFPz7TbN/z6gDNWp+s//tmlSSpe5Namn/W+jhJcrJI7i7O+vKeDmUegzzT5uRM7UnLUb/z2MetoKhYKRm5igryKtPueuqx2cKi4svyCC2Ac2MfMAAAcMnVC/bWh3fGytvduUz4kqTH+jXTtsNZurVDXav2Hk1r6c+tqbqtQ5RZ+dDTzVktIv3V4rrT697qBXvr9Rtby8/TRWnZ+Xrqx5LZpRAfd6Vll6wlu7tzfT3Wt5kyThao7QuJkqR/dI7Wv/s21VtztmnToUwNS6ive75YYTWGjtFB+t+t7TR/W+o5A1hBkaF7v1ihnPwiq/bFO9LM0v2SzPD1r6ub6M3EbWZ7sSGdLCjS/V+t1HPXtZCTRdqSXFJgZXNyptbsz9CnQ+M06P3FkqTpDyRo9b7jyi8qVkLDYLWq7a/8omLd/dlyBXi56ulrYnTrx39pb/oJ/feWNrqhfR1J0qcLd+mN37fq9Ztaa+H2NM3ZfFifDbtC7eoGmmMpLCqWxWIpMxv3/p87lLQzXePvbC/fSzTDCuD82HwG7IMPPtAbb7yh5ORktWjRQm+//ba6du1aYf/58+drzJgx2rhxoyIjI/Xvf/9bw4cPt+ozdepUPf3009q5c6caNmyol19+Wddff715fMGCBXrjjTe0cuVKJScna/r06Ro0aNAFjZsZMAAAzs+BYye0YFuabo6rY87WnMvJ/CK9+OsmJTQM1pVNQzVh4W7dHFdHkWdsE9D6ud+VmVuob//ZUQmNQsz2omJDDZ+YaXW+YQn19dx1LXQ4M1cdX/lDUkmwe/2mVtp0KFMdGwTr5g+TLvjaGoR467dR3XTNuwutipCcS+0ATx08flKS1DsmTLPPeBxzxFWN1CYqoEyIlKQ2UQHq1CBIHaOD9I/Pyx73dHXWwrE9lJ6dL09XZ90wfrFa1wnQxGFXSJKO5eTrn1+u0Mq9xySpZH1hXJRcnJ2UmpWnzNwCNazlI8MwNH9bqtrUCVBqdp7e/3OHRvRsrIbnsUVAYVGxnCwWFRnGef++gequ2jyCOHnyZA0ZMkQffPCBOnfurI8++kiffvqpNm3apLp165bpv3v3brVs2VL33nuv7r//fi1evFgPPPCAvvvuO914442SpKSkJHXt2lUvvviirr/+ek2fPl3PPPOMFi1apI4dS/ZkmTVrlhYvXqz27dvrxhtvJIABAFAN7T96QrvTctStSa0yx7q9/qf2HT29kfWEoXHq2TxMhmGYjxU2DfPV76O7mX0mL98nD1dnfTR/lzadY0+169pEqmm4r26Ji1ItX3elZ+dp+uqDurJpLeUWFCvI200Jr869RFf699UO8JS7q5N2peaUOebu4qRv7+2kf/+wVvuPntRvo7oqaVe6npy+QX1ahGlrSpb2pJ9Q41AfJY7pLqlkfeLhzDwFertaVfLclZqtge8tVlZeoQK9XPXjg51VL7jikv9fLNmjtOw8PdijkUZ8t1rRId56rF8zJW46rNZ1AhR+xsbof1dWboFcnZ3Knb0F/q5qE8A6duyo9u3ba/z48WZb8+bNNWjQII0bN65M/7Fjx2rGjBnavHmz2TZ8+HCtXbtWSUkl/2o1ePBgZWZmatasWWafvn37KjAwUN99912Zc1osFgIYAAAO5uDxk9p8KFNXNQvV4axcRfifnjlr+8JsHT9RoNG9mmhkr8Zl3jtm8hpNO7WWzckiPdG/uX7bkKIVp2aNJOs1bhX5MmmPnvlpY5n29nUDVFRsKL/IMDfP9nJz1qhejfXXrqOau+WI2ffs2bGqMCyhfoX7yv3ycBeNm7VZGw5mKuNkgUJ83PX7qK4K9inZP+/RKWs1ZeUBs/81rSP0+k2t9dKvmzWgVYQ6nzFTeSQrVx1eLpmN7BgdpKW7S9bwxUT4aVNypno2C9WEUzN3e9Nz9PpvW9UhOki3dairozn5CvNzP+/KpckZJ9X7rQVqGuarKcPj7a7iKaq/arEGLD8/XytXrtRjjz1m1d67d28tWbKk3PckJSWpd+/eVm19+vTRhAkTVFBQIFdXVyUlJWn06NFl+rz99tt/a7x5eXnKy8szX2dmVv4vYwAAwHZqB3iq9qnHFc8MX5L0w/AEzd+WqqHx9cp971XNQ80Atu2lfnJxdtI/uzbQmO/XaNqqg3qif7PzGsNd8fU1pFM9fbN0n6KCvPTk9PUK9nbT1P9LkMViUWZugfq9vVC5BUV67cbW6hUTpvu6NdSi7Wl678/tuik2SlfUDzQD2IiejfXu3O0685/O7+/eQAWFhlxdLOrcMER3TVxW6Zi6Ng7RUwNi1LCWt3q8OU/7j54s06eyTb3/+cUKpWTmmq/TsvM0afl+PdijkQzD0J9brYuSzN50WLX/2K5vl+7Tt0v3ad1zvTVlxQFF+nuYlSclmeFLkjn7+MeWIzIMQ4XFhu77cqW2Hs7Sr+uT9eyMklD7n5vbyN3FSV5uzvL3dFVc/SDtSz8hd1cnhflZz5zNWHNIWbmFWrH3mD5fskc9moYq0NtND327Sl0ahej+7g3NvskZJzV5+X7d0yVax3IK9PacbfLzdNU/u0ZryIRl6h0Tpsf7N6/051yZzNwC/bUzXT2bh1Eps4ayWQBLS0tTUVGRwsLCrNrDwsKUkpJS7ntSUlLK7V9YWKi0tDRFRERU2Keic56vcePG6fnnn/9b5wAAALbXKNRHjUIrXss0oFWE8m4uVkykn1VVwZcGtVS/lhG6qlnoeX+WxWLRnacKj/z5yJWynGqTJD8PVy0a20OGITmd8RfxLo1D1KVxyUyRYRi6sX0dZecVaGTPxhrYNlL+nq4K8XFXUbFR5i/we14doMnL9+nQ8VzN2XxYGw9lamDbSPVoGqpGoT5Wm31Pvi9e7/25Q39sPqzDmXlW5/F1d9HVMWFmEC2VkpmrSH8PPT+wpfam5+ilXzfrjd+3asWeo+rbMtwslhLs7ab0nHzlFxZrwsLd5vufmLZev5y1oXdlXv51s66IDtLWw1lljpVuiSCVbG8w+uomev33LZKkR/s01ZbkLN16RZQSGoXo1/WnP/P5nzfp3bk7dHdCfXPD8VuvqKuC4mKtP5ihp6Zv0MHjJ/X2nO1Wn7dkZ5p2p+XoowW7dE+XaLk4OynI2+28r6XUuJlb9N2yffq/KxtqbF/rMG8Yho7m5JszipK0Oy1HEf4ePDrpQGxeBfHsKeBzbYRZXv+z2y/0nOfj8ccf15gxY8zXmZmZioqK+lvnBAAA9sdisejG2Dpl2r3cSkLJxSqvIIXFYlFlf0WxWCx685Y25uszi2BUNHsy+IqSdfQjejZWQVFxhX9xjwzw1CvXt9L2hPoa/PFfGtS2tm7vGKUP5+/SnZ3qqW1UgPak52jVvuNW73vlhla6smmoMk4W6KVfS5aF/Lk11Zz9ah7hp1kju+qpH9fr67/2qfCMXbfPDl9nVriUSh5xbBbuq/hX5yo1K0+fLtqtTxeVBLhhCfW1JSVTf+06qrPlFxXrtd+2mK9f/22rJGnG2kP6aEis1h3IsNoA/GhOvj5asMvsP2Xlfs3akGIWJynPmUVWOpwq5PLCwBa6vl1trdh7TF0ahSg7t1B7j55Q26iAcs9hGIZZhXP8vJ26u3N9JR/PVes6/tqZmqMJi3bpu2X79d7t7XRN60gt2ZGm2z9dqmtaR+i929tLktYdOC4/D1fVDylZWzdn02FtTs7UAz0amfdETl6h/vH5csU3DNaoXk0qHMvbc7braE6+7u3aQHWDvcrth0vPZgEsJCREzs7OZWamjhw5UmYGq1R4eHi5/V1cXBQcHFxpn4rOeb7c3d3l7u5+7o4AAAB2wNnJImenc8+aNA7z1aqnrzZf/+fm04FvYNvaZgBrHuGnq5uH6sqmJTOA/p7ll69vHuErSRoaX18/r022etTwbGP7NtWjP6wzX7eI9JPFYtEN7Wvro/mnA5Kbi5Pu7FRPjUJ9lJNXqBbP/n7O6yp1/1crJUkJDUMUFeRlBqDsvMLT1zx7a7l7x53Lu3N36KP5u3Tw+En935UNNXXlAR3JypOXm7Oev66FDhw7qY4NgpTQsGRG88y96ySp53/mKyuvUPENgrXuwHFz64NRk9bomtaR+nhhyc/gl3XJGnxFqhbvSNeH83fKz8NF8x/toUBvN/3zy5JqmH6eroqtFygXZ4v2pJ3Q0t1HtXT3Ub09Z7uubROpt25pIxdnJxmGoc8W79H2I1n6btl+SdLeoyf05T86XPD1n6mgqFgTF+1Wl8YhahHpf+431GA2C2Bubm6KjY1VYmKiVYn4xMREDRw4sNz3xMfH6+eff7Zqmz17tuLi4uTq6mr2SUxMtFoHNnv2bCUkJFyGqwAAAHBcQzrVU35hsdrXC1BsvbKbSgd5u+loTr5VW+lfvhuH+er3Ud00e1OKGof66pEpa83S+04WKdTXQ9e2idSutBxzNqj0iaV/Xd1UN7Wvo8zcArk5O6tusJcZ+LzdXXRl01qatzVVQd5uWvDvHmp5KpC1iPRTqK+7FmxP0/91b6iZG5LNyo+3dojSgFYRqhfspVdnlcyWRQV5KszXw6rAypnaRAUoJsLPDG2D2kZq4fY0pZ+65tSs07N34+ftNL8/kV90Olj+IdUL9tLxEwVlwmjWqRCYtCvdqt3JyaLiYkMbD52uOTBkwun1fZm5hZqwaLce6HF67dq4WZuVW1AsH3cX/aNLtNX5fl57SAu2perertFydXbSuFlbrI4v252u/MJiuThZ9EXSHtUO8DQLpni7W8eFomJDx0/ka/T3a9WzWaiGJtSXJH28oGRfOt+5Lvp0aJz2HzupbYezdF2bSMVE+Fk9ZlvqZH6RVu8/ppP5RYoM8FTziJpR2M4uytB/+OGHio+P18cff6xPPvlEGzduVL169fT444/r4MGD+vLLLyWdLkN///33695771VSUpKGDx9uVYZ+yZIl6tatm15++WUNHDhQP/30k5566imrMvTZ2dnasWOHJKldu3b673//qx49eigoKKjc8vfloQoiAACo6ZbsTNP/fb1Kr97QSt7uLkrcdFj/6t1EAV5l10bN3pii+75aqeYRfvp0aJxcnS0K9fXQyfwi/bU7Xd0a1zrvohR5hUWasuKAGof6qGODYL06a4s+nL9TE4fFqWvjWsrKLVSQt5uOZObqzdnb1KlhkK5vV/JYaUpGrjqNK3mE8B+do9UrJlS3f7K03M/5aEis+rQI1/bDWZq5PkX3dotWQZGh1Kw8Pf/zxjIzWpLUoX6QcguLtO6MTbvP1DTMV3WDvZRYSXVLZyeLJt/XSTedY2+61nX8y/2cEB83pWXnl/OOij3Wr5l+XnvIDH2lRWz++Fd38zHWNfuP67aP/9LJgtOblCc9fpU++HOnvvprb6Xn/2F4vOLql4T4XanZWr7nqBbvSNeMtYes+vWOCdNHQ2KrXaXKalOGXirZiPn1119XcnKyWrZsqbfeekvdupXsyTFs2DDt2bNH8+bNM/vPnz9fo0ePNjdiHjt2bJmNmH/44Qc99dRT2rVrl7kR8w033GAenzdvnnr06FFmLEOHDtXnn39+XuMmgAEAAFyYpbvSFRngqaigS7veqLjY0JGsvPPeN2zJzjR9s3Sfnr0mRqF+Hvpm6V79sjZZ3ZrUUqcGQTqSlacdR7L1wJUNKwwCX/21V0//uKFM+7xHrlT9EG8t2JZqVZXyujaR2pWWrbcHt9PiHWlmNUdJ6tU8VJEBntp4KLPMOrTeMWE6nJWntfuPS5L+d2tbvTZriw5l5OpcxlzdRG2iArT+wHH9Z/a2cvv0axmuWRsqLlbXqra/Gof56JXrW+nRH9bp57MC04Xo3qSWbusQpUd/WKes3MIK+w1oFaHH+jXT4cxcbU7J0uC4KLm5WK+h/GtXulbuPab/696w3Nm1qlatAlh1RQADAACouc6cSSvl5easjc/3MUPb98v3a+y0dXrjpja66YzCLot3pOmOT0tm3ZqF++q3Uac3BP/3D2v1/YrTe6n95+Y2Gtg2UkXFhjkTNX9bqv7z+1atP1j+LFupHx/srLZRAVq596huHG89m+bp6qxXbmiplIw8s4DJ2UVRzjQ4LkrT1xxUfmGx2kYFaM2pQHguY65uov8mlh/+Srm7OCmvsOI1eNEh3rorvp7u7FRPJwuKlF9YrLiX5kgqCaQD29Y+r7FcTtViHzAAAACgugr399CHd8aq2DDkZJF+25Cia1pHWs2Y3XJFlK5rG1mmEmVsvUDz+91pOVbHHriykWZtSJGLk0XDEqI1sG2kXJ2ddOYpujeppe5Naqn+Y79KKtla4bYOdTWwbaTyCov16JS1OpFfZBZEKa8oxuYX+0qSFp3xGOU/utSXYUhv/L61TP/JK0oKdiQ0DNY3/+yoJ6ZvMNfGlbq3a7Q+OWPbgaevidE9XaIV7uehOZsPW20q/mifpmoU6qNWtf3l5+mqXanZ+mPzEe1Nz9HB4ye1cu8xs2rl7rQcPf/zJs1an6INhzJ0Iv/0I5A7U61/ftUBAQwAAAC4CH1bhp/xfUS5fcrbBsDD1Vm3dYjSd8v266EejayO1Q/x1vxHe8jH3aXMY3dn+2hIrJ79aaNeu7GVVZGUb+/tVObzfhvVVX3fXiipZB1aqZa1T8/WtIz0V5s6ATqak69+LcM1efl+XdUsVP86Fegk6X+3tpPFYtHVMaFmAOvZLFSNw3w1qldjNQnz1e60HN3RqZ4iTz0SessVUbrliigNnbhM87eVbFfQpVGI2pxRrr91nQC1rnP69f6jJ5S0K13Pz9hoVodctqfsFgQHjp2o9GdkjwhgAAAAQBV7aVAr9W8VoSvql19d8nz0aRGuPi3Cz91RUrNwP017IEFvJW7TiwNbmu0BXm66rUOUWTLf3cVZT18TI0lm0QxPN2eNnrxGTw2IUS3fkm2ZujauZZ7j7s7R5ubhN8dVvE9uv5bhZgBrGu5bYT9JigryUlSQl3zcXfTAN6skSZ0bBWtv+gkdOHbS7Lf9jP3ZqgvWgF0k1oABAACgJluz/7i2pWTp5rg651W1MK+wSI9MWafGoT4a0bPxeX9OenaegrzdZLFY9Mu6Q3ro29XmMU/XknV3ti7EwRowAAAAAJdV26gAtT3jMcJzcXdx1ru3tbvgzwn2cTe/7x0Trts61FXbKH/9tiFFDWv5KLewSF5u1SfWMAN2kZgBAwAAACBdWDaofGUfAAAAAOCSIYABAAAAQBUhgAEAAABAFSGAAQAAAEAVIYABAAAAQBUhgAEAAABAFSGAAQAAAEAVIYABAAAAQBUhgAEAAABAFSGAAQAAAEAVIYABAAAAQBUhgAEAAABAFSGAAQAAAEAVIYABAAAAQBUhgAEAAABAFSGAAQAAAEAVIYABAAAAQBUhgAEAAABAFXGx9QCqK8MwJEmZmZk2HgkAAAAAWyrNBKUZoTIEsIuUlZUlSYqKirLxSAAAAADYg6ysLPn7+1fax2KcT0xDGcXFxTp06JB8fX1lsVhsOpbMzExFRUVp//798vPzs+lYUD1wz+BCcc/gYnDf4EJxz+BC2cs9YxiGsrKyFBkZKSenyld5MQN2kZycnFSnTh1bD8OKn58ff1jhgnDP4EJxz+BicN/gQnHP4ELZwz1zrpmvUhThAAAAAIAqQgADAAAAgCpCAHMA7u7uevbZZ+Xu7m7roaCa4J7BheKewcXgvsGF4p7BhaqO9wxFOAAAAACgijADBgAAAABVhAAGAAAAAFWEAAYAAAAAVYQABgAAAABVhADmAD744ANFR0fLw8NDsbGxWrhwoa2HBBsYN26crrjiCvn6+io0NFSDBg3S1q1brfoYhqHnnntOkZGR8vT01JVXXqmNGzda9cnLy9PDDz+skJAQeXt767rrrtOBAweq8lJgI+PGjZPFYtGoUaPMNu4ZnO3gwYO68847FRwcLC8vL7Vt21YrV640j3PP4EyFhYV66qmnFB0dLU9PTzVo0EAvvPCCiouLzT7cMzXbggULdO211yoyMlIWi0U//vij1fFLdX8cO3ZMQ4YMkb+/v/z9/TVkyBAdP378Ml9dBQxUa5MmTTJcXV2NTz75xNi0aZMxcuRIw9vb29i7d6+th4Yq1qdPH+Ozzz4zNmzYYKxZs8YYMGCAUbduXSM7O9vs8+qrrxq+vr7G1KlTjfXr1xuDBw82IiIijMzMTLPP8OHDjdq1axuJiYnGqlWrjB49ehht2rQxCgsLbXFZqCLLli0z6tevb7Ru3doYOXKk2c49gzMdPXrUqFevnjFs2DBj6dKlxu7du405c+YYO3bsMPtwz+BML730khEcHGz88ssvxu7du40pU6YYPj4+xttvv2324Z6p2WbOnGk8+eSTxtSpUw1JxvTp062OX6r7o2/fvkbLli2NJUuWGEuWLDFatmxpXHPNNVV1mVYIYNVchw4djOHDh1u1NWvWzHjsscdsNCLYiyNHjhiSjPnz5xuGYRjFxcVGeHi48eqrr5p9cnNzDX9/f+PDDz80DMMwjh8/bri6uhqTJk0y+xw8eNBwcnIyfvvtt6q9AFSZrKwso3HjxkZiYqLRvXt3M4Bxz+BsY8eONbp06VLhce4ZnG3AgAHGP/7xD6u2G264wbjzzjsNw+CegbWzA9iluj82bdpkSDL++usvs09SUpIhydiyZctlvqqyeASxGsvPz9fKlSvVu3dvq/bevXtryZIlNhoV7EVGRoYkKSgoSJK0e/dupaSkWN0v7u7u6t69u3m/rFy5UgUFBVZ9IiMj1bJlS+4pB/bggw9qwIAB6tWrl1U79wzONmPGDMXFxenmm29WaGio2rVrp08++cQ8zj2Ds3Xp0kV//PGHtm3bJklau3atFi1apP79+0vinkHlLtX9kZSUJH9/f3Xs2NHs06lTJ/n7+9vkHnKp8k/EJZOWlqaioiKFhYVZtYeFhSklJcVGo4I9MAxDY8aMUZcuXdSyZUtJMu+J8u6XvXv3mn3c3NwUGBhYpg/3lGOaNGmSVq1apeXLl5c5xj2Ds+3atUvjx4/XmDFj9MQTT2jZsmUaMWKE3N3dddddd3HPoIyxY8cqIyNDzZo1k7Ozs4qKivTyyy/rtttuk8SfM6jcpbo/UlJSFBoaWub8oaGhNrmHCGAOwGKxWL02DKNMG2qWhx56SOvWrdOiRYvKHLuY+4V7yjHt379fI0eO1OzZs+Xh4VFhP+4ZlCouLlZcXJxeeeUVSVK7du20ceNGjR8/XnfddZfZj3sGpSZPnqyvv/5a3377rVq0aKE1a9Zo1KhRioyM1NChQ81+3DOozKW4P8rrb6t7iEcQq7GQkBA5OzuXSe5Hjhwp8y8FqDkefvhhzZgxQ3/++afq1KljtoeHh0tSpfdLeHi48vPzdezYsQr7wHGsXLlSR44cUWxsrFxcXOTi4qL58+frnXfekYuLi/k7555BqYiICMXExFi1NW/eXPv27ZPEnzMo69FHH9Vjjz2mW2+9Va1atdKQIUM0evRojRs3ThL3DCp3qe6P8PBwHT58uMz5U1NTbXIPEcCqMTc3N8XGxioxMdGqPTExUQkJCTYaFWzFMAw99NBDmjZtmubOnavo6Gir49HR0QoPD7e6X/Lz8zV//nzzfomNjZWrq6tVn+TkZG3YsIF7ygH17NlT69ev15o1a8yvuLg43XHHHVqzZo0aNGjAPQMrnTt3LrO9xbZt21SvXj1J/DmDsk6cOCEnJ+u/bjo7O5tl6LlnUJlLdX/Ex8crIyNDy5YtM/ssXbpUGRkZtrmHqrzsBy6p0jL0EyZMMDZt2mSMGjXK8Pb2Nvbs2WProaGK/d///Z/h7+9vzJs3z0hOTja/Tpw4YfZ59dVXDX9/f2PatGnG+vXrjdtuu63cUq516tQx5syZY6xatcq46qqrKPVbg5xZBdEwuGdgbdmyZYaLi4vx8ssvG9u3bze++eYbw8vLy/j666/NPtwzONPQoUON2rVrm2Xop02bZoSEhBj//ve/zT7cMzVbVlaWsXr1amP16tWGJOO///2vsXr1anNLpUt1f/Tt29do3bq1kZSUZCQlJRmtWrWiDD0u3vvvv2/Uq1fPcHNzM9q3b2+WHUfNIqncr88++8zsU1xcbDz77LNGeHi44e7ubnTr1s1Yv3691XlOnjxpPPTQQ0ZQUJDh6elpXHPNNca+ffuq+GpgK2cHMO4ZnO3nn382WrZsabi7uxvNmjUzPv74Y6vj3DM4U2ZmpjFy5Eijbt26hoeHh9GgQQPjySefNPLy8sw+3DM1259//lnu31+GDh1qGMaluz/S09ONO+64w/D19TV8fX2NO+64wzh27FgVXaU1i2EYRtXPuwEAAABAzcMaMAAAAACoIgQwAAAAAKgiBDAAAAAAqCIEMAAAAACoIgQwAAAAAKgiBDAAAAAAqCIEMAAAAACoIgQwAAAAAKgiBDAAAGzAYrHoxx9/tPUwAABVjAAGAKhxhg0bJovFUuarb9++th4aAMDBudh6AAAA2ELfvn312WefWbW5u7vbaDQAgJqCGTAAQI3k7u6u8PBwq6/AwEBJJY8Hjh8/Xv369ZOnp6eio6M1ZcoUq/evX79eV111lTw9PRUcHKz77rtP2dnZVn0mTpyoFi1ayN3dXREREXrooYesjqelpen666+Xl5eXGjdurBkzZlzeiwYA2BwBDACAcjz99NO68cYbtXbtWt1555267bbbtHnzZknSiRMn1LdvXwUGBmr58uWaMmWK5syZYxWwxo8frwcffFD33Xef1q9frxkzZqhRo0ZWn/H888/rlltu0bp169S/f3/dcccdOnr0aJVeJwCgalkMwzBsPQgAAKrSsGHD9PXXX8vDw8OqfezYsXr66adlsVg0fPhwjR8/3jzWqVMntW/fXh988IE++eQTjR07Vvv375e3t7ckaebMmbr22mt16NAhhYWFqXbt2rr77rv10ksvlTsGi8Wip556Si+++KIkKScnR76+vpo5cyZr0QDAgbEGDABQI/Xo0cMqYElSUFCQ+X18fLzVsfj4eK1Zs0aStHnzZrVp08YMX5LUuXNnFRcXa+vWrbJYLDp06JB69uxZ6Rhat25tfu/t7S1fX18dOXLkYi8JAFANEMAAADWSt7d3mUcCz8VisUiSDMMwvy+vj6en53mdz9XVtcx7i4uLL2hMAIDqhTVgAACU46+//irzulmzZpKkmJgYrVmzRjk5OebxxYsXy8nJSU2aNJGvr6/q16+vP/74o0rHDACwf8yAAQBqpLy8PKWkpFi1ubi4KCQkRJI0ZcoUxcXFqUuXLvrmm2+0bNkyTZgwQZJ0xx136Nlnn9XQoUP13HPPKTU1VQ8//LCGDBmisLAwSdJzzz2n4cOHKzQ0VP369VNWVpYWL16shx9+uGovFABgVwhgAIAa6bffflNERIRVW9OmTbVlyxZJJRUKJ02apAceeEDh4eH65ptvFBMTI0ny8vLS77//rpEjR+qKK66Ql5eXbrzxRv33v/81zzV06FDl5ubqrbfe0iOPPKKQkBDddNNNVXeBAAC7RBVEAADOYrFYNH36dA0aNMjWQwEAOBjWgAEAAABAFSGAAQAAAEAVYQ0YAABn4el8AMDlwgwYAAAAAFQRAhgAAAAAVBECGAAAAABUEQIYAAAAAFQRAhgAAAAAVBECGAAAAABUEQIYAAAAAFQRAhgAAAAAVJH/BwfBuWQ07z0xAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pickle\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Find your training_'losses.pkl' file in the directory that you stored your trained model weights in\n", + "with open( \"/home/wkt7ne/PARCtorch/PARCtorch/demos/results/SF_Euler_debug_model_weights/training_losses_cumulative.pkl\", 'rb' ) as f: # replace with your path\n", + " history = pickle.load(f)\n", + "plt.figure(figsize=(10,6))\n", + "plt.title('ShearFlow Model Loss Curve')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Loss')\n", + "plt.plot(history)\n", + "#plt.xlim(99, 500)\n", + "plt.savefig('normalization_loss_curve.png')\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "591d388f-17d2-4f16-9d6c-c26e85b732a5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "start, end = 800, 1000 # epoch numbers (1-based)\n", + "start_idx = max(0, start - 1) # convert to 0-based index\n", + "end_idx = min(len(history), end) # slice end is exclusive\n", + "\n", + "\n", + "epochs = range(start, end_idx + 1) # x-axis as epoch numbers\n", + "losses = history[start_idx:end_idx]\n", + "\n", + "plt.figure(figsize=(10,6))\n", + "plt.title('ShearFlow Model Loss Curve')\n", + "plt.xlabel('Epoch'); plt.ylabel('Loss')\n", + "plt.plot(epochs, losses)\n", + "plt.tight_layout()\n", + "plt.savefig('normalization_loss_curve_100_500.png')\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (parc)", + "language": "python", + "name": "parc" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}