test2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing of ResNet+MLP where we take the ResNet output, concatenate it with a gravity vector (which the drone has via its IMU) and pass it through an MLP to get an estimate of the pose.\n",
    "\n",
    "We try out a bunch of hidden layer configurations and also compare results between networks with pre-trained ResNet weights and networks where the ResNet weights are allowed to be trained on given the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some useful settings for interactive work\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import functions.data as df\n",
    "import functions.training as tf\n",
    "from models import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize NN variables\n",
    "hidden_sizes = [\n",
    "    [256, 128, 7],\n",
    "    [128, 32, 7],\n",
    "    ]\n",
    "\n",
    "mlps = [VisionPoseMLP(hidden_size,False) for hidden_size in hidden_sizes]\n",
    "\n",
    "Neps = 500\n",
    "Ndata = None\n",
    "ratio = 0.8\n",
    "Nexp = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate data loaders\n",
    "train_loader, test_loader = df.get_resn_data(ratio,Ndata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============================================================\n",
      "Training: basic001\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "Epoch:  50 | Loss: 0.00011\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "Epoch: 100 | Loss: 0.00000\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "Epoch: 150 | Loss: 0.00000\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "Epoch: 200 | Loss: 0.00000\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "Epoch: 250 | Loss: 0.00000\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "Epoch: 300 | Loss: 0.00000\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "Epoch: 350 | Loss: 0.00000\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "Epoch: 400 | Loss: 0.00000\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "Epoch: 450 | Loss: 0.00000\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "Epoch: 500 | Loss: 0.00000\n",
      "-------------------------------------------------------------\n",
      "-------------------------------------------------------------\n",
      "Test Loss: 0.7584\n",
      "Examples:\n",
      "=============================================================\n",
      "Training: basic002\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "Epoch:  50 | Loss: 0.00021\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "Epoch: 100 | Loss: 0.00000\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "Epoch: 150 | Loss: 0.00000\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "Epoch: 200 | Loss: 0.00000\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "Epoch: 250 | Loss: 0.00000\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n",
      "torch.Size([2, 3, 224, 224])\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[8], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m=============================================================\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTraining: \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m+\u001b[39mmlp_name)\n\u001b[0;32m----> 6\u001b[0m \u001b[43mtf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmlp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmlp_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43museNeRF\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mNeps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mNeps\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m-------------------------------------------------------------\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      8\u001b[0m tf\u001b[38;5;241m.\u001b[39mtest_model(mlp, test_loader,useNeRF\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, Nexp\u001b[38;5;241m=\u001b[39mNexp)\n",
      "File \u001b[0;32m~/StanfordMSL/Image2Pose/functions/training.py:37\u001b[0m, in \u001b[0;36mtrain_model\u001b[0;34m(model, train_loader, model_name, useNeRF, Neps, Nprt)\u001b[0m\n\u001b[1;32m     34\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m     36\u001b[0m \u001b[38;5;66;03m# Forward pass\u001b[39;00m\n\u001b[0;32m---> 37\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43minputs_v\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;66;03m# Calculate the loss\u001b[39;00m\n\u001b[1;32m     40\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(outputs, targets)\n",
      "File \u001b[0;32m~/anaconda3/envs/sfti2-env/lib/python3.10/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/sfti2-env/lib/python3.10/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/StanfordMSL/Image2Pose/models.py:16\u001b[0m, in \u001b[0;36mVisionPoseMLP.forward\u001b[0;34m(self, x, v)\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m,x,v):\n\u001b[0;32m---> 16\u001b[0m     y_img \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnetworks\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     17\u001b[0m     y_out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnetworks[\u001b[38;5;241m1\u001b[39m](torch\u001b[38;5;241m.\u001b[39mcat((y_img,v),\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m     19\u001b[0m     y_norm \u001b[38;5;241m=\u001b[39m y_out\u001b[38;5;241m.\u001b[39mclone()\n",
      "File \u001b[0;32m~/anaconda3/envs/sfti2-env/lib/python3.10/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/sfti2-env/lib/python3.10/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/StanfordMSL/Image2Pose/models.py:42\u001b[0m, in \u001b[0;36mResNetReg.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m---> 42\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/sfti2-env/lib/python3.10/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/sfti2-env/lib/python3.10/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/sfti2-env/lib/python3.10/site-packages/torch/nn/modules/container.py:215\u001b[0m, in \u001b[0;36mSequential.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[1;32m    214\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m--> 215\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n",
      "File \u001b[0;32m~/anaconda3/envs/sfti2-env/lib/python3.10/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/sfti2-env/lib/python3.10/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/sfti2-env/lib/python3.10/site-packages/torchvision/models/resnet.py:285\u001b[0m, in \u001b[0;36mResNet.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    284\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 285\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forward_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/sfti2-env/lib/python3.10/site-packages/torchvision/models/resnet.py:276\u001b[0m, in \u001b[0;36mResNet._forward_impl\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    274\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlayer2(x)\n\u001b[1;32m    275\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlayer3(x)\n\u001b[0;32m--> 276\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlayer4\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    278\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mavgpool(x)\n\u001b[1;32m    279\u001b[0m x \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mflatten(x, \u001b[38;5;241m1\u001b[39m)\n",
      "File \u001b[0;32m~/anaconda3/envs/sfti2-env/lib/python3.10/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/sfti2-env/lib/python3.10/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/sfti2-env/lib/python3.10/site-packages/torch/nn/modules/container.py:215\u001b[0m, in \u001b[0;36mSequential.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[1;32m    214\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m--> 215\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n",
      "File \u001b[0;32m~/anaconda3/envs/sfti2-env/lib/python3.10/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/sfti2-env/lib/python3.10/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/sfti2-env/lib/python3.10/site-packages/torchvision/models/resnet.py:92\u001b[0m, in \u001b[0;36mBasicBlock.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     89\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[1;32m     90\u001b[0m     identity \u001b[38;5;241m=\u001b[39m x\n\u001b[0;32m---> 92\u001b[0m     out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     93\u001b[0m     out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbn1(out)\n\u001b[1;32m     94\u001b[0m     out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrelu(out)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Train the basic models\n",
    "for idx,mlp in enumerate(mlps):\n",
    "    mlp_name = \"basic\"+str(idx+1).zfill(3)\n",
    "    print(\"=============================================================\")\n",
    "    print(\"Training: \"+mlp_name)\n",
    "    tf.train_model(mlp, train_loader, mlp_name,useNeRF=False, Neps=Neps)\n",
    "    print(\"-------------------------------------------------------------\")\n",
    "    tf.test_model(mlp, test_loader,useNeRF=False, Nexp=Nexp)\n",
    "\n",
    "# print(mlp.networks[0].model[0].conv1.weight[0,0,0,:])"
   ]
  }
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