loss_landscape/plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = \"0\"\n",
    "from train_mnist import MnistMultiTask,label_remaps_dict,load_clip_features,Mlp\n",
    "from torchvision.datasets import MNIST\n",
    "from torch.utils.data import DataLoader,Dataset\n",
    "from tqdm import tqdm,tqdm_notebook\n",
    "from torch import nn\n",
    "import numpy as np\n",
    "import torchvision.transforms as T\n",
    "import torch\n",
    "from tqdm import tqdm, trange\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn import linear_model\n",
    "from tqdm import tqdm, tqdm_notebook\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pylab as plt\n",
    "import matplotlib.tri as tri\n",
    "import pickle as pkl\n",
    "from copy import deepcopy\n",
    "from scipy.interpolate import griddata\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm\n",
    "from torch.cuda.amp import autocast\n",
    "\n",
    "device=\"cuda\"\n",
    "batch_size = 500\n",
    "repeat_number = 5\n",
    "num_classes = 512\n",
    "num_worker=8\n",
    "test_trans = T.Compose([T.ToTensor(), T.Normalize(0.5, 0.5)])\n",
    "datadir = \"./data\"\n",
    "ds = MnistMultiTask(MNIST(datadir,\n",
    "                                       train=False,\n",
    "                                       transform=test_trans),\n",
    "                                 tasks=[\"prime\",\"odd\"])\n",
    "task_class_name = {\n",
    "    \"prime\": [\"non-prime\", \"prime\"],\n",
    "    \"odd\": [\"even\", \"odd\"],\n",
    "    \"normal\": [\"one\", \"two\", \"three\", \"four\", \"five\", \"six\", \"eight\", \"nine\"]\n",
    "}\n",
    "\n",
    "def get_mlp_perm():\n",
    "    return {\n",
    "        0: [\n",
    "            [0, \"fc1.weight\"],\n",
    "            [1, \"fc2.weight\", {\n",
    "                \"in\": None\n",
    "            }],\n",
    "        ],\n",
    "        1: [\n",
    "            [0, \"fc2.weight\"],\n",
    "            [1, \"fc3.weight\", {\n",
    "                \"in\": None\n",
    "            }],\n",
    "        ],\n",
    "        2: [\n",
    "            [0, \"fc3.weight\"],\n",
    "            [1, \"fc4.weight\", {\n",
    "                \"in\": None\n",
    "            }],\n",
    "        ],\n",
    "    }\n",
    "\n",
    "\n",
    "dl = DataLoader(ds,\n",
    "                             batch_size=batch_size,\n",
    "                             shuffle=False,\n",
    "                             num_workers=num_worker)\n",
    "task1_dir = \"checkpoints/mnist_clip/prime\"\n",
    "task2_dir = \"checkpoints/mnist_clip/odd\"\n",
    "exp_idx = 4\n",
    "\n",
    "prime_class_vectors = load_clip_features(task_class_name[\"prime\"])\n",
    "odd_class_vectors = load_clip_features(task_class_name[\"odd\"])\n",
    "@torch.no_grad()\n",
    "@autocast()\n",
    "def test_network(net:nn.Module):\n",
    "    net.eval().to(device)\n",
    "    loss1 = 0\n",
    "    loss2 = 0\n",
    "    acc1 = 0\n",
    "    acc2 = 0\n",
    "    loss_func = nn.CrossEntropyLoss()\n",
    "    # for img,labels in tqdm(dl,total=len(dl),desc=\"Testing\"):\n",
    "    for img,labels in dl:\n",
    "        img = img.to(device).reshape(img.shape[0],-1)\n",
    "        label1 = labels[0].to(device)\n",
    "        label2 = labels[1].to(device)\n",
    "        encodings = net(img)\n",
    "        normed_encodings = encodings / encodings.norm(dim=-1,\n",
    "                                                                  keepdim=True)\n",
    "\n",
    "        logits1 = (100.0 * normed_encodings @ prime_class_vectors.T)\n",
    "        logits2 = (100.0 * normed_encodings @ odd_class_vectors.T)\n",
    "        loss1 += loss_func(logits1,label1).cpu().detach().item() * img.shape[0]\n",
    "        loss2 += loss_func(logits2,label2).cpu().detach().item() * img.shape[0]\n",
    "        acc1 += (logits1.argmax(dim=1).data == label1).sum().cpu().detach().item()\n",
    "        acc2 += (logits2.argmax(dim=1).data == label2).sum().cpu().detach().item()\n",
    "    \n",
    "    result = {\"loss1\":loss1,\"loss2\":loss2,\"acc1\":acc1,\"acc2\":acc2}\n",
    "\n",
    "    result = {k:v/len(ds) for k,v in result.items()}\n",
    "    return result\n",
    "\n",
    "class MNISTActDataset(Dataset):\n",
    "    def __init__(self) -> None:\n",
    "        super().__init__()\n",
    "        self.ds = MNIST(datadir,\n",
    "                                  train=True,\n",
    "                                  transform=test_trans)\n",
    "    def __len__(self):\n",
    "        return len(self.ds)\n",
    "    def __getitem__(self, index):\n",
    "        img,label = self.ds[index]\n",
    "        return img.reshape(-1),label\n",
    "        \n",
    "    \n",
    "act_ds = MNISTActDataset()\n",
    "act_dl = DataLoader(act_ds,\n",
    "                             batch_size=batch_size,\n",
    "                             shuffle=False,\n",
    "                             num_workers=num_worker)\n",
    "\n",
    "neta_state = torch.load(os.path.join(f\"{task1_dir}/{exp_idx}/best.pth\"),map_location=device)\n",
    "netb_state = torch.load(os.path.join(f\"{task2_dir}/{(exp_idx+1)%5}/best.pth\"),map_location=device)\n",
    "\n",
    "\n",
    "\n",
    "model_keys = [\"fc1.weight\",\"fc2.weight\",\"fc3.weight\",\"fc4.weight\"]\n",
    "def get_1d_weight(net):\n",
    "    if isinstance(net,nn.Module):\n",
    "        net = net.state_dict()\n",
    "    \n",
    "    para = []\n",
    "    for kk in model_keys:\n",
    "        vv = net[kk]\n",
    "        para.append(vv.flatten())\n",
    "    return torch.cat(para)\n",
    "\n",
    "def test_merged(state_merged):\n",
    "    state_merged = deepcopy(state_merged)\n",
    "    net_ = Mlp(out_features=num_classes)\n",
    "    net_.load_state_dict(state_merged)\n",
    "    return test_network(net_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3424256])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "param = get_1d_weight(deepcopy(neta_state))\n",
    "param.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'loss1': 0.03403312081936747,\n",
       "  'loss2': 0.7417371094226837,\n",
       "  'acc1': 0.9872,\n",
       "  'acc2': 0.436},\n",
       " {'loss1': 0.9998829185962677,\n",
       "  'loss2': 0.03771120090968907,\n",
       "  'acc1': 0.3366,\n",
       "  'acc2': 0.9881})"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def vec2net(para):\n",
    "    sdict, left = {}, 0\n",
    "    net = Mlp(out_features=num_classes)\n",
    "    for kk, vv in net.state_dict().items():\n",
    "        num = vv.numel()\n",
    "        sdict[kk] = para[left:left+num].view(vv.shape)\n",
    "        left += num\n",
    "    net.load_state_dict(sdict)\n",
    "    net.to(device)\n",
    "    return net\n",
    "\n",
    "param = get_1d_weight(deepcopy(neta_state))\n",
    "neta = vec2net(param)\n",
    "param = get_1d_weight(deepcopy(netb_state))\n",
    "netb = vec2net(param)\n",
    "test_network(neta),test_network(netb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "use_permute = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_nets = []\n",
    "# avg_rates = [0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]\n",
    "avg_rates = [0,0.5,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Activation based merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing activation: 100%|██████████| 120/120 [00:01<00:00, 76.82it/s] \n",
      "Computing permutation: 100%|██████████| 3/3 [00:02<00:00,  1.43it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'loss1': 0.106729856133461,\n",
       " 'loss2': 0.14129881635308267,\n",
       " 'acc1': 0.9722,\n",
       " 'acc2': 0.9518}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mudsc import WeightFusion\n",
    "perm = get_mlp_perm()\n",
    "neta = Mlp(out_features=num_classes)\n",
    "netb = Mlp(out_features=num_classes)\n",
    "net_root = Mlp(out_features=num_classes)\n",
    "neta.load_state_dict(deepcopy(neta_state))\n",
    "netb.load_state_dict(deepcopy(netb_state))\n",
    "neta.to(device)\n",
    "netb.to(device)\n",
    "net_root.to(device)\n",
    "state_merge = WeightFusion(use_permute=use_permute).transform([neta,netb],perm,act_loader=act_dl,return_state_dict=True)\n",
    "net_root.load_state_dict({k:v[0] for k,v in  state_merge.items()})\n",
    "for r in avg_rates:\n",
    "    state_merge_ = {k:v[0]*r+v[1]*(1-r)  for k,v in state_merge.items()}\n",
    "    base_nets.append(state_merge_)\n",
    "test_merged(base_nets[-2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight based merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter 0 process count 0 newL 0.09776911798204917\n",
      "iter 1 process count 0 newL 0.11801383056445047\n",
      "iter 2 process count 0 newL 0.12237735488573283\n",
      "iter 3 process count 0 newL 0.12341072490623144\n",
      "iter 4 process count 0 newL 0.12400691959919641\n",
      "iter 5 process count 0 newL 0.12452647117728095\n",
      "iter 6 process count 0 newL 0.12463840038374958\n",
      "iter 7 process count 0 newL 0.12483871068009951\n",
      "iter 8 process count 0 newL 0.1248529496891327\n",
      "iter 9 process count 0 newL 0.12498330971599596\n",
      "iter 10 process count 0 newL 0.12540711139202662\n",
      "iter 11 process count 0 newL 0.1256624117947164\n",
      "iter 12 process count 0 newL 0.12587856455138535\n",
      "iter 13 process count 0 newL 0.1257728334494459\n",
      "iter 14 process count 1 newL 0.1255119931071628\n",
      "iter 15 process count 2 newL 0.1257728334494459\n",
      "iter 16 process count 3 newL 0.1258663067468054\n",
      "iter 17 process count 4 newL 0.1257728334494459\n",
      "permute weight\n",
      "iter 0 process count 0 newL 0.15541826436916986\n",
      "iter 1 process count 0 newL 0.2159542739391327\n",
      "iter 2 process count 0 newL 0.21632862587769827\n",
      "iter 3 process count 0 newL 0.21642792721589407\n",
      "iter 4 process count 0 newL 0.2164597064256668\n",
      "iter 5 process count 0 newL 0.2164459079504013\n",
      "iter 6 process count 1 newL 0.2164736638466517\n",
      "iter 7 process count 0 newL 0.21645727256933847\n",
      "iter 8 process count 1 newL 0.21645727256933847\n",
      "iter 9 process count 2 newL 0.21645727256933847\n",
      "iter 10 process count 3 newL 0.21645727256933847\n",
      "iter 11 process count 4 newL 0.21645727256933847\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'loss1': 0.17029702179133893,\n",
       " 'loss2': 0.09680469669401645,\n",
       " 'acc1': 0.9469,\n",
       " 'acc2': 0.968}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mudsc import WeightFusion\n",
    "perm = get_mlp_perm()\n",
    "neta = Mlp(out_features=num_classes)\n",
    "netb = Mlp(out_features=num_classes)\n",
    "neta.load_state_dict(deepcopy(neta_state))\n",
    "netb.load_state_dict(deepcopy(netb_state))\n",
    "neta.to(device)\n",
    "netb.to(device)\n",
    "state_merge = WeightFusion(verbose=True,use_permute=use_permute).transform([neta,netb],perm,return_state_dict=True)\n",
    "\n",
    "neta.load_state_dict({k:v[0] for k,v in state_merge.items()})\n",
    "netb.load_state_dict({k:v[1] for k,v in state_merge.items()})\n",
    "# Align model A with the activation-based method for the closer basins\n",
    "align_fusion = WeightFusion(verbose=True,use_permute=True)\n",
    "state_align_a = align_fusion.transform([net_root,neta],perm,return_state_dict=True)\n",
    "state_align_a = {k:v[1] for k,v in state_align_a.items()}\n",
    "state_align_b = align_fusion.apply_permute([net_root.state_dict(),netb.state_dict()],no_avg=True)\n",
    "state_align_b = {k:v[1] for k,v in state_align_b.items()}\n",
    "state_merge = {k:[state_align_a[k],state_align_b[k]] for k in state_merge}\n",
    "for r in avg_rates:\n",
    "    state_merge_ = {k:v[0]*r+v[1]*(1-r)  for k,v in state_merge.items()}\n",
    "    base_nets.append(state_merge_)\n",
    "test_merged(base_nets[-2])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hybrid merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "nets = deepcopy(base_nets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "169"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gc\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fix Similarity 0.5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing activation: 100%|██████████| 120/120 [00:01<00:00, 93.17it/s] \n",
      "Computing permutation: 100%|██████████| 3/3 [00:02<00:00,  1.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "permute weight\n",
      "iter 0 process count 0 newL 0.2572547247012456\n",
      "iter 1 process count 0 newL 0.3829912543296814\n",
      "iter 2 process count 0 newL 0.38433225949605304\n",
      "iter 3 process count 0 newL 0.38435786962509155\n",
      "iter 4 process count 0 newL 0.38435786962509155\n",
      "iter 5 process count 1 newL 0.38435786962509155\n",
      "iter 6 process count 2 newL 0.38435786962509155\n",
      "iter 7 process count 3 newL 0.38435786962509155\n",
      "iter 8 process count 4 newL 0.38435786962509155\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{0.5: {'loss1': 0.09578942246735096,\n",
       "  'loss2': 0.08641684409230947,\n",
       "  'acc1': 0.9731,\n",
       "  'acc2': 0.9737}}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mudsc import WeightFusion\n",
    "perm = get_mlp_perm()\n",
    "results = {}\n",
    "for fr in [0.5]:\n",
    "    neta = Mlp(out_features=num_classes)\n",
    "    netb = Mlp(out_features=num_classes)\n",
    "    neta.load_state_dict(deepcopy(neta_state))\n",
    "    netb.load_state_dict(deepcopy(netb_state))\n",
    "    neta.to(device)\n",
    "    netb.to(device)\n",
    "    state_merge = WeightFusion(use_permute=use_permute,verbose=False,fix_sims=True,fix_rate=fr).transform([neta,netb],perm,act_loader=act_dl,in_weight_space=True,return_state_dict=True)\n",
    "    neta.load_state_dict({k:v[0] for k,v in state_merge.items()})\n",
    "    netb.load_state_dict({k:v[1] for k,v in state_merge.items()})\n",
    "    # Align model A with the activation-based method \n",
    "    align_fusion = WeightFusion(verbose=True,use_permute=True)\n",
    "    state_align_a = align_fusion.transform([net_root,neta],perm,return_state_dict=True)\n",
    "    state_align_a = {k:v[1] for k,v in state_align_a.items()}\n",
    "    state_align_b = align_fusion.apply_permute([net_root.state_dict(),netb.state_dict()],no_avg=True)\n",
    "    state_align_b = {k:v[1] for k,v in state_align_b.items()}\n",
    "    state_merge = {k:[state_align_a[k],state_align_b[k]] for k in state_merge}\n",
    "    for r in avg_rates:\n",
    "        state_merge_ = {k:v[0]*r+v[1]*(1-r)  for k,v in state_merge.items()}\n",
    "        nets.append(state_merge_)\n",
    "    results[fr] = test_merged(nets[-2])\n",
    "results \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 9/9 [00:00<00:00, 3612.32it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(81, 3424256)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "para_lst, diff_lst = [], []\n",
    "\n",
    "nets_ = deepcopy(nets)\n",
    "# for i in range(8):\n",
    "#     for j in range(i+1,9):\n",
    "#         nets_.append({k: -nets[i][k]/3 + nets[j][k]*4/3 for k in nets[i]})\n",
    "#         nets_.append({k: nets[i][k]/3 + nets[j][k]*2/3 for k in nets[i]})\n",
    "#         nets_.append({k: nets[i][k]*2/3 + nets[j][k]/3 for k in nets[i]})\n",
    "#         nets_.append({k: nets[i][k]*4/3 - nets[j][k]/3 for k in nets[i]})    \n",
    "\n",
    "for net_state in tqdm(nets_):\n",
    "    para = get_1d_weight(net_state)\n",
    "    para_lst.append(para)\n",
    "\n",
    "\n",
    "#denser\n",
    "if use_permute:\n",
    "\n",
    "    pass\n",
    "else:\n",
    "    denser_num = 3\n",
    "\n",
    "    center_idx = 2\n",
    "    other_idxes = [i for i in range(3) if i !=center_idx]\n",
    "    expand_step=2\n",
    "    step_rate = 1/(denser_num+1)\n",
    "    ext_para_list = []\n",
    "    for other_idx in other_idxes:\n",
    "        center_nets = para_lst[center_idx*3:(center_idx+1)*3]\n",
    "        other_nets = para_lst[other_idx*3:(other_idx+1)*3]\n",
    "        for ra in np.linspace(0,1+expand_step*step_rate,denser_num+2+expand_step)[1:-1]:\n",
    "            if abs(ra-1)<1e-4:\n",
    "                continue\n",
    "            net_0 = (1-ra) * center_nets[0] + ra*other_nets[0]\n",
    "            net_2 = (1-ra) * center_nets[2] + ra*other_nets[2]\n",
    "            if ra > 1:\n",
    "                ext_para_list.extend([net_0,net_2])\n",
    "            else:\n",
    "                para_lst.extend([net_0,net_2])\n",
    "            for rb in np.linspace(0,1,denser_num*2+3)[1:-1]:\n",
    "                if ra > 1:\n",
    "                    ext_para_list.append(rb*net_0 + (1-rb)*net_2)\n",
    "                else:\n",
    "                    para_lst.append(rb*net_0 + (1-rb)*net_2)\n",
    "      \n",
    "para_lst = [p.cpu().detach() for p in para_lst]\n",
    "split_idx = len(para_lst)\n",
    "para_lst = para_lst + [p.cpu().detach() for p in ext_para_list]\n",
    "matrix = torch.stack(para_lst).cpu().detach().numpy()\n",
    "matrix.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "angle between pc1 and pc2: -0.000000\n",
      "pca.explained_variance_ratio_: [0.43051118 0.26333168]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(81, 2)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=2)\n",
    "pca.fit(matrix[:split_idx]) # [num_models, num_weights], ignore ours-S2 because it is similar with ours-S1\n",
    "\n",
    "pc1 = np.array(pca.components_[0])\n",
    "pc2 = np.array(pca.components_[1])\n",
    "angle = np.ndarray.dot(pc1, pc2)/(np.linalg.norm(pc1)*np.linalg.norm(pc2))\n",
    "print(\"angle between pc1 and pc2: %f\" % angle)\n",
    "print(\"pca.explained_variance_ratio_: %s\" % str(pca.explained_variance_ratio_))\n",
    "\n",
    "coord = pca.transform(matrix)\n",
    "coord.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-19.211304, 20.917137, -21.396284, 29.116663)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coord[:,0].min(),coord[:,0].max(),coord[:,1].min(),coord[:,1].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3424256,)\n",
      "(1, 2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "range_x = [-23, 25]\n",
    "range_y = [-20, 25]\n",
    "plt.figure()\n",
    "# markers = [\".\",\"*\",\"o\",\"s\",\"d\"]\n",
    "markers = [\".\",\"*\",\"o\"]\n",
    "num_per_basin = len(avg_rates)\n",
    "for i in range(3):\n",
    "    plt.plot(coord[i*num_per_basin:(i+1)*num_per_basin, 0],\n",
    "              coord[i*num_per_basin:(i+1)*num_per_basin, 1],\n",
    "                marker=markers[i], \n",
    "                c=\"black\")\n",
    "\n",
    "i=1\n",
    "\n",
    "reverse_vec = pca.inverse_transform(np.array([coord[i*num_per_basin:i*num_per_basin+1, 0].item(),\n",
    "                                              coord[i*num_per_basin:i*num_per_basin+1, 1].item()]))\n",
    "\n",
    "print(reverse_vec.shape)\n",
    "point1 = pca.transform(reverse_vec.reshape(1,-1))\n",
    "print(point1.shape)\n",
    "plt.plot(point1[:,0],\n",
    "              point1[:,1],\n",
    "                marker=markers[i], \n",
    "                c=\"red\")\n",
    "plt.scatter(coord[9:,0],coord[9:,1])\n",
    "\n",
    "plt.xlim(range_x)\n",
    "plt.ylim(range_y)\n",
    "\n",
    "plt.xlabel('Parameter1')\n",
    "plt.ylabel('Parameter2')\n",
    "# l = plt.legend(['Activation-Based', 'Weight-based', 'Hybrid1', 'Hybrid2', 'Hybrid3'])\n",
    "l = plt.legend(['Activation-Based', 'Weight-based', 'Hybrid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def point_along_traj(mid, dx, dy):\n",
    "    vec = matrix[mid] + dx*pc1 + dy*pc2\n",
    "    xy = pca.transform(vec[None, :]).squeeze()\n",
    "    \n",
    "    vec = torch.from_numpy(vec).to(device)\n",
    "    net = vec2net(vec)\n",
    "\n",
    "    results = test_network(net)\n",
    "    return xy[0], xy[1], results\n",
    "\n",
    "def point_on_grid(point1):\n",
    "    vec = pca.inverse_transform(point1)\n",
    "    \n",
    "    vec = torch.from_numpy(vec).to(device)\n",
    "    net = vec2net(vec)\n",
    "\n",
    "    results = test_network(net)\n",
    "    return  results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## calc grid coord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       " [-23.0,\n",
       "  -22.0,\n",
       "  -21.0,\n",
       "  -20.0,\n",
       "  -19.0,\n",
       "  -18.0,\n",
       "  -17.0,\n",
       "  -16.0,\n",
       "  -15.0,\n",
       "  -14.0,\n",
       "  -13.0,\n",
       "  -12.0,\n",
       "  -11.0,\n",
       "  -10.0,\n",
       "  -9.0,\n",
       "  -8.0,\n",
       "  -7.0,\n",
       "  -6.0,\n",
       "  -5.0,\n",
       "  -4.0,\n",
       "  -3.0,\n",
       "  -2.0,\n",
       "  -1.0,\n",
       "  0.0,\n",
       "  1.0,\n",
       "  2.0,\n",
       "  3.0,\n",
       "  4.0,\n",
       "  5.0,\n",
       "  6.0,\n",
       "  7.0,\n",
       "  8.0,\n",
       "  9.0,\n",
       "  10.0,\n",
       "  11.0,\n",
       "  12.0,\n",
       "  13.0,\n",
       "  14.0,\n",
       "  15.0,\n",
       "  16.0,\n",
       "  17.0,\n",
       "  18.0,\n",
       "  19.0,\n",
       "  20.0,\n",
       "  21.0,\n",
       "  22.0,\n",
       "  23.0,\n",
       "  24.0,\n",
       "  25.0],\n",
       " [-20.0,\n",
       "  -19.0,\n",
       "  -18.0,\n",
       "  -17.0,\n",
       "  -16.0,\n",
       "  -15.0,\n",
       "  -14.0,\n",
       "  -13.0,\n",
       "  -12.0,\n",
       "  -11.0,\n",
       "  -10.0,\n",
       "  -9.0,\n",
       "  -8.0,\n",
       "  -7.0,\n",
       "  -6.0,\n",
       "  -5.0,\n",
       "  -4.0,\n",
       "  -3.0,\n",
       "  -2.0,\n",
       "  -1.0,\n",
       "  0.0,\n",
       "  1.0,\n",
       "  2.0,\n",
       "  3.0,\n",
       "  4.0,\n",
       "  5.0,\n",
       "  6.0,\n",
       "  7.0,\n",
       "  8.0,\n",
       "  9.0,\n",
       "  10.0,\n",
       "  11.0,\n",
       "  12.0,\n",
       "  13.0,\n",
       "  14.0,\n",
       "  15.0,\n",
       "  16.0,\n",
       "  17.0,\n",
       "  18.0,\n",
       "  19.0,\n",
       "  20.0,\n",
       "  21.0,\n",
       "  22.0,\n",
       "  23.0,\n",
       "  24.0,\n",
       "  25.0])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mids = [item for item in range(0, 9)] # ids of models except ours-2\n",
    "xx_lst = np.linspace(range_x[0], range_x[1], range_x[1]-range_x[0]+1).tolist()\n",
    "yy_lst = np.linspace(range_y[0], range_y[1], range_y[1]-range_y[0]+1).tolist()\n",
    "mids, xx_lst, yy_lst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## along path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 81/81 [20:07<00:00, 14.91s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(10.615869,\n",
       "  -19.126413,\n",
       "  {'loss1': 0.9799791514873505,\n",
       "   'loss2': 0.11111131431534886,\n",
       "   'acc1': 0.3612,\n",
       "   'acc2': 0.9664}),\n",
       " (10.615874,\n",
       "  -18.126404,\n",
       "  {'loss1': 0.9849614828824997,\n",
       "   'loss2': 0.1099677979014814,\n",
       "   'acc1': 0.3619,\n",
       "   'acc2': 0.966}),\n",
       " (10.615874,\n",
       "  -17.126415,\n",
       "  {'loss1': 0.9900764882564544,\n",
       "   'loss2': 0.1092548075132072,\n",
       "   'acc1': 0.3646,\n",
       "   'acc2': 0.9663}),\n",
       " (11.615873,\n",
       "  -19.126404,\n",
       "  {'loss1': 0.9818003922700882,\n",
       "   'loss2': 0.10706066442653536,\n",
       "   'acc1': 0.3601,\n",
       "   'acc2': 0.967}),\n",
       " (11.615868,\n",
       "  -18.12641,\n",
       "  {'loss1': 0.9867661535739899,\n",
       "   'loss2': 0.10619326764717699,\n",
       "   'acc1': 0.3628,\n",
       "   'acc2': 0.9662}),\n",
       " (11.615875,\n",
       "  -17.126406,\n",
       "  {'loss1': 0.9919923335313797,\n",
       "   'loss2': 0.10574946142733097,\n",
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      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mids = []\n",
    "tmp_lst = [x for x in range(matrix.shape[0])]\n",
    "mids.extend(tmp_lst)\n",
    "\n",
    "coord_lst = []\n",
    "dx_lst = [-1, 0, 1] \n",
    "dy_lst = [-1, 0, 1]\n",
    "for mid in tqdm(mids):\n",
    "    for dx in dx_lst:\n",
    "        for dy in dy_lst:\n",
    "            xx, yy, results = point_along_traj(mid, dx, dy)\n",
    "            coord_lst.append((xx, yy, results))\n",
    "coord_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# suffix = \"perm\" if use_permute else \"union\"\n",
    "# with open(f\"save/mnist_coord_clip_{exp_idx}_{suffix}.pkl\", 'wb') as f:\n",
    "#     pkl.dump(coord_lst, f)\n",
    "\n",
    "suffix = \"perm\" if use_permute else \"union\"\n",
    "with open(f\"save/mnist_coord_clip_{exp_idx}_{suffix}.pkl\", 'rb') as f:\n",
    "    coord_lst = pkl.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### interpolator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "         19.24,  19.72,  20.2 ,  20.68,  21.16,  21.64,  22.12,  22.6 ,\n",
       "         23.08,  23.56,  24.04,  24.52,  25.  ]),\n",
       " array([-20.  , -19.55, -19.1 , -18.65, -18.2 , -17.75, -17.3 , -16.85,\n",
       "        -16.4 , -15.95, -15.5 , -15.05, -14.6 , -14.15, -13.7 , -13.25,\n",
       "        -12.8 , -12.35, -11.9 , -11.45, -11.  , -10.55, -10.1 ,  -9.65,\n",
       "         -9.2 ,  -8.75,  -8.3 ,  -7.85,  -7.4 ,  -6.95,  -6.5 ,  -6.05,\n",
       "         -5.6 ,  -5.15,  -4.7 ,  -4.25,  -3.8 ,  -3.35,  -2.9 ,  -2.45,\n",
       "         -2.  ,  -1.55,  -1.1 ,  -0.65,  -0.2 ,   0.25,   0.7 ,   1.15,\n",
       "          1.6 ,   2.05,   2.5 ,   2.95,   3.4 ,   3.85,   4.3 ,   4.75,\n",
       "          5.2 ,   5.65,   6.1 ,   6.55,   7.  ,   7.45,   7.9 ,   8.35,\n",
       "          8.8 ,   9.25,   9.7 ,  10.15,  10.6 ,  11.05,  11.5 ,  11.95,\n",
       "         12.4 ,  12.85,  13.3 ,  13.75,  14.2 ,  14.65,  15.1 ,  15.55,\n",
       "         16.  ,  16.45,  16.9 ,  17.35,  17.8 ,  18.25,  18.7 ,  19.15,\n",
       "         19.6 ,  20.05,  20.5 ,  20.95,  21.4 ,  21.85,  22.3 ,  22.75,\n",
       "         23.2 ,  23.65,  24.1 ,  24.55,  25.  ]))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xi = np.linspace(range_x[0], range_x[1], 101)\n",
    "yi = np.linspace(range_y[0], range_y[1], 101)\n",
    "xi, yi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x_inter = np.hstack([np.array(grid_xx), np.array(coord_xx)])\n",
    "# y_inter = np.hstack([np.array(grid_yy),np.array(coord_yy)])\n",
    "# z_inter = np.hstack([np.array(grid_loss), np.array(coord_loss)])\n",
    "# x_inter = np.array(grid_xx)\n",
    "# y_inter = np.array(grid_yy)\n",
    "plt.rcParams[ 'text.usetex' ] = False\n",
    "label = ['Zipit', 'W. Zip', r'MuDSC$_{Zip}$']\n",
    "# z_inter = np.array(grid_loss)\n",
    "title_fontsize=25\n",
    "axis_fontsize=20\n",
    "ticks_fontsize=16\n",
    "legend_fontsize=16\n",
    "coord_xx = [item[0] for item in coord_lst]\n",
    "coord_yy = [item[1] for item in coord_lst]\n",
    "coord_loss = [(item[2][\"loss1\"] + item[2][\"loss2\"])/2 for item in coord_lst]\n",
    "coord_acc = [(item[2][\"acc1\"] + item[2][\"acc2\"])/2 for item in coord_lst]\n",
    "coord_xx, coord_yy, coord_acc, coord_loss\n",
    "\n",
    "x_inter = np.array(coord_xx)\n",
    "y_inter = np.array(coord_yy)\n",
    "z_inter = np.array(coord_loss)\n",
    "interpolator = tri.LinearTriInterpolator(tri.Triangulation(x_inter, y_inter), z_inter)\n",
    "\n",
    "xi = np.linspace(range_x[0], range_x[1], 101)\n",
    "yi = np.linspace(range_y[0], range_y[1], 101)\n",
    "Xi, Yi = np.meshgrid(xi, yi)\n",
    "zi = interpolator(Xi, Yi)\n",
    "zi.min(), zi.max()\n",
    "\n",
    "fig,ax = plt.subplots(1,3,figsize=(20,5))\n",
    "\n",
    "cmap = plt.get_cmap(\"jet_r\")\n",
    "zi_ = np.copy(zi)\n",
    "thres = 0.4\n",
    "zi_[zi_>thres] = thres\n",
    "plt.rcParams.update({'font.size': 14})\n",
    "CS = ax[0].contour(xi, yi, zi_,5,zorder=1, cmap=cmap)\n",
    "CF = ax[0].contourf(xi, yi, zi_,5, zorder=0, cmap=cmap,alpha=0.55)\n",
    "\n",
    "markers = [\"o\",\"o\",\"o\"]\n",
    "num_per_basin = 3\n",
    "for i in range(3):\n",
    "    ax[0].plot(coord[i*num_per_basin:(i+1)*num_per_basin, 0],\n",
    "              coord[i*num_per_basin:(i+1)*num_per_basin, 1],\n",
    "                marker=markers[i], \n",
    "                c=\"black\",linestyle=\"--\")\n",
    "markers = [\"s\",\"d\",\"*\"]\n",
    "for i in range(3):\n",
    "    ax[0].scatter(coord[i*num_per_basin+1:i*num_per_basin+2, 0],\n",
    "              coord[i*num_per_basin+1:i*num_per_basin+2, 1],\n",
    "                marker=markers[i], \n",
    "                c=\"black\",s=140,label=label[i],zorder=2)\n",
    "ax[0].set_xlim(range_x)\n",
    "ax[0].set_ylim(range_y)\n",
    "ax[0].tick_params(axis='x', labelsize=ticks_fontsize)\n",
    "ax[0].tick_params(axis='y', labelsize=ticks_fontsize)\n",
    "\n",
    "# plt.xlabel('Parameter1')\n",
    "ax[0].set_ylabel('Parameter2',fontsize=axis_fontsize)\n",
    "ax[0].set_title('Average',fontsize=title_fontsize)\n",
    "coord_xx = [item[0] for item in coord_lst]\n",
    "coord_yy = [item[1] for item in coord_lst]\n",
    "coord_loss = [item[2][\"loss1\"] for item in coord_lst]\n",
    "coord_acc = [item[2][\"acc1\"] for item in coord_lst]\n",
    "coord_xx, coord_yy, coord_acc, coord_loss\n",
    "\n",
    "x_inter = np.array(coord_xx)\n",
    "y_inter = np.array(coord_yy)\n",
    "z_inter = np.array(coord_loss)\n",
    "interpolator = tri.LinearTriInterpolator(tri.Triangulation(x_inter, y_inter), z_inter)\n",
    "\n",
    "xi = np.linspace(range_x[0], range_x[1], 101)\n",
    "yi = np.linspace(range_y[0], range_y[1], 101)\n",
    "Xi, Yi = np.meshgrid(xi, yi)\n",
    "zi = interpolator(Xi, Yi)\n",
    "zi.min(), zi.max()\n",
    "\n",
    "cmap = plt.get_cmap(\"jet_r\")\n",
    "zi_ = np.copy(zi)\n",
    "thres = 0.4\n",
    "zi_[zi_>thres] = thres\n",
    "# plt.rcParams.update({'font.size': 14})\n",
    "CS = ax[1].contour(xi, yi, zi_,5,zorder=1, cmap=cmap)\n",
    "CF = ax[1].contourf(xi, yi, zi_,5, zorder=0, cmap=cmap,alpha=0.55)\n",
    "# plt.colorbar(CF, label='Loss', shrink=1.0)\n",
    "\n",
    "markers = [\"o\",\"o\",\"o\"]\n",
    "num_per_basin = 3\n",
    "for i in range(3):\n",
    "    ax[1].plot(coord[i*num_per_basin:(i+1)*num_per_basin, 0],\n",
    "              coord[i*num_per_basin:(i+1)*num_per_basin, 1],\n",
    "                marker=markers[i], \n",
    "                c=\"black\",linestyle=\"--\")\n",
    "markers = [\"s\",\"d\",\"*\"]\n",
    "for i in range(3):\n",
    "    ax[1].scatter(coord[i*num_per_basin+1:i*num_per_basin+2, 0],\n",
    "              coord[i*num_per_basin+1:i*num_per_basin+2, 1],\n",
    "                marker=markers[i], \n",
    "                c=\"black\",s=140,label=label[i],zorder=2)\n",
    "ax[1].set_xlim(range_x)\n",
    "ax[1].set_ylim(range_y)\n",
    "ax[1].tick_params(axis='x', labelsize=ticks_fontsize)\n",
    "ax[1].tick_params(axis='y', labelsize=ticks_fontsize)\n",
    "\n",
    "\n",
    "ax[1].set_xlabel('Parameter1',fontsize=axis_fontsize)\n",
    "ax[1].set_title('Task A',fontsize=title_fontsize)\n",
    "\n",
    "\n",
    "coord_xx = [item[0] for item in coord_lst]\n",
    "coord_yy = [item[1] for item in coord_lst]\n",
    "coord_loss = [item[2][\"loss2\"] for item in coord_lst]\n",
    "coord_acc = [item[2][\"acc2\"] for item in coord_lst]\n",
    "# coord_loss = [item[2][\"loss1\"] for item in coord_lst]\n",
    "# coord_acc = [item[2][\"acc1\"] for item in coord_lst]\n",
    "coord_xx, coord_yy, coord_acc, coord_loss\n",
    "\n",
    "x_inter = np.array(coord_xx)\n",
    "y_inter = np.array(coord_yy)\n",
    "z_inter = np.array(coord_loss)\n",
    "interpolator = tri.LinearTriInterpolator(tri.Triangulation(x_inter, y_inter), z_inter)\n",
    "\n",
    "xi = np.linspace(range_x[0], range_x[1], 101)\n",
    "yi = np.linspace(range_y[0], range_y[1], 101)\n",
    "Xi, Yi = np.meshgrid(xi, yi)\n",
    "zi = interpolator(Xi, Yi)\n",
    "zi.min(), zi.max()\n",
    "\n",
    "cmap = plt.get_cmap(\"jet_r\")\n",
    "zi_ = np.copy(zi)\n",
    "thres = 0.4\n",
    "zi_[zi_>thres] = thres\n",
    "# plt.rcParams.update({'font.size': 14})\n",
    "CS = ax[2].contour(xi, yi, zi_,5,zorder=1, cmap=cmap)\n",
    "CF = ax[2].contourf(xi, yi, zi_,5, zorder=0, cmap=cmap,alpha=0.55)\n",
    "\n",
    "fig.subplots_adjust(right=0.8)\n",
    "cbar_ax = fig.add_axes([0.81, 0.15, 0.01, 0.7])\n",
    "cbar_ax.tick_params(labelsize=ticks_fontsize)\n",
    "cbar =fig.colorbar(CF, shrink=1.0,cax=cbar_ax)\n",
    "cbar.set_label('Loss', fontsize=axis_fontsize)\n",
    "\n",
    "\n",
    "markers = [\"o\",\"o\",\"o\"]\n",
    "num_per_basin = 3\n",
    "for i in range(3):\n",
    "    ax[2].plot(coord[i*num_per_basin:(i+1)*num_per_basin, 0],\n",
    "              coord[i*num_per_basin:(i+1)*num_per_basin, 1],\n",
    "                marker=markers[i], \n",
    "                c=\"black\",linestyle=\"--\")\n",
    "markers = [\"s\",\"d\",\"*\"]\n",
    "for i in range(3):\n",
    "    ax[2].scatter(coord[i*num_per_basin+1:i*num_per_basin+2, 0],\n",
    "              coord[i*num_per_basin+1:i*num_per_basin+2, 1],\n",
    "                marker=markers[i], \n",
    "                c=\"black\",s=140,label=label[i],zorder=2)\n",
    "ax[2].set_xlim(range_x)\n",
    "ax[2].set_ylim(range_y)\n",
    "ax[2].tick_params(axis='x', labelsize=ticks_fontsize)\n",
    "ax[2].tick_params(axis='y', labelsize=ticks_fontsize)\n",
    "\n",
    "ax[2].set_title('Task B',fontsize=title_fontsize)\n",
    "l = ax[0].legend(loc=(0.52,0.5),fontsize=legend_fontsize)\n",
    "\n",
    "fig.savefig(\"loss_land.pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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