02-ImgEmbs.ipynb

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   "id": "c6db9fca-d075-4388-b30d-62ca3d2122a3",
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   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ad4ac457-4c20-4e8e-beb2-63c5160a9972",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "from data import load_parquets_from_zip\n",
    "import polars as pl\n",
    "import numpy as np\n",
    "import random\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "from tqdm.auto import tqdm\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0e8a79f-26ae-4d1f-bbf1-a9276812eeff",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.manual_seed(42)\n",
    "random.seed(42)\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "16decf1c-d349-4855-824b-7e7e02a79c3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "imgs = load_parquets_from_zip('dataset/Ekstra_Bladet_image_embeddings.zip')['Ekstra_Bladet_image_embeddings/image_embeddings']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3c65f4c0-0dd0-4072-86e4-807e26dcce55",
   "metadata": {},
   "outputs": [],
   "source": [
    "imgs_embs = torch.from_numpy(np.asarray(list(imgs['image_embedding'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "58341028-f4c6-4912-a2eb-09fbacc9ba0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dl = DataLoader(TensorDataset(imgs_embs), batch_size=512, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
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   "id": "bef4033f-5393-4930-b546-9bfe8e67d1c9",
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   "source": [
    "class Normalize(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        pass\n",
    "\n",
    "    def forward(self, x):\n",
    "        return F.normalize(x, dim=-1)\n",
    "\n",
    "class EncodeDecoder(nn.Module):\n",
    "\n",
    "    def __init__(self, in_size=1024, hidden=[512, 128]):\n",
    "        super().__init__()\n",
    "        layers = [in_size] + hidden\n",
    "        self.encoder = nn.Sequential(*([nn.Linear(layers[i], layers[i+1], bias=False) for i in range(len(layers) - 1)] + [Normalize()]))\n",
    "        layers = layers[::-1]\n",
    "        self.decoder = nn.Sequential(*[nn.Linear(layers[i], layers[i+1], bias=False) for i in range(len(layers) - 1)])\n",
    "        pass\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.decoder(self.encoder(x))"
   ]
  },
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   "id": "66a3aa5d-0c03-4b86-9834-43d49a440b93",
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    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "model = EncodeDecoder().to(device)\n",
    "\n",
    "opt = torch.optim.Adam(model.parameters())\n",
    "\n",
    "mse = nn.MSELoss()"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 79: 9.172783457068508e-08\n"
     ]
    },
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 80: 8.262504614826053e-08\n"
     ]
    },
    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 81: 7.96744842970309e-08\n"
     ]
    },
    {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 82: 7.444707772088211e-08\n"
     ]
    },
    {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 83: 6.907513982360431e-08\n"
     ]
    },
    {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 84: 1.4089717741449904e-07\n"
     ]
    },
    {
     "data": {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 85: 4.7790262290356115e-08\n"
     ]
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    {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 86: 7.662198146278311e-08\n"
     ]
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    {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 87: 7.511229387501654e-08\n"
     ]
    },
    {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 88: 8.381989076509564e-08\n"
     ]
    },
    {
     "data": {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 89: 7.139282817109266e-08\n"
     ]
    },
    {
     "data": {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 90: 7.262293235765273e-08\n"
     ]
    },
    {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 91: 8.369624922364603e-08\n"
     ]
    },
    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 92: 6.93054849993736e-08\n"
     ]
    },
    {
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     "metadata": {},
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    {
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     "text": [
      "Epoch 93: 6.846569429252605e-08\n"
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    {
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     "text": [
      "Epoch 94: 7.354550079831097e-08\n"
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     "text": [
      "Epoch 95: 9.342550440513589e-08\n"
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     "text": [
      "Epoch 96: 8.112770683878106e-08\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 97: 4.8934624047728567e-08\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 98: 8.286820037690844e-08\n"
     ]
    },
    {
     "data": {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 99: 6.589718893718922e-08\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "EncodeDecoder(\n",
       "  (encoder): Sequential(\n",
       "    (0): Linear(in_features=1024, out_features=512, bias=False)\n",
       "    (1): Linear(in_features=512, out_features=128, bias=False)\n",
       "    (2): Normalize()\n",
       "  )\n",
       "  (decoder): Sequential(\n",
       "    (0): Linear(in_features=128, out_features=512, bias=False)\n",
       "    (1): Linear(in_features=512, out_features=1024, bias=False)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_path = 'preprocess/image_encoder.pth'\n",
    "if os.path.exists(model_path):\n",
    "    model.load_state_dict(torch.load(model_path))\n",
    "else:\n",
    "    for e in range(1, 100):\n",
    "        c_loss = 0\n",
    "        c_step = 0\n",
    "        with tqdm(dl, leave=False) as dll:\n",
    "            for x in dll:\n",
    "                opt.zero_grad()\n",
    "                x = x[0].to(device)\n",
    "                x_pred = model(x)\n",
    "                loss = mse(x_pred, x)\n",
    "                loss.backward()\n",
    "                opt.step()\n",
    "                c_loss += loss.item()\n",
    "                c_step += 1\n",
    "                dll.set_postfix(loss=c_loss / c_step)\n",
    "        print(f'Epoch {e}: {c_loss / c_step }')\n",
    "    torch.save(model.state_dict(), model_path)\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8293451a-6f3f-4606-b1d8-78e677d25fa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = next(iter(dl))[0].to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "49a2a17e-9124-4a6c-8cd6-988eb2653d31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.0325,  0.0286, -0.0039,  ...,  0.0428, -0.0180, -0.0141],\n",
       "        [-0.0194, -0.0154, -0.0571,  ..., -0.0247,  0.0367,  0.0314],\n",
       "        [-0.0080,  0.0134,  0.0466,  ...,  0.0630, -0.0346, -0.0435],\n",
       "        ...,\n",
       "        [-0.0051,  0.0427,  0.0274,  ...,  0.0329, -0.0334, -0.0161],\n",
       "        [-0.0104,  0.0157,  0.0363,  ...,  0.0585, -0.0177, -0.0481],\n",
       "        [-0.0461, -0.0466, -0.0732,  ..., -0.0009,  0.0293,  0.0025]],\n",
       "       device='cuda:0')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3b0cb035-4070-4b0f-ade7-c1fad970e978",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.0324,  0.0286, -0.0039,  ...,  0.0429, -0.0181, -0.0143],\n",
       "        [-0.0196, -0.0156, -0.0572,  ..., -0.0245,  0.0363,  0.0307],\n",
       "        [-0.0077,  0.0134,  0.0467,  ...,  0.0633, -0.0348, -0.0433],\n",
       "        ...,\n",
       "        [-0.0050,  0.0430,  0.0274,  ...,  0.0330, -0.0339, -0.0162],\n",
       "        [-0.0105,  0.0161,  0.0360,  ...,  0.0584, -0.0178, -0.0481],\n",
       "        [-0.0465, -0.0460, -0.0730,  ..., -0.0010,  0.0293,  0.0022]],\n",
       "       device='cuda:0', grad_fn=<MmBackward0>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ba4d3fd7-547b-4033-8bd2-c0c967a7f808",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.0186, 0.0231, 0.0307,  ..., 0.0282, 0.0268, 0.0226], device='cuda:0')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.std(x, dim=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4570c39d-4934-4f9c-8cb3-36f2aa1cb24a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.0002, 0.0002, 0.0001,  ..., 0.0002, 0.0001, 0.0002], device='cuda:0',\n",
       "       grad_fn=<MeanBackward1>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.mean(torch.abs(x - model(x)), dim=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b1b31058-3850-42fe-909d-b9f4b19854e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.0001, 0.0002, 0.0001,  ..., 0.0001, 0.0001, 0.0002], device='cuda:0',\n",
       "       grad_fn=<StdBackward0>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.std(torch.abs(x - model(x)), dim=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a3246845-59d8-450b-b57f-9b85a577e300",
   "metadata": {},
   "outputs": [],
   "source": [
    "dl = DataLoader(TensorDataset(imgs_embs), batch_size=512, shuffle=False)\n",
    "\n",
    "embs = []\n",
    "with torch.no_grad():\n",
    "    for x in dl:\n",
    "        x = x[0].to(device)\n",
    "        emb = model.encoder(x).cpu().numpy()\n",
    "        embs.append(emb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bb953877-22f1-47f2-a372-840d00979b05",
   "metadata": {},
   "outputs": [],
   "source": [
    "embs = np.concatenate(embs, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b34954b7-87ae-4e25-aae7-965c7ae6981b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.11453101,  0.00137664, -0.01590084, ..., -0.02036735,\n",
       "        -0.05598754,  0.0513007 ],\n",
       "       [-0.0300234 ,  0.00979323, -0.00183117, ...,  0.0564303 ,\n",
       "        -0.07377557,  0.09771489],\n",
       "       [ 0.2675379 , -0.06965413, -0.18188432, ..., -0.1330114 ,\n",
       "         0.00300317, -0.02472829],\n",
       "       ...,\n",
       "       [-0.05685404,  0.05275042,  0.06841292, ..., -0.02606313,\n",
       "         0.0475052 ,  0.02816776],\n",
       "       [ 0.0441104 ,  0.01659502, -0.03042564, ...,  0.06748616,\n",
       "         0.01042796, -0.05028164],\n",
       "       [-0.02042744,  0.08351305, -0.08589312, ...,  0.10827801,\n",
       "        -0.06187802, -0.02863881]], dtype=float32)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "91feabd3-03cc-48b9-bdec-f1955a0c10c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "embs = pl.DataFrame(data=embs, schema=['embeddings']).with_columns(imgs['article_id']).select('article_id', 'embeddings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b86e7a9e-d2ff-49af-bba1-de5687e37b00",
   "metadata": {},
   "outputs": [],
   "source": [
    "embs.write_parquet('preprocess/image_embs.parquet')"
   ]
  }
 ],
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