nightmareai/arf-svox2:e169fc45 – Run with an API on Replicate

Version

You're looking at a specific version of this model. Jump to the model overview.

nightmareai /arf-svox2:e169fc45

Playground API

Input

Run this model in Node.js with one line of code:

npx create-replicate --model=nightmareai/arf-svox2

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run nightmareai/arf-svox2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "nightmareai/arf-svox2:e169fc45cf0cc731656decd7a6da19145c764b717a7e8126eac2b834232fdf37",
  {
    input: {
      image: "https://replicate.delivery/mgxm/c9ab63f5-0111-46fa-bf3c-b24c45256bd6/20210215_110704.jpg",
      scene: "flower"
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run nightmareai/arf-svox2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "nightmareai/arf-svox2:e169fc45cf0cc731656decd7a6da19145c764b717a7e8126eac2b834232fdf37",
    input={
        "image": "https://replicate.delivery/mgxm/c9ab63f5-0111-46fa-bf3c-b24c45256bd6/20210215_110704.jpg",
        "scene": "flower"
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run nightmareai/arf-svox2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "nightmareai/arf-svox2:e169fc45cf0cc731656decd7a6da19145c764b717a7e8126eac2b834232fdf37",
    "input": {
      "image": "https://replicate.delivery/mgxm/c9ab63f5-0111-46fa-bf3c-b24c45256bd6/20210215_110704.jpg",
      "scene": "flower"
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2022-06-23T03:56:32.317382Z",
  "created_at": "2022-06-23T03:27:41.809269Z",
  "data_removed": false,
  "error": null,
  "id": "slugofz6f5dhrhijnrk5g4wkhi",
  "input": {
    "image": "https://replicate.delivery/mgxm/c9ab63f5-0111-46fa-bf3c-b24c45256bd6/20210215_110704.jpg",
    "scene": "flower"
  },
  "logs": "Downloading checkpoint for flower\nDetected LLFF dataset\nUsing pre-scaled images from /root/.cache/arf-svox2/data/llff/flower/images_4\nLoaded LLFF data /root/.cache/arf-svox2/data/llff/flower 22.573735200863933 222.47985705021057\nrecentered (3, 4)\n[ 1.4332432 12.709555 ]\nOverriding offset 250-> 250\ndmin = 1.433243, dmax = 12.709555, invz = 0, offset = 250\n\n  0%|          | 0/29 [00:00<?, ?it/s]\n  3%|▎         | 1/29 [00:00<00:02,  9.78it/s]\n 10%|█         | 3/29 [00:00<00:01, 13.99it/s]\n 17%|█▋        | 5/29 [00:00<00:01, 15.50it/s]\n 24%|██▍       | 7/29 [00:00<00:01, 17.10it/s]\n 34%|███▍      | 10/29 [00:00<00:00, 19.17it/s]\n 45%|████▍     | 13/29 [00:00<00:00, 21.07it/s]\n 55%|█████▌    | 16/29 [00:00<00:00, 22.08it/s]\n 66%|██████▌   | 19/29 [00:00<00:00, 22.52it/s]\n 76%|███████▌  | 22/29 [00:01<00:00, 22.80it/s]\n 86%|████████▌ | 25/29 [00:01<00:00, 23.24it/s]\n 97%|█████████▋| 28/29 [00:01<00:00, 23.62it/s]\n100%|██████████| 29/29 [00:01<00:00, 21.21it/s]\nz_bounds from LLFF: [1.4332431554794312, 12.709554672241211] (not used)\nscene_radius [1.496031746031746, 1.6613756613756614, 1.0]\n Generating rays, scaling factor 1\nic| \"Loaded ckpt: \": 'Loaded ckpt: '\n    args.init_ckpt: '/root/.cache/arf-svox2/ckpt_svox2/llff/flower/ckpt.npz'\nic| grid.basis_dim: 1\nRender options RenderOptions(backend='cuvol', background_brightness=0.5, step_size=0.5, sigma_thresh=1e-08, stop_thresh=1e-07, last_sample_opaque=False, near_clip=0.0, use_spheric_clip=False, random_sigma_std=0.0, random_sigma_std_background=0.0)\nopt_style.py:508: DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning dissapear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.\n  style_img = imageio.imread(args.style).astype(np.float32) / 255.0\nic| \"Style image: \": 'Style image: '\n    args.style: '/tmp/tmppfk2cab220210215_110704.jpg'\n    style_img.shape: torch.Size([283, 504, 3])\nDownloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth\n\n  0%|          | 0.00/528M [00:00<?, ?B/s]\n  2%|▏         | 8.29M/528M [00:00<00:06, 86.9MB/s]\n  6%|▌         | 31.3M/528M [00:00<00:02, 178MB/s]\n 10%|█         | 54.8M/528M [00:00<00:02, 209MB/s]\n 15%|█▌        | 80.0M/528M [00:00<00:02, 231MB/s]\n 19%|█▉        | 102M/528M [00:00<00:02, 201MB/s]\n 23%|██▎       | 122M/528M [00:00<00:02, 202MB/s]\n 27%|██▋       | 141M/528M [00:00<00:01, 203MB/s]\n 31%|███       | 162M/528M [00:00<00:01, 205MB/s]\n 34%|███▍      | 182M/528M [00:00<00:01, 208MB/s]\n 38%|███▊   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batches_per_epoch: 4420\n    batch_size: 5000\n    optim_type: 'ray'\n\n  0%|          | 0/4420 [00:00<?, ?it/s]\n  0%|          | 5/4420 [00:00<01:35, 46.35it/s]\n  0%|          | 10/4420 [00:00<01:33, 47.41it/s]\n  0%|          | 17/4420 [00:00<01:19, 55.20it/s]\n19 13.1097 :   0%|          | 17/4420 [00:00<01:19, 55.20it/s]\n19 13.1097 :   1%|          | 27/4420 [00:00<01:02, 70.58it/s]\n19 13.1097 :   1%|          | 37/4420 [00:00<00:55, 79.55it/s]\n39 13.8787 :   1%|          | 37/4420 [00:00<00:55, 79.55it/s]\n39 13.8787 :   1%|          | 47/4420 [00:00<00:51, 84.51it/s]\n39 13.8787 :   1%|▏         | 57/4420 [00:00<00:50, 87.04it/s]\n59 14.0519 :   1%|▏         | 57/4420 [00:00<00:50, 87.04it/s]\n59 14.0519 :   1%|▏         | 66/4420 [00:00<00:49, 87.41it/s]\n59 14.0519 :   2%|▏         | 76/4420 [00:00<00:49, 88.62it/s]\n79 15.2427 :   2%|▏         | 76/4420 [00:01<00:49, 88.62it/s]\n79 15.2427 :   2%|▏         | 86/4420 [00:01<00:48, 89.20it/s]\n79 15.2427 :   2%|▏         | 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[00:09<00:33, 102.99it/s]\n1019 22.1010 :  23%|██▎       | 1014/4420 [00:09<00:33, 102.99it/s]\n1019 22.1010 :  23%|██▎       | 1025/4420 [00:09<00:33, 102.70it/s]\n1019 22.1010 :  23%|██▎       | 1036/4420 [00:09<00:32, 104.32it/s]\n1039 24.1583 :  23%|██▎       | 1036/4420 [00:09<00:32, 104.32it/s]\n1039 24.1583 :  24%|██▎       | 1047/4420 [00:09<00:31, 105.59it/s]\n1059 23.5207 :  24%|██▎       | 1047/4420 [00:09<00:31, 105.59it/s]\n1059 23.5207 :  24%|██▍       | 1060/4420 [00:09<00:30, 110.89it/s]\n1059 23.5207 :  24%|██▍       | 1073/4420 [00:09<00:29, 114.33it/s]\n1079 19.7764 :  24%|██▍       | 1073/4420 [00:09<00:29, 114.33it/s]\n1079 19.7764 :  25%|██▍       | 1085/4420 [00:09<00:29, 112.77it/s]\n1079 19.7764 :  25%|██▍       | 1097/4420 [00:09<00:29, 111.55it/s]\n1099 19.8724 :  25%|██▍       | 1097/4420 [00:09<00:29, 111.55it/s]\n1099 19.8724 :  25%|██▌       | 1109/4420 [00:09<00:29, 111.30it/s]\n1119 20.8196 :  25%|██▌       | 1109/4420 [00:09<00:29, 111.30it/s]\n1119 20.8196 :  25%|██▌       | 1121/4420 [00:09<00:30, 108.66it/s]\n1119 20.8196 :  26%|██▌       | 1132/4420 [00:10<00:30, 106.46it/s]\n1139 20.7228 :  26%|██▌       | 1132/4420 [00:10<00:30, 106.46it/s]\n1139 20.7228 :  26%|██▌       | 1143/4420 [00:10<00:31, 105.19it/s]\n1139 20.7228 :  26%|██▌       | 1154/4420 [00:10<00:31, 103.77it/s]\n1159 21.4247 :  26%|██▌       | 1154/4420 [00:10<00:31, 103.77it/s]\n1159 21.4247 :  26%|██▋       | 1165/4420 [00:10<00:31, 104.21it/s]\n1159 21.4247 :  27%|██▋       | 1176/4420 [00:10<00:30, 105.10it/s]\n1179 23.9127 :  27%|██▋       | 1176/4420 [00:10<00:30, 105.10it/s]\n1179 23.9127 :  27%|██▋       | 1188/4420 [00:10<00:30, 106.73it/s]\n1199 21.9844 :  27%|██▋       | 1188/4420 [00:10<00:30, 106.73it/s]\n1199 21.9844 :  27%|██▋       | 1200/4420 [00:10<00:29, 110.30it/s]\n1199 21.9844 :  27%|██▋       | 1214/4420 [00:10<00:27, 117.51it/s]\n1219 17.0933 :  27%|██▋       | 1214/4420 [00:10<00:27, 117.51it/s]\n1219 17.0933 :  28%|██▊       | 1227/4420 [00:10<00:26, 120.80it/s]\n1239 23.5547 :  28%|██▊       | 1227/4420 [00:11<00:26, 120.80it/s]\n1239 23.5547 :  28%|██▊       | 1240/4420 [00:11<00:25, 123.02it/s]\n1239 23.5547 :  28%|██▊       | 1253/4420 [00:11<00:25, 124.11it/s]\n1259 22.8382 :  28%|██▊       | 1253/4420 [00:11<00:25, 124.11it/s]\n1259 22.8382 :  29%|██▊       | 1266/4420 [00:11<00:25, 124.47it/s]\n1259 22.8382 :  29%|██▉       | 1279/4420 [00:11<00:25, 121.77it/s]\n1279 25.0465 :  29%|██▉       | 1279/4420 [00:11<00:25, 121.77it/s]\n1279 25.0465 :  29%|██▉       | 1292/4420 [00:11<00:25, 121.59it/s]\n1299 27.1328 :  29%|██▉       | 1292/4420 [00:11<00:25, 121.59it/s]\n1299 27.1328 :  30%|██▉       | 1305/4420 [00:11<00:25, 121.08it/s]\n1299 27.1328 :  30%|██▉       | 1318/4420 [00:11<00:25, 122.19it/s]\n1319 24.2784 :  30%|██▉       | 1318/4420 [00:11<00:25, 122.19it/s]\n1319 24.2784 :  30%|███       | 1331/4420 [00:11<00:24, 123.76it/s]\n1339 25.8747 :  30%|███       | 1331/4420 [00:11<00:24, 123.76it/s]\n1339 25.8747 :  30%|███       | 1345/4420 [00:11<00:24, 126.71it/s]\n1359 27.8486 :  30%|███       | 1345/4420 [00:11<00:24, 126.71it/s]\n1359 27.8486 :  31%|███       | 1360/4420 [00:11<00:23, 132.70it/s]\n1359 27.8486 :  31%|███       | 1376/4420 [00:12<00:22, 138.09it/s]\n1379 20.8167 :  31%|███       | 1376/4420 [00:12<00:22, 138.09it/s]\n1379 20.8167 :  31%|███▏      | 1391/4420 [00:12<00:21, 139.13it/s]\n1399 23.8203 :  31%|███▏      | 1391/4420 [00:12<00:21, 139.13it/s]\n1399 23.8203 :  32%|███▏      | 1406/4420 [00:12<00:21, 139.50it/s]\n1419 24.2159 :  32%|███▏      | 1406/4420 [00:12<00:21, 139.50it/s]\n1419 24.2159 :  32%|███▏      | 1420/4420 [00:12<00:21, 139.55it/s]\n1419 24.2159 :  32%|███▏      | 1434/4420 [00:12<00:21, 137.57it/s]\n1439 25.8263 :  32%|███▏      | 1434/4420 [00:12<00:21, 137.57it/s]\n1439 25.8263 :  33%|███▎      | 1448/4420 [00:12<00:21, 137.23it/s]\n1459 23.7173 :  33%|███▎      | 1448/4420 [00:12<00:21, 137.23it/s]\n1459 23.7173 :  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[00:13<00:18, 149.05it/s]\n1599 25.9814 :  36%|███▌      | 1590/4420 [00:13<00:18, 149.05it/s]\n1599 25.9814 :  36%|███▋      | 1605/4420 [00:13<00:18, 149.01it/s]\n1619 23.5912 :  36%|███▋      | 1605/4420 [00:13<00:18, 149.01it/s]\n1619 23.5912 :  37%|███▋      | 1621/4420 [00:13<00:18, 149.95it/s]\n1619 23.5912 :  37%|███▋      | 1637/4420 [00:13<00:18, 152.38it/s]\n1639 28.3440 :  37%|███▋      | 1637/4420 [00:13<00:18, 152.38it/s]\n1639 28.3440 :  37%|███▋      | 1654/4420 [00:13<00:17, 156.57it/s]\n1659 25.9747 :  37%|███▋      | 1654/4420 [00:13<00:17, 156.57it/s]\n1659 25.9747 :  38%|███▊      | 1673/4420 [00:14<00:16, 165.05it/s]\n1679 20.2391 :  38%|███▊      | 1673/4420 [00:14<00:16, 165.05it/s]\n1679 20.2391 :  38%|███▊      | 1690/4420 [00:14<00:16, 163.96it/s]\n1699 22.4082 :  38%|███▊      | 1690/4420 [00:14<00:16, 163.96it/s]\n1699 22.4082 :  39%|███▊      | 1707/4420 [00:14<00:17, 159.19it/s]\n1719 22.6230 :  39%|███▊      | 1707/4420 [00:14<00:17, 159.19it/s]\n1719 22.6230 :  39%|███▉      | 1723/4420 [00:14<00:17, 155.47it/s]\n1719 22.6230 :  39%|███▉      | 1739/4420 [00:14<00:17, 150.71it/s]\n1739 24.4139 :  39%|███▉      | 1739/4420 [00:14<00:17, 150.71it/s]\n1739 24.4139 :  40%|███▉      | 1755/4420 [00:14<00:17, 148.69it/s]\n1759 25.9596 :  40%|███▉      | 1755/4420 [00:14<00:17, 148.69it/s]\n1759 25.9596 :  40%|████      | 1770/4420 [00:14<00:17, 148.35it/s]\n1779 25.3048 :  40%|████      | 1770/4420 [00:14<00:17, 148.35it/s]\n1779 25.3048 :  40%|████      | 1786/4420 [00:14<00:17, 149.99it/s]\n1799 27.7904 :  40%|████      | 1786/4420 [00:14<00:17, 149.99it/s]\n1799 27.7904 :  41%|████      | 1803/4420 [00:14<00:17, 153.63it/s]\n1819 27.5043 :  41%|████      | 1803/4420 [00:15<00:17, 153.63it/s]\n1819 27.5043 :  41%|████      | 1822/4420 [00:15<00:16, 162.16it/s]\n1819 27.5043 :  42%|████▏     | 1839/4420 [00:15<00:16, 159.75it/s]\n1839 20.4783 :  42%|████▏     | 1839/4420 [00:15<00:16, 159.75it/s]\n1839 20.4783 :  42%|████▏     | 1855/4420 [00:15<00:16, 151.79it/s]\n1859 21.8136 :  42%|████▏     | 1855/4420 [00:15<00:16, 151.79it/s]\n1859 21.8136 :  42%|████▏     | 1871/4420 [00:15<00:17, 145.12it/s]\n1879 24.1135 :  42%|████▏     | 1871/4420 [00:15<00:17, 145.12it/s]\n1879 24.1135 :  43%|████▎     | 1886/4420 [00:15<00:18, 139.86it/s]\n1899 24.2979 :  43%|████▎     | 1886/4420 [00:15<00:18, 139.86it/s]\n1899 24.2979 :  43%|████▎     | 1901/4420 [00:15<00:18, 136.76it/s]\n1899 24.2979 :  43%|████▎     | 1915/4420 [00:15<00:18, 135.03it/s]\n1919 21.4593 :  43%|████▎     | 1915/4420 [00:15<00:18, 135.03it/s]\n1919 21.4593 :  44%|████▎     | 1929/4420 [00:15<00:18, 135.39it/s]\n1939 28.1469 :  44%|████▎     | 1929/4420 [00:15<00:18, 135.39it/s]\n1939 28.1469 :  44%|████▍     | 1944/4420 [00:15<00:17, 137.95it/s]\n1959 26.7656 :  44%|████▍     | 1944/4420 [00:16<00:17, 137.95it/s]\n1959 26.7656 :  44%|████▍     | 1960/4420 [00:16<00:17, 143.01it/s]\n1959 26.7656 :  45%|████▍     | 1978/4420 [00:16<00:16, 151.01it/s]\n1979 19.7699 :  45%|████▍     | 1978/4420 [00:16<00:16, 151.01it/s]\n1979 19.7699 :  45%|████▌     | 1994/4420 [00:16<00:16, 143.91it/s]\n1999 21.2935 :  45%|████▌     | 1994/4420 [00:16<00:16, 143.91it/s]\n1999 21.2935 :  45%|████▌     | 2009/4420 [00:16<00:17, 135.42it/s]\n2019 19.5594 :  45%|████▌     | 2009/4420 [00:16<00:17, 135.42it/s]\n2019 19.5594 :  46%|████▌     | 2023/4420 [00:16<00:18, 129.63it/s]\n2019 19.5594 :  46%|████▌     | 2037/4420 [00:16<00:19, 124.95it/s]\n2039 20.9467 :  46%|████▌     | 2037/4420 [00:16<00:19, 124.95it/s]\n2039 20.9467 :  46%|████▋     | 2050/4420 [00:16<00:19, 122.48it/s]\n2059 24.6802 :  46%|████▋     | 2050/4420 [00:16<00:19, 122.48it/s]\n2059 24.6802 :  47%|████▋     | 2063/4420 [00:16<00:19, 120.75it/s]\n2059 24.6802 :  47%|████▋     | 2076/4420 [00:16<00:19, 120.88it/s]\n2079 24.2470 :  47%|████▋     | 2076/4420 [00:16<00:19, 120.88it/s]\n2079 24.2470 :  47%|████▋     | 2089/4420 [00:17<00:18, 123.18it/s]\n2099 25.8745 :  47%|████▋     | 2089/4420 [00:17<00:18, 123.18it/s]\n2099 25.8745 :  48%|████▊     | 2103/4420 [00:17<00:18, 126.08it/s]\n2099 25.8745 :  48%|████▊     | 2119/4420 [00:17<00:17, 133.23it/s]\n2119 26.7481 :  48%|████▊     | 2119/4420 [00:17<00:17, 133.23it/s]\n2119 26.7481 :  48%|████▊     | 2135/4420 [00:17<00:16, 138.24it/s]\n2139 18.9922 :  48%|████▊     | 2135/4420 [00:17<00:16, 138.24it/s]\n2139 18.9922 :  49%|████▊     | 2149/4420 [00:17<00:17, 126.55it/s]\n2159 19.3110 :  49%|████▊     | 2149/4420 [00:17<00:17, 126.55it/s]\n2159 19.3110 :  49%|████▉     | 2162/4420 [00:17<00:18, 119.16it/s]\n2159 19.3110 :  49%|████▉     | 2175/4420 [00:17<00:19, 113.32it/s]\n2179 20.7273 :  49%|████▉     | 2175/4420 [00:17<00:19, 113.32it/s]\n2179 20.7273 :  49%|████▉     | 2187/4420 [00:17<00:20, 109.90it/s]\n2179 20.7273 :  50%|████▉     | 2199/4420 [00:17<00:20, 109.21it/s]\n2199 22.8412 :  50%|████▉     | 2199/4420 [00:17<00:20, 109.21it/s]\n2199 22.8412 :  50%|█████     | 2210/4420 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24.7388 :  87%|████████▋ | 3836/4420 [00:31<00:04, 127.94it/s]\n3839 27.7641 :  87%|████████▋ | 3836/4420 [00:31<00:04, 127.94it/s]\n3839 27.7641 :  87%|████████▋ | 3849/4420 [00:31<00:04, 124.33it/s]\n3859 30.0464 :  87%|████████▋ | 3849/4420 [00:31<00:04, 124.33it/s]\n3859 30.0464 :  87%|████████▋ | 3862/4420 [00:31<00:04, 123.99it/s]\n3859 30.0464 :  88%|████████▊ | 3875/4420 [00:31<00:04, 123.19it/s]\n3879 28.4566 :  88%|████████▊ | 3875/4420 [00:31<00:04, 123.19it/s]\n3879 28.4566 :  88%|████████▊ | 3888/4420 [00:31<00:04, 122.87it/s]\n3899 29.8585 :  88%|████████▊ | 3888/4420 [00:31<00:04, 122.87it/s]\n3899 29.8585 :  88%|████████▊ | 3901/4420 [00:31<00:04, 124.46it/s]\n3899 29.8585 :  89%|████████▊ | 3916/4420 [00:32<00:03, 129.65it/s]\n3919 29.8810 :  89%|████████▊ | 3916/4420 [00:32<00:03, 129.65it/s]\n3919 29.8810 :  89%|████████▉ | 3931/4420 [00:32<00:03, 135.50it/s]\n3939 31.4354 :  89%|████████▉ | 3931/4420 [00:32<00:03, 135.50it/s]\n3939 31.4354 :  89%|████████▉ | 3946/4420 [00:32<00:03, 139.68it/s]\n3959 22.5124 :  89%|████████▉ | 3946/4420 [00:32<00:03, 139.68it/s]\n3959 22.5124 :  90%|████████▉ | 3961/4420 [00:32<00:03, 141.17it/s]\n3959 22.5124 :  90%|████████▉ | 3976/4420 [00:32<00:03, 137.49it/s]\n3979 26.9736 :  90%|████████▉ | 3976/4420 [00:32<00:03, 137.49it/s]\n3979 26.9736 :  90%|█████████ | 3990/4420 [00:32<00:03, 133.09it/s]\n3999 27.0092 :  90%|█████████ | 3990/4420 [00:32<00:03, 133.09it/s]\n3999 27.0092 :  91%|█████████ | 4004/4420 [00:32<00:03, 128.75it/s]\n3999 27.0092 :  91%|█████████ | 4017/4420 [00:32<00:03, 127.55it/s]\n4019 28.7737 :  91%|█████████ | 4017/4420 [00:32<00:03, 127.55it/s]\n4019 28.7737 :  91%|█████████ | 4030/4420 [00:32<00:03, 126.91it/s]\n4039 27.4116 :  91%|█████████ | 4030/4420 [00:32<00:03, 126.91it/s]\n4039 27.4116 :  91%|█████████▏| 4043/4420 [00:32<00:02, 126.16it/s]\n4039 27.4116 :  92%|█████████▏| 4057/4420 [00:33<00:02, 128.61it/s]\n4059 31.7108 :  92%|█████████▏| 4057/4420 [00:33<00:02, 128.61it/s]\n4059 31.7108 :  92%|█████████▏| 4072/4420 [00:33<00:02, 133.59it/s]\n4079 33.2201 :  92%|█████████▏| 4072/4420 [00:33<00:02, 133.59it/s]\n4079 33.2201 :  92%|█████████▏| 4088/4420 [00:33<00:02, 138.60it/s]\n4099 31.6397 :  92%|█████████▏| 4088/4420 [00:33<00:02, 138.60it/s]\n4099 31.6397 :  93%|█████████▎| 4103/4420 [00:33<00:02, 140.74it/s]\n4099 31.6397 :  93%|█████████▎| 4118/4420 [00:33<00:02, 140.86it/s]\n4119 23.2851 :  93%|█████████▎| 4118/4420 [00:33<00:02, 140.86it/s]\n4119 23.2851 :  94%|█████████▎| 4133/4420 [00:33<00:02, 132.58it/s]\n4139 26.3890 :  94%|█████████▎| 4133/4420 [00:33<00:02, 132.58it/s]\n4139 26.3890 :  94%|█████████▍| 4147/4420 [00:33<00:02, 126.28it/s]\n4159 26.9501 :  94%|█████████▍| 4147/4420 [00:33<00:02, 126.28it/s]\n4159 26.9501 :  94%|█████████▍| 4160/4420 [00:33<00:02, 122.94it/s]\n4159 26.9501 :  94%|█████████▍| 4173/4420 [00:33<00:02, 121.12it/s]\n4179 23.0219 :  94%|█████████▍| 4173/4420 [00:34<00:02, 121.12it/s]\n4179 23.0219 :  95%|█████████▍| 4186/4420 [00:34<00:01, 120.10it/s]\n4179 23.0219 :  95%|█████████▌| 4199/4420 [00:34<00:01, 119.64it/s]\n4199 26.4230 :  95%|█████████▌| 4199/4420 [00:34<00:01, 119.64it/s]\n4199 26.4230 :  95%|█████████▌| 4212/4420 [00:34<00:01, 121.26it/s]\n4219 31.7778 :  95%|█████████▌| 4212/4420 [00:34<00:01, 121.26it/s]\n4219 31.7778 :  96%|█████████▌| 4226/4420 [00:34<00:01, 126.15it/s]\n4239 31.1322 :  96%|█████████▌| 4226/4420 [00:34<00:01, 126.15it/s]\n4239 31.1322 :  96%|█████████▌| 4241/4420 [00:34<00:01, 130.61it/s]\n4239 31.1322 :  96%|█████████▋| 4256/4420 [00:34<00:01, 134.67it/s]\n4259 29.5662 :  96%|█████████▋| 4256/4420 [00:34<00:01, 134.67it/s]\n4259 29.5662 :  97%|█████████▋| 4270/4420 [00:34<00:01, 131.82it/s]\n4279 20.5298 :  97%|█████████▋| 4270/4420 [00:34<00:01, 131.82it/s]\n4279 20.5298 :  97%|█████████▋| 4284/4420 [00:34<00:01, 113.14it/s]\n4279 20.5298 :  97%|█████████▋| 4296/4420 [00:35<00:01, 103.05it/s]\n4299 21.1968 :  97%|█████████▋| 4296/4420 [00:35<00:01, 103.05it/s]\n4299 21.1968 :  97%|█████████▋| 4307/4420 [00:35<00:01, 97.26it/s]\n4299 21.1968 :  98%|█████████▊| 4318/4420 [00:35<00:01, 94.20it/s]\n4319 23.5769 :  98%|█████████▊| 4318/4420 [00:35<00:01, 94.20it/s]\n4319 23.5769 :  98%|█████████▊| 4328/4420 [00:35<00:00, 92.62it/s]\n4319 23.5769 :  98%|█████████▊| 4338/4420 [00:35<00:00, 91.77it/s]\n4339 23.9798 :  98%|█████████▊| 4338/4420 [00:35<00:00, 91.77it/s]\n4339 23.9798 :  98%|█████████▊| 4348/4420 [00:35<00:00, 91.48it/s]\n4339 23.9798 :  99%|█████████▊| 4358/4420 [00:35<00:00, 92.53it/s]\n4359 24.4153 :  99%|█████████▊| 4358/4420 [00:35<00:00, 92.53it/s]\n4359 24.4153 :  99%|█████████▉| 4369/4420 [00:35<00:00, 95.75it/s]\n4379 25.8403 :  99%|█████████▉| 4369/4420 [00:35<00:00, 95.75it/s]\n4379 25.8403 :  99%|█████████▉| 4381/4420 [00:35<00:00, 100.26it/s]\n4379 25.8403 :  99%|█████████▉| 4392/4420 [00:36<00:00, 102.70it/s]\n4399 25.4426 :  99%|█████████▉| 4392/4420 [00:36<00:00, 102.70it/s]\n4399 25.4426 : 100%|█████████▉| 4404/4420 [00:36<00:00, 105.22it/s]\n4399 25.4426 : 100%|█████████▉| 4415/4420 [00:36<00:00, 106.32it/s]\n4419 15.4968 : 100%|█████████▉| 4415/4420 [00:36<00:00, 106.32it/s]\n4419 15.4968 : 100%|██████████| 4420/4420 [00:36<00:00, 121.72it/s]\nic| \"Training epoch: \": 'Training epoch: '\n    epoch_id: 1\n    epoch_size: 22099392\n    batches_per_epoch: 4420\n    batch_size: 5000\n    optim_type: 'ray'\n\n  0%|          | 0/4420 [00:00<?, ?it/s]\n  0%|          | 11/4420 [00:00<00:42, 102.68it/s]\n4439 22.2793 :   0%|          | 11/4420 [00:00<00:42, 102.68it/s]\n4439 22.2793 :   0%|          | 22/4420 [00:00<00:45, 96.88it/s]\n4439 22.2793 :   1%|          | 32/4420 [00:00<00:46, 94.75it/s]\n4459 19.5932 :   1%|          | 32/4420 [00:00<00:46, 94.75it/s]\n4459 19.5932 :   1%|          | 42/4420 [00:00<00:46, 93.98it/s]\n4459 19.5932 :   1%|          | 52/4420 [00:00<00:46, 93.19it/s]\n4479 21.4706 :   1%|          | 52/4420 [00:00<00:46, 93.19it/s]\n4479 21.4706 :   1%|▏         | 62/4420 [00:00<00:48, 90.15it/s]\n4479 21.4706 :   2%|▏         | 72/4420 [00:00<00:48, 89.07it/s]\n4499 22.4807 :   2%|▏         | 72/4420 [00:00<00:48, 89.07it/s]\n4499 22.4807 :   2%|▏         | 81/4420 [00:00<00:49, 87.92it/s]\n4499 22.4807 :   2%|▏         | 90/4420 [00:00<00:50, 86.56it/s]\n4499 22.4807 :   2%|▏         | 99/4420 [00:01<00:49, 87.50it/s]\n4519 22.7482 :   2%|▏         | 99/4420 [00:01<00:49, 87.50it/s]\n4519 22.7482 :   2%|▏         | 109/4420 [00:01<00:48, 89.22it/s]\n4519 22.7482 :   3%|▎         | 119/4420 [00:01<00:46, 91.81it/s]\n4539 23.0472 :   3%|▎         | 119/4420 [00:01<00:46, 91.81it/s]\n4539 23.0472 :   3%|▎         | 129/4420 [00:01<00:45, 93.29it/s]\n4559 21.6970 :   3%|▎         | 129/4420 [00:01<00:45, 93.29it/s]\n4559 21.6970 :   3%|▎         | 140/4420 [00:01<00:44, 96.98it/s]\n4559 21.6970 :   3%|▎         | 152/4420 [00:01<00:41, 103.52it/s]\n4579 22.3786 :   3%|▎         | 152/4420 [00:01<00:41, 103.52it/s]\n4579 22.3786 :   4%|▎         | 163/4420 [00:01<00:40, 104.47it/s]\n4579 22.3786 :   4%|▍         | 174/4420 [00:01<00:41, 103.37it/s]\n4599 25.4847 :   4%|▍         | 174/4420 [00:01<00:41, 103.37it/s]\n4599 25.4847 :   4%|▍         | 185/4420 [00:01<00:41, 101.35it/s]\n4599 25.4847 :   4%|▍         | 196/4420 [00:02<00:41, 101.20it/s]\n4619 25.5097 :   4%|▍         | 196/4420 [00:02<00:41, 101.20it/s]\n4619 25.5097 :   5%|▍         | 207/4420 [00:02<00:42, 99.92it/s]\n4619 25.5097 :   5%|▍         | 218/4420 [00:02<00:42, 98.64it/s]\n4639 29.1739 :   5%|▍         | 218/4420 [00:02<00:42, 98.64it/s]\n4639 29.1739 :   5%|▌         | 228/4420 [00:02<00:43, 97.33it/s]\n4639 29.1739 :   5%|▌         | 238/4420 [00:02<00:42, 97.50it/s]\n4659 28.3746 :   5%|▌         | 238/4420 [00:02<00:42, 97.50it/s]\n4659 28.3746 :   6%|▌         | 248/4420 [00:02<00:42, 97.99it/s]\n4659 28.3746 :   6%|▌         | 259/4420 [00:02<00:41, 99.11it/s]\n4679 28.3165 :   6%|▌         | 259/4420 [00:02<00:41, 99.11it/s]\n4679 28.3165 :   6%|▌         | 270/4420 [00:02<00:41, 100.09it/s]\n4699 27.5854 :   6%|▌         | 270/4420 [00:02<00:41, 100.09it/s]\n4699 27.5854 :   6%|▋         | 281/4420 [00:02<00:40, 101.07it/s]\n4699 27.5854 :   7%|▋         | 293/4420 [00:03<00:39, 105.74it/s]\n4719 28.4761 :   7%|▋         | 293/4420 [00:03<00:39, 105.74it/s]\n4719 28.4761 :   7%|▋         | 306/4420 [00:03<00:36, 111.31it/s]\n4719 28.4761 :   7%|▋         | 319/4420 [00:03<00:35, 116.18it/s]\n4739 26.7542 :   7%|▋         | 319/4420 [00:03<00:35, 116.18it/s]\n4739 26.7542 :   8%|▊         | 332/4420 [00:03<00:34, 117.34it/s]\n4759 26.3466 :   8%|▊         | 332/4420 [00:03<00:34, 117.34it/s]\n4759 26.3466 :   8%|▊         | 344/4420 [00:03<00:34, 118.05it/s]\n4759 26.3466 :   8%|▊         | 357/4420 [00:03<00:34, 118.69it/s]\n4779 26.1676 :   8%|▊         | 357/4420 [00:03<00:34, 118.69it/s]\n4779 26.1676 :   8%|▊         | 369/4420 [00:03<00:34, 116.36it/s]\n4799 28.3293 :   8%|▊         | 369/4420 [00:03<00:34, 116.36it/s]\n4799 28.3293 :   9%|▊         | 381/4420 [00:03<00:35, 114.82it/s]\n4799 28.3293 :   9%|▉         | 393/4420 [00:03<00:35, 113.86it/s]\n4819 30.4026 :   9%|▉         | 393/4420 [00:03<00:35, 113.86it/s]\n4819 30.4026 :   9%|▉         | 405/4420 [00:03<00:35, 113.52it/s]\n4819 30.4026 :   9%|▉         | 417/4420 [00:04<00:34, 114.78it/s]\n4839 30.0284 :   9%|▉         | 417/4420 [00:04<00:34, 114.78it/s]\n4839 30.0284 :  10%|▉         | 430/4420 [00:04<00:34, 116.92it/s]\n4859 27.6325 :  10%|▉         | 430/4420 [00:04<00:34, 116.92it/s]\n4859 27.6325 :  10%|█         | 444/4420 [00:04<00:32, 121.48it/s]\n4859 27.6325 :  10%|█         | 459/4420 [00:04<00:30, 128.55it/s]\n4879 25.8947 :  10%|█         | 459/4420 [00:04<00:30, 128.55it/s]\n4879 25.8947 :  11%|█         | 473/4420 [00:04<00:30, 129.16it/s]\n4899 28.6376 :  11%|█         | 473/4420 [00:04<00:30, 129.16it/s]\n4899 28.6376 :  11%|█         | 486/4420 [00:04<00:30, 128.13it/s]\n4899 28.6376 :  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129.90it/s]\n5019 29.8732 :  14%|█▍        | 618/4420 [00:05<00:28, 131.97it/s]\n5039 25.8078 :  14%|█▍        | 618/4420 [00:05<00:28, 131.97it/s]\n5039 25.8078 :  14%|█▍        | 632/4420 [00:05<00:29, 129.86it/s]\n5059 28.1601 :  14%|█▍        | 632/4420 [00:05<00:29, 129.86it/s]\n5059 28.1601 :  15%|█▍        | 646/4420 [00:05<00:29, 127.54it/s]\n5059 28.1601 :  15%|█▍        | 659/4420 [00:05<00:29, 126.63it/s]\n5079 24.3957 :  15%|█▍        | 659/4420 [00:05<00:29, 126.63it/s]\n5079 24.3957 :  15%|█▌        | 672/4420 [00:06<00:30, 123.37it/s]\n5099 26.3775 :  15%|█▌        | 672/4420 [00:06<00:30, 123.37it/s]\n5099 26.3775 :  15%|█▌        | 685/4420 [00:06<00:30, 121.35it/s]\n5099 26.3775 :  16%|█▌        | 698/4420 [00:06<00:30, 120.94it/s]\n5119 28.8383 :  16%|█▌        | 698/4420 [00:06<00:30, 120.94it/s]\n5119 28.8383 :  16%|█▌        | 711/4420 [00:06<00:30, 121.18it/s]\n5139 28.0307 :  16%|█▌        | 711/4420 [00:06<00:30, 121.18it/s]\n5139 28.0307 :  16%|█▋        | 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Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n  warnings.warn(\n/root/.pyenv/versions/3.8.8/lib/python3.8/site-packages/torch/nn/functional.py:3657: UserWarning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and now uses scale_factor directly, instead of relying on the computed output size. If you wish to restore the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.\n  warnings.warn(\n/root/.pyenv/versions/3.8.8/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n\n  3%|▎         | 1/29 [00:01<00:51,  1.83s/it]\n  7%|▋         | 2/29 [00:03<00:47,  1.75s/it]\n 10%|█         | 3/29 [00:05<00:44,  1.72s/it]\n 14%|█▍        | 4/29 [00:06<00:38,  1.55s/it]\n 17%|█▋        | 5/29 [00:08<00:36,  1.53s/it]\n 21%|██        | 6/29 [00:09<00:35,  1.53s/it]\n 24%|██▍       | 7/29 [00:10<00:31,  1.44s/it]\n 28%|██▊       | 8/29 [00:12<00:33,  1.59s/it]\n 31%|███       | 9/29 [00:13<00:29,  1.45s/it]\n 34%|███▍      | 10/29 [00:15<00:26,  1.42s/it]\n 38%|███▊      | 11/29 [00:16<00:25,  1.41s/it]\n 41%|████▏     | 12/29 [00:17<00:21,  1.29s/it]\n 45%|████▍     | 13/29 [00:18<00:20,  1.26s/it]\n 48%|████▊     | 14/29 [00:20<00:20,  1.34s/it]\n 52%|█████▏    | 15/29 [00:21<00:17,  1.26s/it]\n 55%|█████▌    | 16/29 [00:23<00:19,  1.47s/it]\n 59%|█████▊    | 17/29 [00:24<00:17,  1.48s/it]\n 62%|██████▏   | 18/29 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1.44s/it]\nic| \"Training epoch: \": 'Training epoch: '\n    epoch_id: 3\n    epoch_size: 29\n    batches_per_epoch: 29\n    batch_size: 1\n    optim_type: 'image'\n\n  0%|          | 0/29 [00:00<?, ?it/s]\n  3%|▎         | 1/29 [00:01<00:53,  1.92s/it]\n  7%|▋         | 2/29 [00:03<00:51,  1.92s/it]\n 10%|█         | 3/29 [00:05<00:43,  1.68s/it]\n 14%|█▍        | 4/29 [00:07<00:44,  1.78s/it]\n 17%|█▋        | 5/29 [00:08<00:40,  1.67s/it]\n 21%|██        | 6/29 [00:09<00:34,  1.50s/it]\n 24%|██▍       | 7/29 [00:11<00:37,  1.70s/it]\n 28%|██▊       | 8/29 [00:13<00:32,  1.54s/it]\n 31%|███       | 9/29 [00:14<00:29,  1.49s/it]\n 34%|███▍      | 10/29 [00:15<00:27,  1.45s/it]\n 38%|███▊      | 11/29 [00:17<00:29,  1.65s/it]\n 41%|████▏     | 12/29 [00:18<00:24,  1.45s/it]\n 45%|████▍     | 13/29 [00:20<00:22,  1.40s/it]\n 48%|████▊     | 14/29 [00:21<00:20,  1.36s/it]\n 52%|█████▏    | 15/29 [00:22<00:19,  1.39s/it]\n 55%|█████▌    | 16/29 [00:23<00:16,  1.26s/it]\n 59%|█████▊    | 17/29 [00:24<00:14,  1.20s/it]\n 62%|██████▏   | 18/29 [00:26<00:13,  1.26s/it]\n 66%|██████▌   | 19/29 [00:27<00:12,  1.25s/it]\n8888 0.2967 0.0170 0.0027 :  66%|██████▌   | 19/29 [00:28<00:12,  1.25s/it]\n8888 0.2967 0.0170 0.0027 :  69%|██████▉   | 20/29 [00:28<00:11,  1.25s/it]\n8888 0.2967 0.0170 0.0027 :  72%|███████▏  | 21/29 [00:30<00:11,  1.38s/it]\n8888 0.2967 0.0170 0.0027 :  76%|███████▌  | 22/29 [00:32<00:10,  1.53s/it]\n8888 0.2967 0.0170 0.0027 :  79%|███████▉  | 23/29 [00:33<00:08,  1.41s/it]\n8888 0.2967 0.0170 0.0027 :  83%|████████▎ | 24/29 [00:34<00:06,  1.33s/it]\n8888 0.2967 0.0170 0.0027 :  86%|████████▌ | 25/29 [00:36<00:05,  1.33s/it]\n8888 0.2967 0.0170 0.0027 :  90%|████████▉ | 26/29 [00:36<00:03,  1.21s/it]\n8888 0.2967 0.0170 0.0027 :  93%|█████████▎| 27/29 [00:38<00:02,  1.24s/it]\n8888 0.2967 0.0170 0.0027 :  97%|█████████▋| 28/29 [00:40<00:01,  1.50s/it]\n8888 0.2967 0.0170 0.0027 : 100%|██████████| 29/29 [00:41<00:00,  1.39s/it]\n8888 0.2967 0.0170 0.0027 : 100%|██████████| 29/29 [00:41<00:00,  1.43s/it]\nic| \"Training epoch: \": 'Training epoch: '\n    epoch_id: 4\n    epoch_size: 29\n    batches_per_epoch: 29\n    batch_size: 1\n    optim_type: 'image'\n\n  0%|          | 0/29 [00:00<?, ?it/s]\n  3%|▎         | 1/29 [00:01<00:52,  1.86s/it]\n  7%|▋         | 2/29 [00:03<00:41,  1.55s/it]\n 10%|█         | 3/29 [00:04<00:37,  1.44s/it]\n 14%|█▍        | 4/29 [00:06<00:40,  1.63s/it]\n 17%|█▋        | 5/29 [00:07<00:37,  1.57s/it]\n 21%|██        | 6/29 [00:09<00:35,  1.54s/it]\n 24%|██▍       | 7/29 [00:10<00:32,  1.47s/it]\n 28%|██▊       | 8/29 [00:11<00:27,  1.33s/it]\n 31%|███       | 9/29 [00:13<00:26,  1.35s/it]\n 34%|███▍      | 10/29 [00:14<00:25,  1.33s/it]\n 38%|███▊      | 11/29 [00:15<00:23,  1.31s/it]\n 41%|████▏     | 12/29 [00:17<00:24,  1.43s/it]\n 45%|████▍     | 13/29 [00:18<00:21,  1.34s/it]\n 48%|████▊     | 14/29 [00:19<00:19,  1.29s/it]\n 52%|█████▏    | 15/29 [00:21<00:20,  1.48s/it]\n 55%|█████▌    | 16/29 [00:22<00:18,  1.42s/it]\n 59%|█████▊    | 17/29 [00:24<00:16,  1.34s/it]\n 62%|██████▏   | 18/29 [00:25<00:14,  1.36s/it]\n 66%|██████▌   | 19/29 [00:26<00:13,  1.35s/it]\n8917 0.2887 0.0188 0.0037 :  66%|██████▌   | 19/29 [00:27<00:13,  1.35s/it]\n8917 0.2887 0.0188 0.0037 :  69%|██████▉   | 20/29 [00:27<00:11,  1.23s/it]\n8917 0.2887 0.0188 0.0037 :  72%|███████▏  | 21/29 [00:29<00:10,  1.34s/it]\n8917 0.2887 0.0188 0.0037 :  76%|███████▌  | 22/29 [00:30<00:08,  1.24s/it]\n8917 0.2887 0.0188 0.0037 :  79%|███████▉  | 23/29 [00:32<00:08,  1.39s/it]\n8917 0.2887 0.0188 0.0037 :  83%|████████▎ | 24/29 [00:33<00:06,  1.37s/it]\n8917 0.2887 0.0188 0.0037 :  86%|████████▌ | 25/29 [00:34<00:05,  1.26s/it]\n8917 0.2887 0.0188 0.0037 :  90%|████████▉ | 26/29 [00:36<00:04,  1.40s/it]\n8917 0.2887 0.0188 0.0037 :  93%|█████████▎| 27/29 [00:37<00:02,  1.35s/it]\n8917 0.2887 0.0188 0.0037 :  97%|█████████▋| 28/29 [00:39<00:01,  1.46s/it]\n8917 0.2887 0.0188 0.0037 : 100%|██████████| 29/29 [00:40<00:00,  1.30s/it]\n8917 0.2887 0.0188 0.0037 : 100%|██████████| 29/29 [00:40<00:00,  1.38s/it]\nic| \"Training epoch: \": 'Training epoch: '\n    epoch_id: 5\n    epoch_size: 29\n    batches_per_epoch: 29\n    batch_size: 1\n    optim_type: 'image'\n\n  0%|          | 0/29 [00:00<?, ?it/s]\n  3%|▎         | 1/29 [00:01<00:46,  1.67s/it]\n  7%|▋         | 2/29 [00:02<00:37,  1.37s/it]\n 10%|█         | 3/29 [00:04<00:38,  1.47s/it]\n 14%|█▍        | 4/29 [00:05<00:33,  1.36s/it]\n 17%|█▋        | 5/29 [00:07<00:34,  1.44s/it]\n 21%|██        | 6/29 [00:08<00:29,  1.29s/it]\n 24%|██▍       | 7/29 [00:09<00:31,  1.42s/it]\n 28%|██▊       | 8/29 [00:11<00:31,  1.48s/it]\n 31%|███       | 9/29 [00:12<00:29,  1.48s/it]\n 34%|███▍      | 10/29 [00:14<00:26,  1.37s/it]\n 38%|███▊      | 11/29 [00:15<00:24,  1.34s/it]\n 41%|████▏     | 12/29 [00:16<00:23,  1.36s/it]\n 45%|████▍     | 13/29 [00:18<00:23,  1.47s/it]\n 48%|████▊     | 14/29 [00:20<00:24,  1.61s/it]\n 52%|█████▏    | 15/29 [00:21<00:22,  1.57s/it]\n 55%|█████▌    | 16/29 [00:23<00:19,  1.52s/it]\n 59%|█████▊    | 17/29 [00:24<00:16,  1.41s/it]\n 62%|██████▏   | 18/29 [00:25<00:15,  1.38s/it]\n 66%|██████▌   | 19/29 [00:26<00:13,  1.31s/it]\n8946 0.2648 0.0200 0.0040 :  66%|██████▌   | 19/29 [00:28<00:13,  1.31s/it]\n8946 0.2648 0.0200 0.0040 :  69%|██████▉   | 20/29 [00:28<00:11,  1.26s/it]\n8946 0.2648 0.0200 0.0040 :  72%|███████▏  | 21/29 [00:29<00:09,  1.19s/it]\n8946 0.2648 0.0200 0.0040 :  76%|███████▌  | 22/29 [00:30<00:08,  1.24s/it]\n8946 0.2648 0.0200 0.0040 :  79%|███████▉  | 23/29 [00:32<00:08,  1.37s/it]\n8946 0.2648 0.0200 0.0040 :  83%|████████▎ | 24/29 [00:33<00:07,  1.47s/it]\n8946 0.2648 0.0200 0.0040 :  86%|████████▌ | 25/29 [00:35<00:05,  1.46s/it]\n8946 0.2648 0.0200 0.0040 :  90%|████████▉ | 26/29 [00:36<00:04,  1.43s/it]\n8946 0.2648 0.0200 0.0040 :  93%|█████████▎| 27/29 [00:38<00:03,  1.52s/it]\n8946 0.2648 0.0200 0.0040 :  97%|█████████▋| 28/29 [00:39<00:01,  1.37s/it]\n8946 0.2648 0.0200 0.0040 : 100%|██████████| 29/29 [00:40<00:00,  1.37s/it]\n8946 0.2648 0.0200 0.0040 : 100%|██████████| 29/29 [00:40<00:00,  1.41s/it]\nic| \"Training epoch: \": 'Training epoch: '\n    epoch_id: 6\n    epoch_size: 29\n    batches_per_epoch: 29\n    batch_size: 1\n    optim_type: 'image'\n\n  0%|          | 0/29 [00:00<?, ?it/s]\n  3%|▎         | 1/29 [00:01<00:38,  1.37s/it]\n  7%|▋         | 2/29 [00:02<00:33,  1.24s/it]\n 10%|█         | 3/29 [00:03<00:31,  1.21s/it]\n 14%|█▍        | 4/29 [00:05<00:39,  1.56s/it]\n 17%|█▋        | 5/29 [00:07<00:37,  1.54s/it]\n 21%|██        | 6/29 [00:08<00:33,  1.46s/it]\n 24%|██▍       | 7/29 [00:09<00:28,  1.31s/it]\n 28%|██▊       | 8/29 [00:11<00:29,  1.43s/it]\n 31%|███       | 9/29 [00:12<00:30,  1.51s/it]\n 34%|███▍      | 10/29 [00:14<00:28,  1.49s/it]\n 38%|███▊      | 11/29 [00:15<00:25,  1.42s/it]\n 41%|████▏     | 12/29 [00:17<00:24,  1.43s/it]\n 45%|████▍     | 13/29 [00:18<00:23,  1.45s/it]\n 48%|████▊     | 14/29 [00:19<00:19,  1.32s/it]\n 52%|█████▏    | 15/29 [00:20<00:17,  1.23s/it]\n 55%|█████▌    | 16/29 [00:22<00:18,  1.44s/it]\n 59%|█████▊    | 17/29 [00:23<00:16,  1.40s/it]\n 62%|██████▏   | 18/29 [00:24<00:13,  1.26s/it]\n 66%|██████▌   | 19/29 [00:25<00:11,  1.19s/it]\n8975 0.2575 0.0204 0.0037 :  66%|██████▌   | 19/29 [00:27<00:11,  1.19s/it]\n8975 0.2575 0.0204 0.0037 :  69%|██████▉   | 20/29 [00:27<00:11,  1.28s/it]\n8975 0.2575 0.0204 0.0037 :  72%|███████▏  | 21/29 [00:28<00:09,  1.20s/it]\n8975 0.2575 0.0204 0.0037 :  76%|███████▌  | 22/29 [00:30<00:10,  1.47s/it]\n8975 0.2575 0.0204 0.0037 :  79%|███████▉  | 23/29 [00:31<00:08,  1.41s/it]\n8975 0.2575 0.0204 0.0037 :  83%|████████▎ | 24/29 [00:33<00:07,  1.50s/it]\n8975 0.2575 0.0204 0.0037 :  86%|████████▌ | 25/29 [00:34<00:05,  1.34s/it]\n8975 0.2575 0.0204 0.0037 :  90%|████████▉ | 26/29 [00:35<00:03,  1.26s/it]\n8975 0.2575 0.0204 0.0037 :  93%|█████████▎| 27/29 [00:37<00:02,  1.40s/it]\n8975 0.2575 0.0204 0.0037 :  97%|█████████▋| 28/29 [00:38<00:01,  1.35s/it]\n8975 0.2575 0.0204 0.0037 : 100%|██████████| 29/29 [00:40<00:00,  1.46s/it]\n8975 0.2575 0.0204 0.0037 : 100%|██████████| 29/29 [00:40<00:00,  1.39s/it]\nic| \"Training epoch: \": 'Training epoch: '\n    epoch_id: 7\n    epoch_size: 29\n    batches_per_epoch: 29\n    batch_size: 1\n    optim_type: 'image'\n\n  0%|          | 0/29 [00:00<?, ?it/s]\n  3%|▎         | 1/29 [00:00<00:26,  1.04it/s]\n  7%|▋         | 2/29 [00:01<00:26,  1.03it/s]\n 10%|█         | 3/29 [00:03<00:32,  1.25s/it]\n 14%|█▍        | 4/29 [00:04<00:32,  1.28s/it]\n 17%|█▋        | 5/29 [00:06<00:30,  1.27s/it]\n 21%|██        | 6/29 [00:07<00:27,  1.20s/it]\n 24%|██▍       | 7/29 [00:08<00:29,  1.32s/it]\n 28%|██▊       | 8/29 [00:10<00:27,  1.31s/it]\n 31%|███       | 9/29 [00:11<00:26,  1.30s/it]\n 34%|███▍      | 10/29 [00:12<00:25,  1.33s/it]\n 38%|███▊      | 11/29 [00:14<00:23,  1.33s/it]\n 41%|████▏     | 12/29 [00:16<00:26,  1.56s/it]\n 45%|████▍     | 13/29 [00:17<00:22,  1.39s/it]\n 48%|████▊     | 14/29 [00:18<00:20,  1.34s/it]\n 52%|█████▏    | 15/29 [00:19<00:18,  1.33s/it]\n 55%|█████▌    | 16/29 [00:21<00:17,  1.38s/it]\n 59%|█████▊    | 17/29 [00:22<00:16,  1.37s/it]\n 62%|██████▏   | 18/29 [00:24<00:16,  1.47s/it]\n 66%|██████▌   | 19/29 [00:26<00:15,  1.60s/it]\n9004 0.2485 0.0214 0.0037 :  66%|██████▌   | 19/29 [00:27<00:15,  1.60s/it]\n9004 0.2485 0.0214 0.0037 :  69%|██████▉   | 20/29 [00:27<00:14,  1.63s/it]\n9004 0.2485 0.0214 0.0037 :  72%|███████▏  | 21/29 [00:29<00:12,  1.58s/it]\n9004 0.2485 0.0214 0.0037 :  76%|███████▌  | 22/29 [00:30<00:10,  1.54s/it]\n9004 0.2485 0.0214 0.0037 :  79%|███████▉  | 23/29 [00:31<00:08,  1.39s/it]\n9004 0.2485 0.0214 0.0037 :  83%|████████▎ | 24/29 [00:32<00:06,  1.32s/it]\n9004 0.2485 0.0214 0.0037 :  86%|████████▌ | 25/29 [00:34<00:05,  1.32s/it]\n9004 0.2485 0.0214 0.0037 :  90%|████████▉ | 26/29 [00:35<00:03,  1.31s/it]\n9004 0.2485 0.0214 0.0037 :  93%|█████████▎| 27/29 [00:37<00:02,  1.39s/it]\n9004 0.2485 0.0214 0.0037 :  97%|█████████▋| 28/29 [00:38<00:01,  1.32s/it]\n9004 0.2485 0.0214 0.0037 : 100%|██████████| 29/29 [00:39<00:00,  1.32s/it]\n9004 0.2485 0.0214 0.0037 : 100%|██████████| 29/29 [00:39<00:00,  1.36s/it]\nic| \"Training epoch: \": 'Training epoch: '\n    epoch_id: 8\n    epoch_size: 29\n    batches_per_epoch: 29\n    batch_size: 1\n    optim_type: 'image'\n\n  0%|          | 0/29 [00:00<?, ?it/s]\n  3%|▎         | 1/29 [00:01<00:29,  1.04s/it]\n  7%|▋         | 2/29 [00:02<00:38,  1.42s/it]\n 10%|█         | 3/29 [00:04<00:35,  1.38s/it]\n 14%|█▍        | 4/29 [00:05<00:34,  1.36s/it]\n 17%|█▋        | 5/29 [00:07<00:35,  1.48s/it]\n 21%|██        | 6/29 [00:08<00:35,  1.56s/it]\n 24%|██▍       | 7/29 [00:10<00:38,  1.73s/it]\n 28%|██▊       | 8/29 [00:12<00:32,  1.55s/it]\n 31%|███       | 9/29 [00:13<00:27,  1.40s/it]\n 34%|███▍      | 10/29 [00:14<00:27,  1.43s/it]\n 38%|███▊      | 11/29 [00:16<00:26,  1.48s/it]\n 41%|████▏     | 12/29 [00:18<00:27,  1.61s/it]\n 45%|████▍     | 13/29 [00:20<00:27,  1.70s/it]\n 48%|████▊     | 14/29 [00:21<00:22,  1.49s/it]\n 52%|█████▏    | 15/29 [00:21<00:18,  1.33s/it]\n 55%|█████▌    | 16/29 [00:23<00:17,  1.31s/it]\n 59%|█████▊    | 17/29 [00:25<00:17,  1.50s/it]\n 62%|██████▏   | 18/29 [00:26<00:16,  1.49s/it]\n 66%|██████▌   | 19/29 [00:27<00:13,  1.35s/it]\n9033 0.2691 0.0196 0.0029 :  66%|██████▌   | 19/29 [00:29<00:13,  1.35s/it]\n9033 0.2691 0.0196 0.0029 :  69%|██████▉   | 20/29 [00:29<00:14,  1.58s/it]\n9033 0.2691 0.0196 0.0029 :  72%|███████▏  | 21/29 [00:30<00:11,  1.40s/it]\n9033 0.2691 0.0196 0.0029 :  76%|███████▌  | 22/29 [00:32<00:10,  1.46s/it]\n9033 0.2691 0.0196 0.0029 :  79%|███████▉  | 23/29 [00:33<00:08,  1.40s/it]\n9033 0.2691 0.0196 0.0029 :  83%|████████▎ | 24/29 [00:34<00:06,  1.39s/it]\n9033 0.2691 0.0196 0.0029 :  86%|████████▌ | 25/29 [00:36<00:05,  1.37s/it]\n9033 0.2691 0.0196 0.0029 :  90%|████████▉ | 26/29 [00:37<00:04,  1.36s/it]\n9033 0.2691 0.0196 0.0029 :  93%|█████████▎| 27/29 [00:39<00:02,  1.37s/it]\n9033 0.2691 0.0196 0.0029 :  97%|█████████▋| 28/29 [00:40<00:01,  1.36s/it]\n9033 0.2691 0.0196 0.0029 : 100%|██████████| 29/29 [00:41<00:00,  1.33s/it]\n9033 0.2691 0.0196 0.0029 : 100%|██████████| 29/29 [00:41<00:00,  1.44s/it]\nic| \"Training epoch: \": 'Training epoch: '\n    epoch_id: 9\n    epoch_size: 29\n    batches_per_epoch: 29\n    batch_size: 1\n    optim_type: 'image'\n\n  0%|          | 0/29 [00:00<?, ?it/s]\n  3%|▎         | 1/29 [00:02<00:58,  2.08s/it]\n  7%|▋         | 2/29 [00:03<00:46,  1.71s/it]\n 10%|█         | 3/29 [00:04<00:37,  1.46s/it]\n 14%|█▍        | 4/29 [00:05<00:32,  1.30s/it]\n 17%|█▋        | 5/29 [00:06<00:30,  1.26s/it]\n 21%|██        | 6/29 [00:08<00:34,  1.48s/it]\n 24%|██▍       | 7/29 [00:10<00:31,  1.43s/it]\n 28%|██▊       | 8/29 [00:12<00:33,  1.59s/it]\n 31%|███       | 9/29 [00:13<00:28,  1.43s/it]\n 34%|███▍      | 10/29 [00:14<00:26,  1.40s/it]\n 38%|███▊      | 11/29 [00:16<00:26,  1.46s/it]\n 41%|████▏     | 12/29 [00:18<00:28,  1.66s/it]\n 45%|████▍     | 13/29 [00:20<00:27,  1.73s/it]\n 48%|████▊     | 14/29 [00:21<00:23,  1.56s/it]\n 52%|█████▏    | 15/29 [00:22<00:20,  1.45s/it]\n 55%|█████▌    | 16/29 [00:23<00:19,  1.47s/it]\n 59%|█████▊    | 17/29 [00:25<00:18,  1.53s/it]\n 62%|██████▏   | 18/29 [00:26<00:16,  1.46s/it]\n 66%|██████▌   | 19/29 [00:27<00:13,  1.33s/it]\n9062 0.2432 0.0218 0.0053 :  66%|██████▌   | 19/29 [00:29<00:13,  1.33s/it]\n9062 0.2432 0.0218 0.0053 :  69%|██████▉   | 20/29 [00:29<00:11,  1.24s/it]\n9062 0.2432 0.0218 0.0053 :  72%|███████▏  | 21/29 [00:30<00:10,  1.27s/it]\n9062 0.2432 0.0218 0.0053 :  76%|███████▌  | 22/29 [00:31<00:08,  1.28s/it]\n9062 0.2432 0.0218 0.0053 :  79%|███████▉  | 23/29 [00:32<00:07,  1.22s/it]\n9062 0.2432 0.0218 0.0053 :  83%|████████▎ | 24/29 [00:33<00:06,  1.23s/it]\n9062 0.2432 0.0218 0.0053 :  86%|████████▌ | 25/29 [00:35<00:05,  1.34s/it]\n9062 0.2432 0.0218 0.0053 :  90%|████████▉ | 26/29 [00:36<00:03,  1.31s/it]\n9062 0.2432 0.0218 0.0053 :  93%|█████████▎| 27/29 [00:38<00:02,  1.39s/it]\n9062 0.2432 0.0218 0.0053 :  97%|█████████▋| 28/29 [00:39<00:01,  1.32s/it]\n9062 0.2432 0.0218 0.0053 : 100%|██████████| 29/29 [00:41<00:00,  1.50s/it]\n9062 0.2432 0.0218 0.0053 : 100%|██████████| 29/29 [00:41<00:00,  1.43s/it]\nic| \"Training epoch: \": 'Training epoch: '\n    epoch_id: 10\n    epoch_size: 29\n    batches_per_epoch: 29\n    batch_size: 1\n    optim_type: 'image'\n\n  0%|          | 0/29 [00:00<?, ?it/s]\n  3%|▎         | 1/29 [00:01<00:53,  1.89s/it]\n  7%|▋         | 2/29 [00:03<00:46,  1.72s/it]\n 10%|█         | 3/29 [00:05<00:49,  1.90s/it]\n 14%|█▍        | 4/29 [00:07<00:44,  1.78s/it]\n 17%|█▋        | 5/29 [00:08<00:35,  1.48s/it]\n 21%|██        | 6/29 [00:09<00:30,  1.31s/it]\n 24%|██▍       | 7/29 [00:10<00:27,  1.23s/it]\n 28%|██▊       | 8/29 [00:11<00:28,  1.35s/it]\n 31%|███       | 9/29 [00:13<00:31,  1.59s/it]\n 34%|███▍      | 10/29 [00:15<00:28,  1.51s/it]\n 38%|███▊      | 11/29 [00:16<00:26,  1.46s/it]\n 41%|████▏     | 12/29 [00:17<00:22,  1.31s/it]\n 45%|████▍     | 13/29 [00:18<00:20,  1.29s/it]\n 48%|████▊     | 14/29 [00:19<00:18,  1.22s/it]\n 52%|█████▏    | 15/29 [00:21<00:18,  1.30s/it]\n 55%|█████▌    | 16/29 [00:22<00:17,  1.33s/it]\n 59%|█████▊    | 17/29 [00:24<00:16,  1.41s/it]\n 62%|██████▏   | 18/29 [00:25<00:15,  1.39s/it]\n 66%|██████▌   | 19/29 [00:27<00:14,  1.45s/it]\n9091 0.2858 0.0184 0.0027 :  66%|██████▌   | 19/29 [00:29<00:14,  1.45s/it]\n9091 0.2858 0.0184 0.0027 :  69%|██████▉   | 20/29 [00:29<00:14,  1.58s/it]\n9091 0.2858 0.0184 0.0027 :  72%|███████▏  | 21/29 [00:30<00:12,  1.55s/it]\n9091 0.2858 0.0184 0.0027 :  76%|███████▌  | 22/29 [00:32<00:11,  1.60s/it]\n9091 0.2858 0.0184 0.0027 :  79%|███████▉  | 23/29 [00:33<00:08,  1.42s/it]\n9091 0.2858 0.0184 0.0027 :  83%|████████▎ | 24/29 [00:34<00:06,  1.34s/it]\n9091 0.2858 0.0184 0.0027 :  86%|████████▌ | 25/29 [00:36<00:06,  1.51s/it]\n9091 0.2858 0.0184 0.0027 :  90%|████████▉ | 26/29 [00:37<00:04,  1.35s/it]\n9091 0.2858 0.0184 0.0027 :  93%|█████████▎| 27/29 [00:38<00:02,  1.29s/it]\n9091 0.2858 0.0184 0.0027 :  97%|█████████▋| 28/29 [00:39<00:01,  1.30s/it]\n9091 0.2858 0.0184 0.0027 : 100%|██████████| 29/29 [00:41<00:00,  1.42s/it]\n9091 0.2858 0.0184 0.0027 : 100%|██████████| 29/29 [00:41<00:00,  1.43s/it]\nic| \"Training epoch: \": 'Training epoch: '\n    epoch_id: 11\n    epoch_size: 29\n    batches_per_epoch: 29\n    batch_size: 1\n    optim_type: 'image'\n\n  0%|          | 0/29 [00:00<?, ?it/s]\n  3%|▎         | 1/29 [00:01<00:47,  1.68s/it]\n  7%|▋         | 2/29 [00:02<00:37,  1.38s/it]\n 10%|█         | 3/29 [00:03<00:31,  1.21s/it]\n 14%|█▍        | 4/29 [00:05<00:30,  1.23s/it]\n 17%|█▋        | 5/29 [00:06<00:29,  1.24s/it]\n 21%|██        | 6/29 [00:07<00:31,  1.36s/it]\n 24%|██▍       | 7/29 [00:08<00:27,  1.24s/it]\n 28%|██▊       | 8/29 [00:09<00:24,  1.18s/it]\n 31%|███       | 9/29 [00:12<00:29,  1.47s/it]\n 34%|███▍      | 10/29 [00:13<00:27,  1.45s/it]\n 38%|███▊      | 11/29 [00:15<00:29,  1.65s/it]\n 41%|████▏     | 12/29 [00:17<00:27,  1.63s/it]\n 45%|████▍     | 13/29 [00:18<00:24,  1.54s/it]\n 48%|████▊     | 14/29 [00:19<00:22,  1.48s/it]\n 52%|█████▏    | 15/29 [00:21<00:20,  1.43s/it]\n 55%|█████▌    | 16/29 [00:22<00:17,  1.31s/it]\n 59%|█████▊    | 17/29 [00:23<00:15,  1.30s/it]\n 62%|██████▏   | 18/29 [00:24<00:14,  1.30s/it]\n 66%|██████▌   | 19/29 [00:26<00:13,  1.37s/it]\n9120 0.2437 0.0208 0.0046 :  66%|██████▌   | 19/29 [00:27<00:13,  1.37s/it]\n9120 0.2437 0.0208 0.0046 :  69%|██████▉   | 20/29 [00:27<00:11,  1.33s/it]\n9120 0.2437 0.0208 0.0046 :  72%|███████▏  | 21/29 [00:28<00:10,  1.27s/it]\n9120 0.2437 0.0208 0.0046 :  76%|███████▌  | 22/29 [00:30<00:10,  1.47s/it]\n9120 0.2437 0.0208 0.0046 :  79%|███████▉  | 23/29 [00:31<00:08,  1.43s/it]\n9120 0.2437 0.0208 0.0046 :  83%|████████▎ | 24/29 [00:33<00:06,  1.31s/it]\n9120 0.2437 0.0208 0.0046 :  86%|████████▌ | 25/29 [00:34<00:05,  1.34s/it]\n9120 0.2437 0.0208 0.0046 :  90%|████████▉ | 26/29 [00:36<00:04,  1.51s/it]\n9120 0.2437 0.0208 0.0046 :  93%|█████████▎| 27/29 [00:38<00:03,  1.69s/it]\n9120 0.2437 0.0208 0.0046 :  97%|█████████▋| 28/29 [00:40<00:01,  1.81s/it]\n9120 0.2437 0.0208 0.0046 : 100%|██████████| 29/29 [00:42<00:00,  1.84s/it]\n9120 0.2437 0.0208 0.0046 : 100%|██████████| 29/29 [00:42<00:00,  1.46s/it]\nSetting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off]\nLoading model from: /root/.pyenv/versions/3.8.8/lib/python3.8/site-packages/lpips/weights/v0.1/vgg.pth\nDetected LLFF dataset\nUsing pre-scaled images from /root/.cache/arf-svox2/data/llff/flower/images_4\nLoaded LLFF data /root/.cache/arf-svox2/data/llff/flower 22.573735200863933 222.47985705021057\nrecentered (3, 4)\n[ 1.4332432 12.709555 ]\nOverriding offset 250-> 250\ndmin = 1.433243, dmax = 12.709555, invz = 0, offset = 250\n\n  0%|          | 0/5 [00:00<?, ?it/s]\n 40%|████      | 2/5 [00:00<00:00, 16.72it/s]\n100%|██████████| 5/5 [00:00<00:00, 22.23it/s]\n100%|██████████| 5/5 [00:00<00:00, 21.36it/s]\n\n  0%|          | 0/120 [00:00<?, ?it/s]\n100%|██████████| 120/120 [00:00<00:00, 78643.20it/s]\nz_bounds from LLFF: [1.4332431554794312, 12.709554672241211] (not used)\nscene_radius [1.496031746031746, 1.6613756613756614, 1.0]\nWriting to /root/.cache/arf-svox2/ckpt_arf/llff/flower/69777919-aa83-4f8b-a42f-d13f519301fe/test_renders_path\n\n  0%|          | 0/120 [00:00<?, ?it/s]\n  1%|          | 1/120 [00:00<00:48,  2.45it/s]\n  2%|▏         | 2/120 [00:00<00:42,  2.77it/s]\n  2%|▎         | 3/120 [00:01<00:41,  2.84it/s]\n  3%|▎         | 4/120 [00:01<00:40,  2.85it/s]\n  4%|▍         | 5/120 [00:01<00:39,  2.88it/s]\n  5%|▌         | 6/120 [00:02<00:39,  2.89it/s]\n  6%|▌         | 7/120 [00:02<00:38,  2.90it/s]\n  7%|▋         | 8/120 [00:02<00:38,  2.95it/s]\n  8%|▊         | 9/120 [00:03<00:37,  2.99it/s]\n  8%|▊         | 10/120 [00:03<00:36,  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[00:35<00:02,  3.39it/s]\n 92%|█████████▎| 111/120 [00:35<00:02,  3.32it/s]\n 93%|█████████▎| 112/120 [00:36<00:02,  3.24it/s]\n 94%|█████████▍| 113/120 [00:36<00:02,  3.15it/s]\n 95%|█████████▌| 114/120 [00:36<00:01,  3.07it/s]\n 96%|█████████▌| 115/120 [00:37<00:01,  3.00it/s]\n 97%|█████████▋| 116/120 [00:37<00:01,  2.94it/s]\n 98%|█████████▊| 117/120 [00:37<00:01,  2.89it/s]\n 98%|█████████▊| 118/120 [00:38<00:00,  2.86it/s]\n 99%|█████████▉| 119/120 [00:38<00:00,  2.86it/s]\n100%|██████████| 120/120 [00:38<00:00,  2.87it/s]\n100%|██████████| 120/120 [00:38<00:00,  3.09it/s]\nIMAGEIO FFMPEG_WRITER WARNING: input image is not divisible by macro_block_size=8, resizing from (1008, 756) to (1008, 760) to ensure video compatibility with most codecs and players. To prevent resizing, make your input image divisible by the macro_block_size or set the macro_block_size to 1 (risking incompatibility).",
  "metrics": {
    "predict_time": 667.194595,
    "total_time": 1730.508113
  },
  "output": "https://replicate.delivery/mgxm/23d0d376-7c05-4900-a5b2-3afb78b7a9b8/test_renders_path.mp4",
  "started_at": "2022-06-23T03:45:25.122787Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/slugofz6f5dhrhijnrk5g4wkhi",
    "cancel": "https://api.replicate.com/v1/predictions/slugofz6f5dhrhijnrk5g4wkhi/cancel"
  },
  "version": "e169fc45cf0cc731656decd7a6da19145c764b717a7e8126eac2b834232fdf37"
}

Generated in

11 minutes 7 seconds

Tweak itReport

Downloading checkpoint for flower
Detected LLFF dataset
Using pre-scaled images from /root/.cache/arf-svox2/data/llff/flower/images_4
Loaded LLFF data /root/.cache/arf-svox2/data/llff/flower 22.573735200863933 222.47985705021057
recentered (3, 4)
[ 1.4332432 12.709555 ]
Overriding offset 250-> 250
dmin = 1.433243, dmax = 12.709555, invz = 0, offset = 250

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z_bounds from LLFF: [1.4332431554794312, 12.709554672241211] (not used)
scene_radius [1.496031746031746, 1.6613756613756614, 1.0]
 Generating rays, scaling factor 1
ic| "Loaded ckpt: ": 'Loaded ckpt: '
    args.init_ckpt: '/root/.cache/arf-svox2/ckpt_svox2/llff/flower/ckpt.npz'
ic| grid.basis_dim: 1
Render options RenderOptions(backend='cuvol', background_brightness=0.5, step_size=0.5, sigma_thresh=1e-08, stop_thresh=1e-07, last_sample_opaque=False, near_clip=0.0, use_spheric_clip=False, random_sigma_std=0.0, random_sigma_std_background=0.0)
opt_style.py:508: DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning dissapear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.
  style_img = imageio.imread(args.style).astype(np.float32) / 255.0
ic| "Style image: ": 'Style image: '
    args.style: '/tmp/tmppfk2cab220210215_110704.jpg'
    style_img.shape: torch.Size([283, 504, 3])
Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth

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ic| "Training epoch: ": 'Training epoch: '
    epoch_id: 0
    epoch_size: 22099392
    batches_per_epoch: 4420
    batch_size: 5000
    optim_type: 'ray'

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759 23.7078 :  18%|█▊        | 777/4420 [00:06<00:27, 132.09it/s]
779 18.6404 :  18%|█▊        | 777/4420 [00:06<00:27, 132.09it/s]
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899 21.2989 :  21%|██        | 919/4420 [00:08<00:27, 125.79it/s]
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1039 24.1583 :  23%|██▎       | 1036/4420 [00:09<00:32, 104.32it/s]
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1079 19.7764 :  25%|██▍       | 1085/4420 [00:09<00:29, 112.77it/s]
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1099 19.8724 :  25%|██▌       | 1109/4420 [00:09<00:29, 111.30it/s]
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1159 21.4247 :  27%|██▋       | 1176/4420 [00:10<00:30, 105.10it/s]
1179 23.9127 :  27%|██▋       | 1176/4420 [00:10<00:30, 105.10it/s]
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1199 21.9844 :  27%|██▋       | 1188/4420 [00:10<00:30, 106.73it/s]
1199 21.9844 :  27%|██▋       | 1200/4420 [00:10<00:29, 110.30it/s]
1199 21.9844 :  27%|██▋       | 1214/4420 [00:10<00:27, 117.51it/s]
1219 17.0933 :  27%|██▋       | 1214/4420 [00:10<00:27, 117.51it/s]
1219 17.0933 :  28%|██▊       | 1227/4420 [00:10<00:26, 120.80it/s]
1239 23.5547 :  28%|██▊       | 1227/4420 [00:11<00:26, 120.80it/s]
1239 23.5547 :  28%|██▊       | 1240/4420 [00:11<00:25, 123.02it/s]
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1259 22.8382 :  29%|██▊       | 1266/4420 [00:11<00:25, 124.47it/s]
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1359 27.8486 :  31%|███       | 1360/4420 [00:11<00:23, 132.70it/s]
1359 27.8486 :  31%|███       | 1376/4420 [00:12<00:22, 138.09it/s]
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1419 24.2159 :  32%|███▏      | 1434/4420 [00:12<00:21, 137.57it/s]
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1619 23.5912 :  37%|███▋      | 1621/4420 [00:13<00:18, 149.95it/s]
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1639 28.3440 :  37%|███▋      | 1637/4420 [00:13<00:18, 152.38it/s]
1639 28.3440 :  37%|███▋      | 1654/4420 [00:13<00:17, 156.57it/s]
1659 25.9747 :  37%|███▋      | 1654/4420 [00:13<00:17, 156.57it/s]
1659 25.9747 :  38%|███▊      | 1673/4420 [00:14<00:16, 165.05it/s]
1679 20.2391 :  38%|███▊      | 1673/4420 [00:14<00:16, 165.05it/s]
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1699 22.4082 :  39%|███▊      | 1707/4420 [00:14<00:17, 159.19it/s]
1719 22.6230 :  39%|███▊      | 1707/4420 [00:14<00:17, 159.19it/s]
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1819 27.5043 :  42%|████▏     | 1839/4420 [00:15<00:16, 159.75it/s]
1839 20.4783 :  42%|████▏     | 1839/4420 [00:15<00:16, 159.75it/s]
1839 20.4783 :  42%|████▏     | 1855/4420 [00:15<00:16, 151.79it/s]
1859 21.8136 :  42%|████▏     | 1855/4420 [00:15<00:16, 151.79it/s]
1859 21.8136 :  42%|████▏     | 1871/4420 [00:15<00:17, 145.12it/s]
1879 24.1135 :  42%|████▏     | 1871/4420 [00:15<00:17, 145.12it/s]
1879 24.1135 :  43%|████▎     | 1886/4420 [00:15<00:18, 139.86it/s]
1899 24.2979 :  43%|████▎     | 1886/4420 [00:15<00:18, 139.86it/s]
1899 24.2979 :  43%|████▎     | 1901/4420 [00:15<00:18, 136.76it/s]
1899 24.2979 :  43%|████▎     | 1915/4420 [00:15<00:18, 135.03it/s]
1919 21.4593 :  43%|████▎     | 1915/4420 [00:15<00:18, 135.03it/s]
1919 21.4593 :  44%|████▎     | 1929/4420 [00:15<00:18, 135.39it/s]
1939 28.1469 :  44%|████▎     | 1929/4420 [00:15<00:18, 135.39it/s]
1939 28.1469 :  44%|████▍     | 1944/4420 [00:15<00:17, 137.95it/s]
1959 26.7656 :  44%|████▍     | 1944/4420 [00:16<00:17, 137.95it/s]
1959 26.7656 :  44%|████▍     | 1960/4420 [00:16<00:17, 143.01it/s]
1959 26.7656 :  45%|████▍     | 1978/4420 [00:16<00:16, 151.01it/s]
1979 19.7699 :  45%|████▍     | 1978/4420 [00:16<00:16, 151.01it/s]
1979 19.7699 :  45%|████▌     | 1994/4420 [00:16<00:16, 143.91it/s]
1999 21.2935 :  45%|████▌     | 1994/4420 [00:16<00:16, 143.91it/s]
1999 21.2935 :  45%|████▌     | 2009/4420 [00:16<00:17, 135.42it/s]
2019 19.5594 :  45%|████▌     | 2009/4420 [00:16<00:17, 135.42it/s]
2019 19.5594 :  46%|████▌     | 2023/4420 [00:16<00:18, 129.63it/s]
2019 19.5594 :  46%|████▌     | 2037/4420 [00:16<00:19, 124.95it/s]
2039 20.9467 :  46%|████▌     | 2037/4420 [00:16<00:19, 124.95it/s]
2039 20.9467 :  46%|████▋     | 2050/4420 [00:16<00:19, 122.48it/s]
2059 24.6802 :  46%|████▋     | 2050/4420 [00:16<00:19, 122.48it/s]
2059 24.6802 :  47%|████▋     | 2063/4420 [00:16<00:19, 120.75it/s]
2059 24.6802 :  47%|████▋     | 2076/4420 [00:16<00:19, 120.88it/s]
2079 24.2470 :  47%|████▋     | 2076/4420 [00:16<00:19, 120.88it/s]
2079 24.2470 :  47%|████▋     | 2089/4420 [00:17<00:18, 123.18it/s]
2099 25.8745 :  47%|████▋     | 2089/4420 [00:17<00:18, 123.18it/s]
2099 25.8745 :  48%|████▊     | 2103/4420 [00:17<00:18, 126.08it/s]
2099 25.8745 :  48%|████▊     | 2119/4420 [00:17<00:17, 133.23it/s]
2119 26.7481 :  48%|████▊     | 2119/4420 [00:17<00:17, 133.23it/s]
2119 26.7481 :  48%|████▊     | 2135/4420 [00:17<00:16, 138.24it/s]
2139 18.9922 :  48%|████▊     | 2135/4420 [00:17<00:16, 138.24it/s]
2139 18.9922 :  49%|████▊     | 2149/4420 [00:17<00:17, 126.55it/s]
2159 19.3110 :  49%|████▊     | 2149/4420 [00:17<00:17, 126.55it/s]
2159 19.3110 :  49%|████▉     | 2162/4420 [00:17<00:18, 119.16it/s]
2159 19.3110 :  49%|████▉     | 2175/4420 [00:17<00:19, 113.32it/s]
2179 20.7273 :  49%|████▉     | 2175/4420 [00:17<00:19, 113.32it/s]
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2179 20.7273 :  50%|████▉     | 2199/4420 [00:17<00:20, 109.21it/s]
2199 22.8412 :  50%|████▉     | 2199/4420 [00:17<00:20, 109.21it/s]
2199 22.8412 :  50%|█████     | 2210/4420 [00:18<00:20, 108.99it/s]
2219 26.3214 :  50%|█████     | 2210/4420 [00:18<00:20, 108.99it/s]
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2239 23.6153 :  51%|█████     | 2233/4420 [00:18<00:19, 110.69it/s]
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2259 24.9981 :  51%|█████     | 2246/4420 [00:18<00:18, 115.27it/s]
2259 24.9981 :  51%|█████     | 2260/4420 [00:18<00:17, 121.99it/s]
2259 24.9981 :  51%|█████▏    | 2275/4420 [00:18<00:16, 129.07it/s]
2279 22.8236 :  51%|█████▏    | 2275/4420 [00:18<00:16, 129.07it/s]
2279 22.8236 :  52%|█████▏    | 2289/4420 [00:18<00:16, 131.31it/s]
2299 22.7373 :  52%|█████▏    | 2289/4420 [00:18<00:16, 131.31it/s]
2299 22.7373 :  52%|█████▏    | 2303/4420 [00:18<00:16, 128.46it/s]
2299 22.7373 :  52%|█████▏    | 2316/4420 [00:18<00:16, 126.20it/s]
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2319 24.9495 :  53%|█████▎    | 2329/4420 [00:19<00:16, 123.52it/s]
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2339 23.1895 :  53%|█████▎    | 2355/4420 [00:19<00:16, 122.34it/s]
2359 23.3066 :  53%|█████▎    | 2355/4420 [00:19<00:16, 122.34it/s]
2359 23.3066 :  54%|█████▎    | 2368/4420 [00:19<00:16, 123.76it/s]
2379 27.2013 :  54%|█████▎    | 2368/4420 [00:19<00:16, 123.76it/s]
2379 27.2013 :  54%|█████▍    | 2381/4420 [00:19<00:16, 124.36it/s]
2379 27.2013 :  54%|█████▍    | 2395/4420 [00:19<00:15, 128.34it/s]
2399 29.0276 :  54%|█████▍    | 2395/4420 [00:19<00:15, 128.34it/s]
2399 29.0276 :  55%|█████▍    | 2411/4420 [00:19<00:14, 135.28it/s]
2419 28.4289 :  55%|█████▍    | 2411/4420 [00:19<00:14, 135.28it/s]
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4019 28.7737 :  91%|█████████ | 4030/4420 [00:32<00:03, 126.91it/s]
4039 27.4116 :  91%|█████████ | 4030/4420 [00:32<00:03, 126.91it/s]
4039 27.4116 :  91%|█████████▏| 4043/4420 [00:32<00:02, 126.16it/s]
4039 27.4116 :  92%|█████████▏| 4057/4420 [00:33<00:02, 128.61it/s]
4059 31.7108 :  92%|█████████▏| 4057/4420 [00:33<00:02, 128.61it/s]
4059 31.7108 :  92%|█████████▏| 4072/4420 [00:33<00:02, 133.59it/s]
4079 33.2201 :  92%|█████████▏| 4072/4420 [00:33<00:02, 133.59it/s]
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4099 31.6397 :  92%|█████████▏| 4088/4420 [00:33<00:02, 138.60it/s]
4099 31.6397 :  93%|█████████▎| 4103/4420 [00:33<00:02, 140.74it/s]
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4119 23.2851 :  93%|█████████▎| 4118/4420 [00:33<00:02, 140.86it/s]
4119 23.2851 :  94%|█████████▎| 4133/4420 [00:33<00:02, 132.58it/s]
4139 26.3890 :  94%|█████████▎| 4133/4420 [00:33<00:02, 132.58it/s]
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4159 26.9501 :  94%|█████████▍| 4147/4420 [00:33<00:02, 126.28it/s]
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4219 31.7778 :  95%|█████████▌| 4212/4420 [00:34<00:01, 121.26it/s]
4219 31.7778 :  96%|█████████▌| 4226/4420 [00:34<00:01, 126.15it/s]
4239 31.1322 :  96%|█████████▌| 4226/4420 [00:34<00:01, 126.15it/s]
4239 31.1322 :  96%|█████████▌| 4241/4420 [00:34<00:01, 130.61it/s]
4239 31.1322 :  96%|█████████▋| 4256/4420 [00:34<00:01, 134.67it/s]
4259 29.5662 :  96%|█████████▋| 4256/4420 [00:34<00:01, 134.67it/s]
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4279 20.5298 :  97%|█████████▋| 4270/4420 [00:34<00:01, 131.82it/s]
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4359 24.4153 :  99%|█████████▊| 4358/4420 [00:35<00:00, 92.53it/s]
4359 24.4153 :  99%|█████████▉| 4369/4420 [00:35<00:00, 95.75it/s]
4379 25.8403 :  99%|█████████▉| 4369/4420 [00:35<00:00, 95.75it/s]
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4419 15.4968 : 100%|██████████| 4420/4420 [00:36<00:00, 121.72it/s]
ic| "Training epoch: ": 'Training epoch: '
    epoch_id: 1
    epoch_size: 22099392
    batches_per_epoch: 4420
    batch_size: 5000
    optim_type: 'ray'

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4999 30.2560 :  13%|█▎        | 576/4420 [00:05<00:31, 121.51it/s]
4999 30.2560 :  13%|█▎        | 589/4420 [00:05<00:31, 123.36it/s]
5019 29.8732 :  13%|█▎        | 589/4420 [00:05<00:31, 123.36it/s]
5019 29.8732 :  14%|█▎        | 604/4420 [00:05<00:29, 129.90it/s]
5019 29.8732 :  14%|█▍        | 618/4420 [00:05<00:28, 131.97it/s]
5039 25.8078 :  14%|█▍        | 618/4420 [00:05<00:28, 131.97it/s]
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5639 22.9777 :  28%|██▊       | 1217/4420 [00:11<00:27, 115.25it/s]
5639 22.9777 :  28%|██▊       | 1230/4420 [00:11<00:26, 118.88it/s]
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5719 29.6869 :  30%|██▉       | 1305/4420 [00:11<00:26, 116.07it/s]
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5739 27.2193 :  30%|██▉       | 1318/4420 [00:11<00:26, 117.55it/s]
5739 27.2193 :  30%|███       | 1331/4420 [00:11<00:25, 119.27it/s]
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5759 27.6007 :  31%|███       | 1358/4420 [00:12<00:24, 126.39it/s]
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6159 29.1776 :  39%|███▉      | 1740/4420 [00:14<00:18, 144.22it/s]
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6219 30.8417 :  41%|████      | 1801/4420 [00:15<00:17, 146.92it/s]
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6239 30.0745 :  41%|████      | 1819/4420 [00:15<00:16, 154.67it/s]
6239 30.0745 :  42%|████▏     | 1835/4420 [00:15<00:16, 156.05it/s]
6259 23.3657 :  42%|████▏     | 1835/4420 [00:15<00:16, 156.05it/s]
6259 23.3657 :  42%|████▏     | 1851/4420 [00:15<00:17, 147.70it/s]
6279 26.0346 :  42%|████▏     | 1851/4420 [00:15<00:17, 147.70it/s]
6279 26.0346 :  42%|████▏     | 1866/4420 [00:15<00:18, 141.12it/s]
6299 27.2836 :  42%|████▏     | 1866/4420 [00:15<00:18, 141.12it/s]
6299 27.2836 :  43%|████▎     | 1881/4420 [00:15<00:18, 135.68it/s]
6299 27.2836 :  43%|████▎     | 1895/4420 [00:15<00:19, 131.91it/s]
6319 27.5485 :  43%|████▎     | 1895/4420 [00:15<00:19, 131.91it/s]
6319 27.5485 :  43%|████▎     | 1909/4420 [00:16<00:19, 130.15it/s]
6339 26.6192 :  43%|████▎     | 1909/4420 [00:16<00:19, 130.15it/s]
6339 26.6192 :  44%|████▎     | 1923/4420 [00:16<00:19, 129.54it/s]
6339 26.6192 :  44%|████▍     | 1937/4420 [00:16<00:18, 131.13it/s]
6359 30.3417 :  44%|████▍     | 1937/4420 [00:16<00:18, 131.13it/s]
6359 30.3417 :  44%|████▍     | 1952/4420 [00:16<00:18, 134.10it/s]
6379 27.3501 :  44%|████▍     | 1952/4420 [00:16<00:18, 134.10it/s]
6379 27.3501 :  45%|████▍     | 1968/4420 [00:16<00:17, 141.05it/s]
6399 27.4614 :  45%|████▍     | 1968/4420 [00:16<00:17, 141.05it/s]
6399 27.4614 :  45%|████▍     | 1984/4420 [00:16<00:16, 144.41it/s]
6399 27.4614 :  45%|████▌     | 1999/4420 [00:16<00:17, 134.54it/s]
6419 26.6880 :  45%|████▌     | 1999/4420 [00:16<00:17, 134.54it/s]
6419 26.6880 :  46%|████▌     | 2013/4420 [00:16<00:18, 127.59it/s]
6439 23.1880 :  46%|████▌     | 2013/4420 [00:16<00:18, 127.59it/s]
6439 23.1880 :  46%|████▌     | 2026/4420 [00:16<00:19, 123.01it/s]
6439 23.1880 :  46%|████▌     | 2039/4420 [00:17<00:20, 118.67it/s]
6459 28.9120 :  46%|████▌     | 2039/4420 [00:17<00:20, 118.67it/s]
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6479 30.3427 :  47%|████▋     | 2063/4420 [00:17<00:20, 115.92it/s]
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ic| "Training epoch: ": 'Training epoch: '
    epoch_id: 2
    epoch_size: 29
    batches_per_epoch: 29
    batch_size: 1
    optim_type: 'image'

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  warnings.warn(
/root/.pyenv/versions/3.8.8/lib/python3.8/site-packages/torch/nn/functional.py:3657: UserWarning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and now uses scale_factor directly, instead of relying on the computed output size. If you wish to restore the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.
  warnings.warn(
/root/.pyenv/versions/3.8.8/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)
  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)

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ic| "Training epoch: ": 'Training epoch: '
    epoch_id: 3
    epoch_size: 29
    batches_per_epoch: 29
    batch_size: 1
    optim_type: 'image'

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ic| "Training epoch: ": 'Training epoch: '
    epoch_id: 4
    epoch_size: 29
    batches_per_epoch: 29
    batch_size: 1
    optim_type: 'image'

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ic| "Training epoch: ": 'Training epoch: '
    epoch_id: 5
    epoch_size: 29
    batches_per_epoch: 29
    batch_size: 1
    optim_type: 'image'

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ic| "Training epoch: ": 'Training epoch: '
    epoch_id: 6
    epoch_size: 29
    batches_per_epoch: 29
    batch_size: 1
    optim_type: 'image'

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ic| "Training epoch: ": 'Training epoch: '
    epoch_id: 7
    epoch_size: 29
    batches_per_epoch: 29
    batch_size: 1
    optim_type: 'image'

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ic| "Training epoch: ": 'Training epoch: '
    epoch_id: 8
    epoch_size: 29
    batches_per_epoch: 29
    batch_size: 1
    optim_type: 'image'

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ic| "Training epoch: ": 'Training epoch: '
    epoch_id: 9
    epoch_size: 29
    batches_per_epoch: 29
    batch_size: 1
    optim_type: 'image'

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ic| "Training epoch: ": 'Training epoch: '
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    epoch_size: 29
    batches_per_epoch: 29
    batch_size: 1
    optim_type: 'image'

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ic| "Training epoch: ": 'Training epoch: '
    epoch_id: 11
    epoch_size: 29
    batches_per_epoch: 29
    batch_size: 1
    optim_type: 'image'

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Setting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off]
Loading model from: /root/.pyenv/versions/3.8.8/lib/python3.8/site-packages/lpips/weights/v0.1/vgg.pth
Detected LLFF dataset
Using pre-scaled images from /root/.cache/arf-svox2/data/llff/flower/images_4
Loaded LLFF data /root/.cache/arf-svox2/data/llff/flower 22.573735200863933 222.47985705021057
recentered (3, 4)
[ 1.4332432 12.709555 ]
Overriding offset 250-> 250
dmin = 1.433243, dmax = 12.709555, invz = 0, offset = 250

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z_bounds from LLFF: [1.4332431554794312, 12.709554672241211] (not used)
scene_radius [1.496031746031746, 1.6613756613756614, 1.0]
Writing to /root/.cache/arf-svox2/ckpt_arf/llff/flower/69777919-aa83-4f8b-a42f-d13f519301fe/test_renders_path

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IMAGEIO FFMPEG_WRITER WARNING: input image is not divisible by macro_block_size=8, resizing from (1008, 756) to (1008, 760) to ensure video compatibility with most codecs and players. To prevent resizing, make your input image divisible by the macro_block_size or set the macro_block_size to 1 (risking incompatibility).