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nightmareai /arf-svox2:7bb81e40
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 replicateSet 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-svox2:7bb81e4046b96358001e6275e36eee3e7228fe9f1a0e1515a5f553cbbcad07e7",
{
input: {
image: "https://replicate.delivery/mgxm/065ea769-0fe4-472b-961f-19e3d5a80902/941d4f18-97f8-4bdf-a0e6-bec0acd53301.png",
scene: "room",
num_epoches: 10
}
}
);
// 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 replicateSet 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 replicateRun 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-svox2:7bb81e4046b96358001e6275e36eee3e7228fe9f1a0e1515a5f553cbbcad07e7",
input={
"image": "https://replicate.delivery/mgxm/065ea769-0fe4-472b-961f-19e3d5a80902/941d4f18-97f8-4bdf-a0e6-bec0acd53301.png",
"scene": "room",
"num_epoches": 10
}
)
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-svox2:7bb81e4046b96358001e6275e36eee3e7228fe9f1a0e1515a5f553cbbcad07e7",
"input": {
"image": "https://replicate.delivery/mgxm/065ea769-0fe4-472b-961f-19e3d5a80902/941d4f18-97f8-4bdf-a0e6-bec0acd53301.png",
"scene": "room",
"num_epoches": 10
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Add a payment method to run this model.
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Output
{
"completed_at": "2022-06-24T05:51:41.426538Z",
"created_at": "2022-06-24T05:34:26.963497Z",
"data_removed": false,
"error": null,
"id": "2wdxxvwgcnbipeebxbhfzr4te4",
"input": {
"image": "https://replicate.delivery/mgxm/065ea769-0fe4-472b-961f-19e3d5a80902/941d4f18-97f8-4bdf-a0e6-bec0acd53301.png",
"scene": "room",
"num_epoches": "10"
},
"logs": "Downloading checkpoint for room\nDetected LLFF dataset\nUsing pre-scaled images from /root/.cache/arf/data/llff/room/images_4\nLoaded LLFF data /root/.cache/arf/data/llff/room 10.706691140704915 91.66782174279389\nrecentered (3, 4)\n[1.7806027 6.0023217]\nOverriding offset 250-> 250\ndmin = 1.780603, dmax = 6.002322, invz = 0, offset = 250\n\n 0%| | 0/35 [00:00<?, ?it/s]\n 9%|▊ | 3/35 [00:00<00:01, 24.66it/s]\n 20%|██ | 7/35 [00:00<00:00, 30.02it/s]\n 31%|███▏ | 11/35 [00:00<00:00, 31.50it/s]\n 43%|████▎ | 15/35 [00:00<00:00, 31.63it/s]\n 54%|█████▍ | 19/35 [00:00<00:00, 32.74it/s]\n 66%|██████▌ | 23/35 [00:00<00:00, 33.27it/s]\n 77%|███████▋ | 27/35 [00:00<00:00, 32.79it/s]\n 89%|████████▊ | 31/35 [00:00<00:00, 33.18it/s]\n100%|██████████| 35/35 [00:01<00:00, 33.71it/s]\n100%|██████████| 35/35 [00:01<00:00, 32.56it/s]\nz_bounds from LLFF: [1.7806026935577393, 6.002321720123291] (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/ckpt_svox2/llff/room/ckpt.npz'\nic| grid.basis_dim: 1\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\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)\nic| \"Style image: \": 'Style image: '\n args.style: '/tmp/tmpb6u9aqcp941d4f18-97f8-4bdf-a0e6-bec0acd53301.png'\n style_img.shape: torch.Size([504, 420, 3])\nic| \"Training epoch: \": 'Training epoch: '\n epoch_id: 0\n epoch_size: 26671680\n batches_per_epoch: 5335\n batch_size: 5000\n optim_type: 'ray'\n\n 0%| | 0/5335 [00:00<?, ?it/s]\n 0%| | 3/5335 [00:00<03:09, 28.14it/s]\n 0%| | 6/5335 [00:00<03:15, 27.26it/s]\n 0%| | 12/5335 [00:00<02:19, 38.12it/s]\n 0%| | 18/5335 [00:00<02:02, 43.29it/s]\n19 12.6034 : 0%| | 18/5335 [00:00<02:02, 43.29it/s]\n19 12.6034 : 0%| | 24/5335 [00:00<01:53, 46.89it/s]\n19 12.6034 : 1%| | 30/5335 [00:00<01:45, 50.11it/s]\n19 12.6034 : 1%| | 37/5335 [00:00<01:38, 53.67it/s]\n39 14.0885 : 1%| | 37/5335 [00:00<01:38, 53.67it/s]\n39 14.0885 : 1%| | 45/5335 [00:00<01:29, 59.27it/s]\n39 14.0885 : 1%| | 54/5335 [00:01<01:19, 66.03it/s]\n59 17.0749 : 1%| | 54/5335 [00:01<01:19, 66.03it/s]\n59 17.0749 : 1%| | 63/5335 [00:01<01:13, 71.96it/s]\n59 17.0749 : 1%|▏ | 72/5335 [00:01<01:09, 75.99it/s]\n79 15.8040 : 1%|▏ | 72/5335 [00:01<01:09, 75.99it/s]\n79 15.8040 : 2%|▏ | 81/5335 [00:01<01:06, 78.51it/s]\n79 15.8040 : 2%|▏ | 90/5335 [00:01<01:05, 80.42it/s]\n79 15.8040 : 2%|▏ | 99/5335 [00:01<01:03, 81.86it/s]\n99 14.9307 : 2%|▏ | 99/5335 [00:01<01:03, 81.86it/s]\n99 14.9307 : 2%|▏ | 108/5335 [00:01<01:02, 83.04it/s]\n99 14.9307 : 2%|▏ | 117/5335 [00:01<01:03, 82.03it/s]\n119 13.6552 : 2%|▏ | 117/5335 [00:01<01:03, 82.03it/s]\n119 13.6552 : 2%|▏ | 126/5335 [00:01<01:06, 78.47it/s]\n119 13.6552 : 3%|▎ 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4840/5335 [00:53<00:03, 127.29it/s]\n4839 25.2131 : 91%|█████████ | 4853/5335 [00:53<00:04, 118.79it/s]\n4859 24.8576 : 91%|█████████ | 4853/5335 [00:53<00:04, 118.79it/s]\n4859 24.8576 : 91%|█████████ | 4866/5335 [00:54<00:04, 108.01it/s]\n4859 24.8576 : 91%|█████████▏| 4878/5335 [00:54<00:04, 102.07it/s]\n4879 24.9813 : 91%|█████████▏| 4878/5335 [00:54<00:04, 102.07it/s]\n4879 24.9813 : 92%|█████████▏| 4889/5335 [00:54<00:04, 93.78it/s]\n4879 24.9813 : 92%|█████████▏| 4899/5335 [00:54<00:04, 90.27it/s]\n4899 24.6033 : 92%|█████████▏| 4899/5335 [00:54<00:04, 90.27it/s]\n4899 24.6033 : 92%|█████████▏| 4909/5335 [00:54<00:04, 90.99it/s]\n4899 24.6033 : 92%|█████████▏| 4919/5335 [00:54<00:04, 93.30it/s]\n4919 24.1499 : 92%|█████████▏| 4919/5335 [00:54<00:04, 93.30it/s]\n4919 24.1499 : 92%|█████████▏| 4930/5335 [00:54<00:04, 95.75it/s]\n4939 24.9934 : 92%|█████████▏| 4930/5335 [00:54<00:04, 95.75it/s]\n4939 24.9934 : 93%|█████████▎| 4940/5335 [00:54<00:04, 96.83it/s]\n4939 24.9934 : 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27.5497 : 95%|█████████▍| 5050/5335 [00:56<00:03, 82.49it/s]\n5059 25.4145 : 95%|█████████▍| 5050/5335 [00:56<00:03, 82.49it/s]\n5059 25.4145 : 95%|█████████▍| 5062/5335 [00:56<00:02, 91.14it/s]\n5059 25.4145 : 95%|█████████▌| 5075/5335 [00:56<00:02, 100.02it/s]\n5079 26.6469 : 95%|█████████▌| 5075/5335 [00:56<00:02, 100.02it/s]\n5079 26.6469 : 95%|█████████▌| 5088/5335 [00:56<00:02, 107.68it/s]\n5099 27.8851 : 95%|█████████▌| 5088/5335 [00:56<00:02, 107.68it/s]\n5099 27.8851 : 96%|█████████▌| 5101/5335 [00:56<00:02, 113.16it/s]\n5099 27.8851 : 96%|█████████▌| 5114/5335 [00:56<00:01, 117.56it/s]\n5119 26.5010 : 96%|█████████▌| 5114/5335 [00:56<00:01, 117.56it/s]\n5119 26.5010 : 96%|█████████▌| 5127/5335 [00:56<00:01, 119.48it/s]\n5139 24.4466 : 96%|█████████▌| 5127/5335 [00:56<00:01, 119.48it/s]\n5139 24.4466 : 96%|█████████▋| 5140/5335 [00:56<00:01, 116.40it/s]\n5139 24.4466 : 97%|█████████▋| 5152/5335 [00:57<00:01, 108.78it/s]\n5159 24.9924 : 97%|█████████▋| 5152/5335 [00:57<00:01, 108.78it/s]\n5159 24.9924 : 97%|█████████▋| 5164/5335 [00:57<00:01, 99.70it/s]\n5159 24.9924 : 97%|█████████▋| 5175/5335 [00:57<00:01, 92.12it/s]\n5179 25.8780 : 97%|█████████▋| 5175/5335 [00:57<00:01, 92.12it/s]\n5179 25.8780 : 97%|█████████▋| 5185/5335 [00:57<00:01, 90.72it/s]\n5179 25.8780 : 97%|█████████▋| 5197/5335 [00:57<00:01, 97.67it/s]\n5199 27.1586 : 97%|█████████▋| 5197/5335 [00:57<00:01, 97.67it/s]\n5199 27.1586 : 98%|█████████▊| 5211/5335 [00:57<00:01, 106.94it/s]\n5219 26.3455 : 98%|█████████▊| 5211/5335 [00:57<00:01, 106.94it/s]\n5219 26.3455 : 98%|█████████▊| 5226/5335 [00:57<00:00, 117.38it/s]\n5239 27.0302 : 98%|█████████▊| 5226/5335 [00:57<00:00, 117.38it/s]\n5239 27.0302 : 98%|█████████▊| 5242/5335 [00:57<00:00, 127.24it/s]\n5239 27.0302 : 99%|█████████▊| 5258/5335 [00:57<00:00, 134.99it/s]\n5259 26.0070 : 99%|█████████▊| 5258/5335 [00:58<00:00, 134.99it/s]\n5259 26.0070 : 99%|█████████▉| 5274/5335 [00:58<00:00, 140.71it/s]\n5279 24.9669 : 99%|█████████▉| 5274/5335 [00:58<00:00, 140.71it/s]\n5279 24.9669 : 99%|█████████▉| 5289/5335 [00:58<00:00, 140.97it/s]\n5299 25.1446 : 99%|█████████▉| 5289/5335 [00:58<00:00, 140.97it/s]\n5299 25.1446 : 99%|█████████▉| 5304/5335 [00:58<00:00, 135.41it/s]\n5299 25.1446 : 100%|█████████▉| 5318/5335 [00:58<00:00, 124.45it/s]\n5319 24.7313 : 100%|█████████▉| 5318/5335 [00:58<00:00, 124.45it/s]\n5319 24.7313 : 100%|█████████▉| 5331/5335 [00:58<00:00, 115.46it/s]\n5319 24.7313 : 100%|██████████| 5335/5335 [00:58<00:00, 91.00it/s]\nic| \"Training epoch: \": 'Training epoch: '\n epoch_id: 1\n epoch_size: 26671680\n batches_per_epoch: 5335\n batch_size: 5000\n optim_type: 'ray'\n\n 0%| | 0/5335 [00:00<?, ?it/s]\n 0%| | 3/5335 [00:00<03:02, 29.23it/s]\n 0%| | 6/5335 [00:00<03:13, 27.49it/s]\n 0%| | 11/5335 [00:00<02:30, 35.43it/s]\n 0%| | 16/5335 [00:00<02:11, 40.50it/s]\n5354 19.5023 : 0%| | 16/5335 [00:00<02:11, 40.50it/s]\n5354 19.5023 : 0%| | 22/5335 [00:00<01:58, 44.87it/s]\n5354 19.5023 : 1%| | 28/5335 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133/5335 [00:02<01:14, 69.71it/s]\n5474 21.1332 : 2%|▏ | 133/5335 [00:02<01:14, 69.71it/s]\n5474 21.1332 : 3%|▎ | 141/5335 [00:02<01:19, 65.04it/s]\n5474 21.1332 : 3%|▎ | 148/5335 [00:02<01:24, 61.62it/s]\n5474 21.1332 : 3%|▎ | 155/5335 [00:02<01:24, 61.60it/s]\n5494 22.5178 : 3%|▎ | 155/5335 [00:02<01:24, 61.60it/s]\n5494 22.5178 : 3%|▎ | 162/5335 [00:02<01:23, 62.08it/s]\n5494 22.5178 : 3%|▎ | 169/5335 [00:02<01:23, 62.03it/s]\n5494 22.5178 : 3%|▎ | 176/5335 [00:02<01:21, 62.99it/s]\n5514 25.1444 : 3%|▎ | 176/5335 [00:02<01:21, 62.99it/s]\n5514 25.1444 : 3%|▎ | 183/5335 [00:02<01:19, 64.56it/s]\n5514 25.1444 : 4%|▎ | 191/5335 [00:03<01:16, 67.40it/s]\n5534 22.9705 : 4%|▎ | 191/5335 [00:03<01:16, 67.40it/s]\n5534 22.9705 : 4%|▎ | 200/5335 [00:03<01:10, 72.66it/s]\n5534 22.9705 : 4%|▍ | 210/5335 [00:03<01:04, 79.93it/s]\n5554 24.5430 : 4%|▍ | 210/5335 [00:03<01:04, 79.93it/s]\n5554 24.5430 : 4%|▍ | 221/5335 [00:03<00:59, 86.13it/s]\n5554 24.5430 : 4%|▍ | 232/5335 [00:03<00:56, 90.42it/s]\n5574 26.6955 : 4%|▍ | 232/5335 [00:03<00:56, 90.42it/s]\n5574 26.6955 : 5%|▍ | 242/5335 [00:03<00:55, 91.99it/s]\n5574 26.6955 : 5%|▍ | 252/5335 [00:03<00:54, 93.86it/s]\n5594 25.3195 : 5%|▍ | 252/5335 [00:03<00:54, 93.86it/s]\n5594 25.3195 : 5%|▍ | 263/5335 [00:03<00:52, 96.03it/s]\n5594 25.3195 : 5%|▌ | 273/5335 [00:03<00:53, 94.43it/s]\n5614 24.9697 : 5%|▌ | 273/5335 [00:03<00:53, 94.43it/s]\n5614 24.9697 : 5%|▌ | 283/5335 [00:04<00:57, 88.23it/s]\n5614 24.9697 : 5%|▌ | 292/5335 [00:04<01:01, 82.39it/s]\n5634 24.3198 : 5%|▌ | 292/5335 [00:04<01:01, 82.39it/s]\n5634 24.3198 : 6%|▌ | 301/5335 [00:04<01:05, 77.40it/s]\n5634 24.3198 : 6%|▌ | 309/5335 [00:04<01:08, 73.43it/s]\n5634 24.3198 : 6%|▌ | 317/5335 [00:04<01:14, 67.22it/s]\n5654 24.8458 : 6%|▌ | 317/5335 [00:04<01:14, 67.22it/s]\n5654 24.8458 : 6%|▌ | 324/5335 [00:04<01:17, 64.34it/s]\n5654 24.8458 : 6%|▌ | 331/5335 [00:04<01:18, 63.40it/s]\n5654 24.8458 : 6%|▋ | 338/5335 [00:04<01:18, 63.45it/s]\n5674 24.1731 : 6%|▋ | 338/5335 [00:04<01:18, 63.45it/s]\n5674 24.1731 : 6%|▋ | 346/5335 [00:05<01:15, 66.37it/s]\n5674 24.1731 : 7%|▋ | 355/5335 [00:05<01:09, 71.52it/s]\n5694 26.9016 : 7%|▋ | 355/5335 [00:05<01:09, 71.52it/s]\n5694 26.9016 : 7%|▋ | 365/5335 [00:05<01:03, 78.23it/s]\n5694 26.9016 : 7%|▋ | 375/5335 [00:05<00:59, 83.66it/s]\n5714 23.6531 : 7%|▋ | 375/5335 [00:05<00:59, 83.66it/s]\n5714 23.6531 : 7%|▋ | 385/5335 [00:05<00:57, 86.83it/s]\n5714 23.6531 : 7%|▋ | 395/5335 [00:05<00:55, 88.86it/s]\n5734 25.7088 : 7%|▋ | 395/5335 [00:05<00:55, 88.86it/s]\n5734 25.7088 : 8%|▊ | 405/5335 [00:05<00:55, 89.61it/s]\n5734 25.7088 : 8%|▊ | 415/5335 [00:05<00:54, 90.66it/s]\n5754 24.9477 : 8%|▊ | 415/5335 [00:05<00:54, 90.66it/s]\n5754 24.9477 : 8%|▊ | 425/5335 [00:05<00:55, 88.73it/s]\n5754 24.9477 : 8%|▊ | 434/5335 [00:05<00:58, 83.47it/s]\n5774 24.3233 : 8%|▊ | 434/5335 [00:06<00:58, 83.47it/s]\n5774 24.3233 : 8%|▊ | 443/5335 [00:06<01:02, 77.96it/s]\n5774 24.3233 : 8%|▊ | 451/5335 [00:06<01:06, 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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%|▎ | 1/35 [00:01<01:03, 1.86s/it]\n 6%|▌ | 2/35 [00:03<01:01, 1.87s/it]\n 9%|▊ | 3/35 [00:05<01:03, 2.00s/it]\n 11%|█▏ | 4/35 [00:08<01:04, 2.09s/it]\n 14%|█▍ | 5/35 [00:09<00:58, 1.94s/it]\n 17%|█▋ | 6/35 [00:12<00:59, 2.06s/it]\n 20%|██ | 7/35 [00:13<00:49, 1.78s/it]\n 23%|██▎ | 8/35 [00:15<00:54, 2.03s/it]\n 26%|██▌ | 9/35 [00:17<00:52, 2.03s/it]\n 29%|██▊ | 10/35 [00:19<00:47, 1.88s/it]\n 31%|███▏ | 11/35 [00:21<00:50, 2.09s/it]\n 34%|███▍ | 12/35 [00:24<00:49, 2.15s/it]\n 37%|███▋ | 13/35 [00:25<00:43, 1.97s/it]\n 40%|████ | 14/35 [00:28<00:47, 2.28s/it]\n 43%|████▎ | 15/35 [00:31<00:50, 2.50s/it]\n 46%|████▌ | 16/35 [00:33<00:43, 2.31s/it]\n 49%|████▊ | 17/35 [00:36<00:45, 2.52s/it]\n 51%|█████▏ | 18/35 [00:39<00:41, 2.46s/it]\n 54%|█████▍ | 19/35 [00:41<00:39, 2.47s/it]\n10689 0.3898 0.0124 0.0010 : 54%|█████▍ | 19/35 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2.31s/it]\n10689 0.3898 0.0124 0.0010 : 94%|█████████▍| 33/35 [01:11<00:04, 2.07s/it]\n10689 0.3898 0.0124 0.0010 : 97%|█████████▋| 34/35 [01:13<00:01, 1.81s/it]\n10689 0.3898 0.0124 0.0010 : 100%|██████████| 35/35 [01:14<00:00, 1.83s/it]\n10689 0.3898 0.0124 0.0010 : 100%|██████████| 35/35 [01:14<00:00, 2.14s/it]\nic| \"Training epoch: \": 'Training epoch: '\n epoch_id: 3\n epoch_size: 35\n batches_per_epoch: 35\n batch_size: 1\n optim_type: 'image'\n\n 0%| | 0/35 [00:00<?, ?it/s]\n 3%|▎ | 1/35 [00:02<01:39, 2.94s/it]\n 6%|▌ | 2/35 [00:04<01:12, 2.19s/it]\n 9%|▊ | 3/35 [00:07<01:17, 2.42s/it]\n 11%|█▏ | 4/35 [00:09<01:15, 2.44s/it]\n 14%|█▍ | 5/35 [00:12<01:14, 2.48s/it]\n 17%|█▋ | 6/35 [00:14<01:10, 2.42s/it]\n 20%|██ | 7/35 [00:16<00:59, 2.14s/it]\n 23%|██▎ | 8/35 [00:18<01:00, 2.23s/it]\n 26%|██▌ | 9/35 [00:21<01:04, 2.47s/it]\n 29%|██▊ | 10/35 [00:24<01:04, 2.58s/it]\n 31%|███▏ | 11/35 [00:25<00:52, 2.20s/it]\n 34%|███▍ | 12/35 [00:27<00:47, 2.06s/it]\n 37%|███▋ | 13/35 [00:28<00:39, 1.80s/it]\n 40%|████ | 14/35 [00:30<00:36, 1.72s/it]\n 43%|████▎ | 15/35 [00:33<00:42, 2.10s/it]\n 46%|████▌ | 16/35 [00:35<00:38, 2.03s/it]\n 49%|████▊ | 17/35 [00:35<00:30, 1.69s/it]\n 51%|█████▏ | 18/35 [00:37<00:28, 1.65s/it]\n 54%|█████▍ | 19/35 [00:39<00:27, 1.73s/it]\n10724 0.3268 0.0164 0.0015 : 54%|█████▍ | 19/35 [00:41<00:27, 1.73s/it]\n10724 0.3268 0.0164 0.0015 : 57%|█████▋ | 20/35 [00:41<00:26, 1.79s/it]\n10724 0.3268 0.0164 0.0015 : 60%|██████ | 21/35 [00:43<00:26, 1.93s/it]\n10724 0.3268 0.0164 0.0015 : 63%|██████▎ | 22/35 [00:45<00:23, 1.82s/it]\n10724 0.3268 0.0164 0.0015 : 66%|██████▌ | 23/35 [00:47<00:23, 1.94s/it]\n10724 0.3268 0.0164 0.0015 : 69%|██████▊ | 24/35 [00:49<00:22, 2.04s/it]\n10724 0.3268 0.0164 0.0015 : 71%|███████▏ | 25/35 [00:51<00:19, 1.99s/it]\n10724 0.3268 0.0164 0.0015 : 74%|███████▍ | 26/35 [00:53<00:17, 1.97s/it]\n10724 0.3268 0.0164 0.0015 : 77%|███████▋ | 27/35 [00:56<00:17, 2.23s/it]\n10724 0.3268 0.0164 0.0015 : 80%|████████ | 28/35 [00:58<00:14, 2.12s/it]\n10724 0.3268 0.0164 0.0015 : 83%|████████▎ | 29/35 [01:00<00:13, 2.29s/it]\n10724 0.3268 0.0164 0.0015 : 86%|████████▌ | 30/35 [01:03<00:11, 2.28s/it]\n10724 0.3268 0.0164 0.0015 : 89%|████████▊ | 31/35 [01:04<00:08, 2.09s/it]\n10724 0.3268 0.0164 0.0015 : 91%|█████████▏| 32/35 [01:07<00:06, 2.14s/it]\n10724 0.3268 0.0164 0.0015 : 94%|█████████▍| 33/35 [01:09<00:04, 2.25s/it]\n10724 0.3268 0.0164 0.0015 : 97%|█████████▋| 34/35 [01:12<00:02, 2.47s/it]\n10724 0.3268 0.0164 0.0015 : 100%|██████████| 35/35 [01:14<00:00, 2.19s/it]\n10724 0.3268 0.0164 0.0015 : 100%|██████████| 35/35 [01:14<00:00, 2.12s/it]\nic| \"Training epoch: \": 'Training epoch: '\n epoch_id: 4\n epoch_size: 35\n batches_per_epoch: 35\n batch_size: 1\n optim_type: 'image'\n\n 0%| | 0/35 [00:00<?, ?it/s]\n 3%|▎ | 1/35 [00:02<01:35, 2.82s/it]\n 6%|▌ | 2/35 [00:03<00:55, 1.68s/it]\n 9%|▊ | 3/35 [00:06<01:11, 2.22s/it]\n 11%|█▏ | 4/35 [00:08<01:08, 2.22s/it]\n 14%|█▍ | 5/35 [00:11<01:06, 2.23s/it]\n 17%|█▋ | 6/35 [00:11<00:51, 1.77s/it]\n 20%|██ | 7/35 [00:13<00:47, 1.69s/it]\n 23%|██▎ | 8/35 [00:15<00:51, 1.93s/it]\n 26%|██▌ | 9/35 [00:18<00:54, 2.10s/it]\n 29%|██▊ | 10/35 [00:20<00:53, 2.12s/it]\n 31%|███▏ | 11/35 [00:23<00:57, 2.39s/it]\n 34%|███▍ | 12/35 [00:26<00:55, 2.42s/it]\n 37%|███▋ | 13/35 [00:28<00:56, 2.59s/it]\n 40%|████ | 14/35 [00:30<00:48, 2.31s/it]\n 43%|████▎ | 15/35 [00:33<00:49, 2.48s/it]\n 46%|████▌ | 16/35 [00:35<00:45, 2.37s/it]\n 49%|████▊ | 17/35 [00:36<00:36, 2.02s/it]\n 51%|█████▏ | 18/35 [00:39<00:35, 2.09s/it]\n 54%|█████▍ | 19/35 [00:41<00:37, 2.32s/it]\n10759 0.3328 0.0160 0.0012 : 54%|█████▍ | 19/35 [00:44<00:37, 2.32s/it]\n10759 0.3328 0.0160 0.0012 : 57%|█████▋ | 20/35 [00:44<00:36, 2.43s/it]\n10759 0.3328 0.0160 0.0012 : 60%|██████ | 21/35 [00:47<00:35, 2.56s/it]\n10759 0.3328 0.0160 0.0012 : 63%|██████▎ | 22/35 [00:48<00:26, 2.06s/it]\n10759 0.3328 0.0160 0.0012 : 66%|██████▌ | 23/35 [00:50<00:24, 2.06s/it]\n10759 0.3328 0.0160 0.0012 : 69%|██████▊ | 24/35 [00:52<00:21, 1.96s/it]\n10759 0.3328 0.0160 0.0012 : 71%|███████▏ | 25/35 [00:54<00:19, 1.94s/it]\n10759 0.3328 0.0160 0.0012 : 74%|███████▍ | 26/35 [00:56<00:18, 2.05s/it]\n10759 0.3328 0.0160 0.0012 : 77%|███████▋ | 27/35 [00:57<00:13, 1.71s/it]\n10759 0.3328 0.0160 0.0012 : 80%|████████ | 28/35 [01:00<00:14, 2.10s/it]\n10759 0.3328 0.0160 0.0012 : 83%|████████▎ | 29/35 [01:02<00:12, 2.03s/it]\n10759 0.3328 0.0160 0.0012 : 86%|████████▌ | 30/35 [01:04<00:10, 2.04s/it]\n10759 0.3328 0.0160 0.0012 : 89%|████████▊ | 31/35 [01:06<00:07, 1.99s/it]\n10759 0.3328 0.0160 0.0012 : 91%|█████████▏| 32/35 [01:09<00:06, 2.29s/it]\n10759 0.3328 0.0160 0.0012 : 94%|█████████▍| 33/35 [01:11<00:04, 2.41s/it]\n10759 0.3328 0.0160 0.0012 : 97%|█████████▋| 34/35 [01:14<00:02, 2.42s/it]\n10759 0.3328 0.0160 0.0012 : 100%|██████████| 35/35 [01:15<00:00, 2.14s/it]\n10759 0.3328 0.0160 0.0012 : 100%|██████████| 35/35 [01:15<00:00, 2.16s/it]\nic| \"Training epoch: \": 'Training epoch: '\n epoch_id: 5\n epoch_size: 35\n batches_per_epoch: 35\n batch_size: 1\n optim_type: 'image'\n\n 0%| | 0/35 [00:00<?, ?it/s]\n 3%|▎ | 1/35 [00:02<01:15, 2.21s/it]\n 6%|▌ | 2/35 [00:04<01:19, 2.41s/it]\n 9%|▊ | 3/35 [00:07<01:14, 2.34s/it]\n 11%|█▏ | 4/35 [00:08<01:04, 2.08s/it]\n 14%|█▍ | 5/35 [00:09<00:49, 1.66s/it]\n 17%|█▋ | 6/35 [00:11<00:48, 1.66s/it]\n 20%|██ | 7/35 [00:13<00:48, 1.73s/it]\n 23%|██▎ | 8/35 [00:14<00:43, 1.60s/it]\n 26%|██▌ | 9/35 [00:15<00:38, 1.47s/it]\n 29%|██▊ | 10/35 [00:17<00:37, 1.49s/it]\n 31%|███▏ | 11/35 [00:19<00:43, 1.80s/it]\n 34%|███▍ | 12/35 [00:22<00:49, 2.16s/it]\n 37%|███▋ | 13/35 [00:23<00:41, 1.87s/it]\n 40%|████ | 14/35 [00:27<00:47, 2.28s/it]\n 43%|████▎ | 15/35 [00:27<00:37, 1.86s/it]\n 46%|████▌ | 16/35 [00:30<00:38, 2.03s/it]\n 49%|████▊ | 17/35 [00:33<00:41, 2.32s/it]\n 51%|█████▏ | 18/35 [00:35<00:36, 2.12s/it]\n 54%|█████▍ | 19/35 [00:37<00:33, 2.10s/it]\n10794 0.3024 0.0190 0.0018 : 54%|█████▍ | 19/35 [00:38<00:33, 2.10s/it]\n10794 0.3024 0.0190 0.0018 : 57%|█████▋ | 20/35 [00:38<00:28, 1.93s/it]\n10794 0.3024 0.0190 0.0018 : 60%|██████ | 21/35 [00:40<00:26, 1.91s/it]\n10794 0.3024 0.0190 0.0018 : 63%|██████▎ | 22/35 [00:43<00:28, 2.23s/it]\n10794 0.3024 0.0190 0.0018 : 66%|██████▌ | 23/35 [00:44<00:22, 1.83s/it]\n10794 0.3024 0.0190 0.0018 : 69%|██████▊ | 24/35 [00:45<00:18, 1.68s/it]\n10794 0.3024 0.0190 0.0018 : 71%|███████▏ | 25/35 [00:46<00:14, 1.45s/it]\n10794 0.3024 0.0190 0.0018 : 74%|███████▍ | 26/35 [00:48<00:13, 1.49s/it]\n10794 0.3024 0.0190 0.0018 : 77%|███████▋ | 27/35 [00:49<00:11, 1.50s/it]\n10794 0.3024 0.0190 0.0018 : 80%|████████ | 28/35 [00:52<00:13, 1.94s/it]\n10794 0.3024 0.0190 0.0018 : 83%|████████▎ | 29/35 [00:55<00:13, 2.26s/it]\n10794 0.3024 0.0190 0.0018 : 86%|████████▌ | 30/35 [00:57<00:10, 2.14s/it]\n10794 0.3024 0.0190 0.0018 : 89%|████████▊ | 31/35 [00:58<00:07, 1.86s/it]\n10794 0.3024 0.0190 0.0018 : 91%|█████████▏| 32/35 [01:01<00:06, 2.06s/it]\n10794 0.3024 0.0190 0.0018 : 94%|█████████▍| 33/35 [01:02<00:03, 1.71s/it]\n10794 0.3024 0.0190 0.0018 : 97%|█████████▋| 34/35 [01:03<00:01, 1.56s/it]\n10794 0.3024 0.0190 0.0018 : 100%|██████████| 35/35 [01:06<00:00, 1.99s/it]\n10794 0.3024 0.0190 0.0018 : 100%|██████████| 35/35 [01:06<00:00, 1.90s/it]\nic| \"Training epoch: \": 'Training epoch: '\n epoch_id: 6\n epoch_size: 35\n batches_per_epoch: 35\n batch_size: 1\n optim_type: 'image'\n\n 0%| | 0/35 [00:00<?, ?it/s]\n 3%|▎ | 1/35 [00:02<01:09, 2.03s/it]\n 6%|▌ | 2/35 [00:05<01:30, 2.73s/it]\n 9%|▊ | 3/35 [00:07<01:21, 2.53s/it]\n 11%|█▏ | 4/35 [00:09<01:14, 2.41s/it]\n 14%|█▍ | 5/35 [00:10<00:55, 1.86s/it]\n 17%|█▋ | 6/35 [00:12<00:50, 1.74s/it]\n 20%|██ | 7/35 [00:15<01:00, 2.15s/it]\n 23%|██▎ | 8/35 [00:17<01:02, 2.32s/it]\n 26%|██▌ | 9/35 [00:19<00:55, 2.12s/it]\n 29%|██▊ | 10/35 [00:20<00:43, 1.75s/it]\n 31%|███▏ | 11/35 [00:22<00:44, 1.87s/it]\n 34%|███▍ | 12/35 [00:23<00:38, 1.67s/it]\n 37%|███▋ | 13/35 [00:25<00:38, 1.73s/it]\n 40%|████ | 14/35 [00:27<00:39, 1.89s/it]\n 43%|████▎ | 15/35 [00:29<00:37, 1.90s/it]\n 46%|████▌ | 16/35 [00:32<00:41, 2.19s/it]\n 49%|████▊ | 17/35 [00:34<00:36, 2.03s/it]\n 51%|█████▏ | 18/35 [00:35<00:30, 1.78s/it]\n 54%|█████▍ | 19/35 [00:38<00:32, 2.01s/it]\n10829 0.2987 0.0185 0.0013 : 54%|█████▍ | 19/35 [00:40<00:32, 2.01s/it]\n10829 0.2987 0.0185 0.0013 : 57%|█████▋ | 20/35 [00:40<00:33, 2.21s/it]\n10829 0.2987 0.0185 0.0013 : 60%|██████ | 21/35 [00:43<00:32, 2.35s/it]\n10829 0.2987 0.0185 0.0013 : 63%|██████▎ | 22/35 [00:45<00:27, 2.11s/it]\n10829 0.2987 0.0185 0.0013 : 66%|██████▌ | 23/35 [00:47<00:27, 2.28s/it]\n10829 0.2987 0.0185 0.0013 : 69%|██████▊ | 24/35 [00:49<00:22, 2.06s/it]\n10829 0.2987 0.0185 0.0013 : 71%|███████▏ | 25/35 [00:51<00:21, 2.12s/it]\n10829 0.2987 0.0185 0.0013 : 74%|███████▍ | 26/35 [00:53<00:18, 2.00s/it]\n10829 0.2987 0.0185 0.0013 : 77%|███████▋ | 27/35 [00:55<00:17, 2.20s/it]\n10829 0.2987 0.0185 0.0013 : 80%|████████ | 28/35 [00:57<00:14, 2.10s/it]\n10829 0.2987 0.0185 0.0013 : 83%|████████▎ | 29/35 [00:59<00:12, 2.05s/it]\n10829 0.2987 0.0185 0.0013 : 86%|████████▌ | 30/35 [01:01<00:09, 1.90s/it]\n10829 0.2987 0.0185 0.0013 : 89%|████████▊ | 31/35 [01:03<00:08, 2.14s/it]\n10829 0.2987 0.0185 0.0013 : 91%|█████████▏| 32/35 [01:05<00:06, 2.06s/it]\n10829 0.2987 0.0185 0.0013 : 94%|█████████▍| 33/35 [01:07<00:03, 1.80s/it]\n10829 0.2987 0.0185 0.0013 : 97%|█████████▋| 34/35 [01:07<00:01, 1.52s/it]\n10829 0.2987 0.0185 0.0013 : 100%|██████████| 35/35 [01:10<00:00, 1.75s/it]\n10829 0.2987 0.0185 0.0013 : 100%|██████████| 35/35 [01:10<00:00, 2.01s/it]\nic| \"Training epoch: \": 'Training epoch: '\n epoch_id: 7\n epoch_size: 35\n batches_per_epoch: 35\n batch_size: 1\n optim_type: 'image'\n\n 0%| | 0/35 [00:00<?, ?it/s]\n 3%|▎ | 1/35 [00:01<00:51, 1.51s/it]\n 6%|▌ | 2/35 [00:02<00:43, 1.33s/it]\n 9%|▊ | 3/35 [00:03<00:36, 1.14s/it]\n 11%|█▏ | 4/35 [00:06<00:56, 1.81s/it]\n 14%|█▍ | 5/35 [00:09<01:07, 2.23s/it]\n 17%|█▋ | 6/35 [00:10<00:57, 1.98s/it]\n 20%|██ | 7/35 [00:12<00:48, 1.73s/it]\n 23%|██▎ | 8/35 [00:14<00:47, 1.77s/it]\n 26%|██▌ | 9/35 [00:15<00:44, 1.69s/it]\n 29%|██▊ | 10/35 [00:17<00:41, 1.64s/it]\n 31%|███▏ | 11/35 [00:18<00:37, 1.55s/it]\n 34%|███▍ | 12/35 [00:21<00:44, 1.95s/it]\n 37%|███▋ | 13/35 [00:22<00:40, 1.86s/it]\n 40%|████ | 14/35 [00:24<00:37, 1.80s/it]\n 43%|████▎ | 15/35 [00:26<00:34, 1.73s/it]\n 46%|████▌ | 16/35 [00:29<00:39, 2.10s/it]\n 49%|████▊ | 17/35 [00:30<00:32, 1.83s/it]\n 51%|█████▏ | 18/35 [00:31<00:29, 1.74s/it]\n 54%|█████▍ | 19/35 [00:35<00:34, 2.19s/it]\n10864 0.2865 0.0207 0.0017 : 54%|█████▍ | 19/35 [00:36<00:34, 2.19s/it]\n10864 0.2865 0.0207 0.0017 : 57%|█████▋ | 20/35 [00:36<00:31, 2.09s/it]\n10864 0.2865 0.0207 0.0017 : 60%|██████ | 21/35 [00:38<00:28, 2.03s/it]\n10864 0.2865 0.0207 0.0017 : 63%|██████▎ | 22/35 [00:40<00:23, 1.78s/it]\n10864 0.2865 0.0207 0.0017 : 66%|██████▌ | 23/35 [00:42<00:25, 2.10s/it]\n10864 0.2865 0.0207 0.0017 : 69%|██████▊ | 24/35 [00:45<00:25, 2.28s/it]\n10864 0.2865 0.0207 0.0017 : 71%|███████▏ | 25/35 [00:46<00:18, 1.87s/it]\n10864 0.2865 0.0207 0.0017 : 74%|███████▍ | 26/35 [00:48<00:17, 1.95s/it]\n10864 0.2865 0.0207 0.0017 : 77%|███████▋ | 27/35 [00:50<00:15, 1.88s/it]\n10864 0.2865 0.0207 0.0017 : 80%|████████ | 28/35 [00:51<00:12, 1.82s/it]\n10864 0.2865 0.0207 0.0017 : 83%|████████▎ | 29/35 [00:53<00:10, 1.83s/it]\n10864 0.2865 0.0207 0.0017 : 86%|████████▌ | 30/35 [00:55<00:09, 1.90s/it]\n10864 0.2865 0.0207 0.0017 : 89%|████████▊ | 31/35 [00:57<00:07, 1.78s/it]\n10864 0.2865 0.0207 0.0017 : 91%|█████████▏| 32/35 [00:58<00:04, 1.65s/it]\n10864 0.2865 0.0207 0.0017 : 94%|█████████▍| 33/35 [01:01<00:04, 2.01s/it]\n10864 0.2865 0.0207 0.0017 : 97%|█████████▋| 34/35 [01:02<00:01, 1.77s/it]\n10864 0.2865 0.0207 0.0017 : 100%|██████████| 35/35 [01:05<00:00, 1.93s/it]\n10864 0.2865 0.0207 0.0017 : 100%|██████████| 35/35 [01:05<00:00, 1.86s/it]\nic| \"Training epoch: \": 'Training epoch: '\n epoch_id: 8\n epoch_size: 35\n batches_per_epoch: 35\n batch_size: 1\n optim_type: 'image'\n\n 0%| | 0/35 [00:00<?, ?it/s]\n 3%|▎ | 1/35 [00:02<01:17, 2.28s/it]\n 6%|▌ | 2/35 [00:04<01:07, 2.04s/it]\n 9%|▊ | 3/35 [00:07<01:18, 2.46s/it]\n 11%|█▏ | 4/35 [00:10<01:22, 2.68s/it]\n 14%|█▍ | 5/35 [00:12<01:20, 2.68s/it]\n 17%|█▋ | 6/35 [00:14<01:06, 2.29s/it]\n 20%|██ | 7/35 [00:16<01:00, 2.17s/it]\n 23%|██▎ | 8/35 [00:18<00:57, 2.12s/it]\n 26%|██▌ | 9/35 [00:19<00:44, 1.73s/it]\n 29%|██▊ | 10/35 [00:20<00:42, 1.69s/it]\n 31%|███▏ | 11/35 [00:22<00:41, 1.74s/it]\n 34%|███▍ | 12/35 [00:25<00:45, 1.99s/it]\n 37%|███▋ | 13/35 [00:28<00:50, 2.29s/it]\n 40%|████ | 14/35 [00:29<00:45, 2.16s/it]\n 43%|████▎ | 15/35 [00:32<00:47, 2.37s/it]\n 46%|████▌ | 16/35 [00:35<00:48, 2.55s/it]\n 49%|████▊ | 17/35 [00:37<00:41, 2.30s/it]\n 51%|█████▏ | 18/35 [00:40<00:41, 2.42s/it]\n 54%|█████▍ | 19/35 [00:42<00:38, 2.38s/it]\n10899 0.2900 0.0202 0.0019 : 54%|█████▍ | 19/35 [00:44<00:38, 2.38s/it]\n10899 0.2900 0.0202 0.0019 : 57%|█████▋ | 20/35 [00:44<00:31, 2.13s/it]\n10899 0.2900 0.0202 0.0019 : 60%|██████ | 21/35 [00:47<00:33, 2.39s/it]\n10899 0.2900 0.0202 0.0019 : 63%|██████▎ | 22/35 [00:48<00:26, 2.03s/it]\n10899 0.2900 0.0202 0.0019 : 66%|██████▌ | 23/35 [00:51<00:27, 2.28s/it]\n10899 0.2900 0.0202 0.0019 : 69%|██████▊ | 24/35 [00:52<00:21, 1.99s/it]\n10899 0.2900 0.0202 0.0019 : 71%|███████▏ | 25/35 [00:55<00:22, 2.20s/it]\n10899 0.2900 0.0202 0.0019 : 74%|███████▍ | 26/35 [00:57<00:18, 2.11s/it]\n10899 0.2900 0.0202 0.0019 : 77%|███████▋ | 27/35 [01:00<00:19, 2.44s/it]\n10899 0.2900 0.0202 0.0019 : 80%|████████ | 28/35 [01:01<00:15, 2.23s/it]\n10899 0.2900 0.0202 0.0019 : 83%|████████▎ | 29/35 [01:03<00:12, 2.08s/it]\n10899 0.2900 0.0202 0.0019 : 86%|████████▌ | 30/35 [01:05<00:10, 2.01s/it]\n10899 0.2900 0.0202 0.0019 : 89%|████████▊ | 31/35 [01:08<00:09, 2.27s/it]\n10899 0.2900 0.0202 0.0019 : 91%|█████████▏| 32/35 [01:11<00:07, 2.48s/it]\n10899 0.2900 0.0202 0.0019 : 94%|█████████▍| 33/35 [01:13<00:04, 2.40s/it]\n10899 0.2900 0.0202 0.0019 : 97%|█████████▋| 34/35 [01:16<00:02, 2.49s/it]\n10899 0.2900 0.0202 0.0019 : 100%|██████████| 35/35 [01:18<00:00, 2.31s/it]\n10899 0.2900 0.0202 0.0019 : 100%|██████████| 35/35 [01:18<00:00, 2.23s/it]\nic| \"Training epoch: \": 'Training epoch: '\n epoch_id: 9\n epoch_size: 35\n batches_per_epoch: 35\n batch_size: 1\n optim_type: 'image'\n\n 0%| | 0/35 [00:00<?, ?it/s]\n 3%|▎ | 1/35 [00:01<00:44, 1.31s/it]\n 6%|▌ | 2/35 [00:04<01:15, 2.30s/it]\n 9%|▊ | 3/35 [00:06<01:16, 2.39s/it]\n 11%|█▏ | 4/35 [00:08<01:03, 2.06s/it]\n 14%|█▍ | 5/35 [00:10<01:03, 2.12s/it]\n 17%|█▋ | 6/35 [00:11<00:52, 1.80s/it]\n 20%|██ | 7/35 [00:13<00:51, 1.83s/it]\n 23%|██▎ | 8/35 [00:16<00:58, 2.16s/it]\n 26%|██▌ | 9/35 [00:17<00:48, 1.86s/it]\n 29%|██▊ | 10/35 [00:20<00:50, 2.03s/it]\n 31%|███▏ | 11/35 [00:22<00:49, 2.07s/it]\n 34%|███▍ | 12/35 [00:23<00:41, 1.80s/it]\n 37%|███▋ | 13/35 [00:25<00:37, 1.72s/it]\n 40%|████ | 14/35 [00:25<00:31, 1.48s/it]\n 43%|████▎ | 15/35 [00:28<00:33, 1.68s/it]\n 46%|████▌ | 16/35 [00:29<00:33, 1.74s/it]\n 49%|████▊ | 17/35 [00:31<00:28, 1.57s/it]\n 51%|█████▏ | 18/35 [00:32<00:26, 1.55s/it]\n 54%|█████▍ | 19/35 [00:35<00:31, 1.98s/it]\n10934 0.3043 0.0202 0.0016 : 54%|█████▍ | 19/35 [00:37<00:31, 1.98s/it]\n10934 0.3043 0.0202 0.0016 : 57%|█████▋ | 20/35 [00:37<00:30, 2.02s/it]\n10934 0.3043 0.0202 0.0016 : 60%|██████ | 21/35 [00:38<00:23, 1.68s/it]\n10934 0.3043 0.0202 0.0016 : 63%|██████▎ | 22/35 [00:41<00:24, 1.90s/it]\n10934 0.3043 0.0202 0.0016 : 66%|██████▌ | 23/35 [00:43<00:23, 1.97s/it]\n10934 0.3043 0.0202 0.0016 : 69%|██████▊ | 24/35 [00:46<00:25, 2.34s/it]\n10934 0.3043 0.0202 0.0016 : 71%|███████▏ | 25/35 [00:49<00:24, 2.50s/it]\n10934 0.3043 0.0202 0.0016 : 74%|███████▍ | 26/35 [00:52<00:23, 2.64s/it]\n10934 0.3043 0.0202 0.0016 : 77%|███████▋ | 27/35 [00:53<00:16, 2.11s/it]\n10934 0.3043 0.0202 0.0016 : 80%|████████ | 28/35 [00:54<00:14, 2.05s/it]\n10934 0.3043 0.0202 0.0016 : 83%|████████▎ | 29/35 [00:57<00:13, 2.19s/it]\n10934 0.3043 0.0202 0.0016 : 86%|████████▌ | 30/35 [01:00<00:12, 2.42s/it]\n10934 0.3043 0.0202 0.0016 : 89%|████████▊ | 31/35 [01:02<00:09, 2.46s/it]\n10934 0.3043 0.0202 0.0016 : 91%|█████████▏| 32/35 [01:04<00:06, 2.24s/it]\n10934 0.3043 0.0202 0.0016 : 94%|█████████▍| 33/35 [01:06<00:04, 2.14s/it]\n10934 0.3043 0.0202 0.0016 : 97%|█████████▋| 34/35 [01:08<00:02, 2.01s/it]\n10934 0.3043 0.0202 0.0016 : 100%|██████████| 35/35 [01:11<00:00, 2.37s/it]\n10934 0.3043 0.0202 0.0016 : 100%|██████████| 35/35 [01:11<00:00, 2.04s/it]\nic| \"Training epoch: \": 'Training epoch: '\n epoch_id: 10\n epoch_size: 35\n batches_per_epoch: 35\n batch_size: 1\n optim_type: 'image'\n\n 0%| | 0/35 [00:00<?, ?it/s]\n 3%|▎ | 1/35 [00:02<01:39, 2.93s/it]\n 6%|▌ | 2/35 [00:04<01:19, 2.41s/it]\n 9%|▊ | 3/35 [00:07<01:23, 2.61s/it]\n 11%|█▏ | 4/35 [00:10<01:25, 2.75s/it]\n 14%|█▍ | 5/35 [00:13<01:24, 2.83s/it]\n 17%|█▋ | 6/35 [00:15<01:12, 2.50s/it]\n 20%|██ | 7/35 [00:17<01:01, 2.20s/it]\n 23%|██▎ | 8/35 [00:19<00:56, 2.10s/it]\n 26%|██▌ | 9/35 [00:20<00:51, 1.98s/it]\n 29%|██▊ | 10/35 [00:22<00:45, 1.83s/it]\n 31%|███▏ | 11/35 [00:23<00:37, 1.55s/it]\n 34%|███▍ | 12/35 [00:24<00:35, 1.55s/it]\n 37%|███▋ | 13/35 [00:27<00:42, 1.94s/it]\n 40%|████ | 14/35 [00:30<00:47, 2.25s/it]\n 43%|████▎ | 15/35 [00:32<00:40, 2.04s/it]\n 46%|████▌ | 16/35 [00:35<00:44, 2.32s/it]\n 49%|████▊ | 17/35 [00:37<00:41, 2.32s/it]\n 51%|█████▏ | 18/35 [00:39<00:37, 2.18s/it]\n 54%|█████▍ | 19/35 [00:42<00:40, 2.50s/it]\n10969 0.2907 0.0206 0.0017 : 54%|█████▍ | 19/35 [00:44<00:40, 2.50s/it]\n10969 0.2907 0.0206 0.0017 : 57%|█████▋ | 20/35 [00:44<00:35, 2.35s/it]\n10969 0.2907 0.0206 0.0017 : 60%|██████ | 21/35 [00:46<00:32, 2.30s/it]\n10969 0.2907 0.0206 0.0017 : 63%|██████▎ | 22/35 [00:49<00:30, 2.36s/it]\n10969 0.2907 0.0206 0.0017 : 66%|██████▌ | 23/35 [00:51<00:27, 2.30s/it]\n10969 0.2907 0.0206 0.0017 : 69%|██████▊ | 24/35 [00:52<00:21, 1.97s/it]\n10969 0.2907 0.0206 0.0017 : 71%|███████▏ | 25/35 [00:54<00:18, 1.90s/it]\n10969 0.2907 0.0206 0.0017 : 74%|███████▍ | 26/35 [00:56<00:17, 1.96s/it]\n10969 0.2907 0.0206 0.0017 : 77%|███████▋ | 27/35 [00:58<00:15, 1.88s/it]\n10969 0.2907 0.0206 0.0017 : 80%|████████ | 28/35 [00:59<00:13, 1.87s/it]\n10969 0.2907 0.0206 0.0017 : 83%|████████▎ | 29/35 [01:01<00:10, 1.77s/it]\n10969 0.2907 0.0206 0.0017 : 86%|████████▌ | 30/35 [01:03<00:09, 1.80s/it]\n10969 0.2907 0.0206 0.0017 : 89%|████████▊ | 31/35 [01:06<00:08, 2.12s/it]\n10969 0.2907 0.0206 0.0017 : 91%|█████████▏| 32/35 [01:09<00:07, 2.39s/it]\n10969 0.2907 0.0206 0.0017 : 94%|█████████▍| 33/35 [01:10<00:03, 1.93s/it]\n10969 0.2907 0.0206 0.0017 : 97%|█████████▋| 34/35 [01:10<00:01, 1.62s/it]\n10969 0.2907 0.0206 0.0017 : 100%|██████████| 35/35 [01:11<00:00, 1.39s/it]\n10969 0.2907 0.0206 0.0017 : 100%|██████████| 35/35 [01:11<00:00, 2.05s/it]\nic| \"Training epoch: \": 'Training epoch: '\n epoch_id: 11\n epoch_size: 35\n batches_per_epoch: 35\n batch_size: 1\n optim_type: 'image'\n\n 0%| | 0/35 [00:00<?, ?it/s]\n 3%|▎ | 1/35 [00:02<01:11, 2.10s/it]\n 6%|▌ | 2/35 [00:03<00:57, 1.75s/it]\n 9%|▊ | 3/35 [00:05<00:57, 1.81s/it]\n 11%|█▏ | 4/35 [00:07<00:52, 1.70s/it]\n 14%|█▍ | 5/35 [00:07<00:42, 1.42s/it]\n 17%|█▋ | 6/35 [00:10<00:49, 1.70s/it]\n 20%|██ | 7/35 [00:11<00:44, 1.58s/it]\n 23%|██▎ | 8/35 [00:13<00:50, 1.85s/it]\n 26%|██▌ | 9/35 [00:15<00:42, 1.65s/it]\n 29%|██▊ | 10/35 [00:16<00:41, 1.67s/it]\n 31%|███▏ | 11/35 [00:17<00:34, 1.43s/it]\n 34%|███▍ | 12/35 [00:19<00:32, 1.40s/it]\n 37%|███▋ | 13/35 [00:20<00:33, 1.55s/it]\n 40%|████ | 14/35 [00:23<00:36, 1.76s/it]\n 43%|████▎ | 15/35 [00:25<00:35, 1.80s/it]\n 46%|████▌ | 16/35 [00:26<00:32, 1.71s/it]\n 49%|████▊ | 17/35 [00:28<00:31, 1.76s/it]\n 51%|█████▏ | 18/35 [00:30<00:28, 1.69s/it]\n 54%|█████▍ | 19/35 [00:30<00:23, 1.46s/it]\n11004 0.2607 0.0210 0.0016 : 54%|█████▍ | 19/35 [00:33<00:23, 1.46s/it]\n11004 0.2607 0.0210 0.0016 : 57%|█████▋ | 20/35 [00:33<00:25, 1.70s/it]\n11004 0.2607 0.0210 0.0016 : 60%|██████ | 21/35 [00:35<00:27, 1.95s/it]\n11004 0.2607 0.0210 0.0016 : 63%|██████▎ | 22/35 [00:38<00:27, 2.11s/it]\n11004 0.2607 0.0210 0.0016 : 66%|██████▌ | 23/35 [00:40<00:24, 2.04s/it]\n11004 0.2607 0.0210 0.0016 : 69%|██████▊ | 24/35 [00:42<00:22, 2.08s/it]\n11004 0.2607 0.0210 0.0016 : 71%|███████▏ | 25/35 [00:43<00:19, 1.91s/it]\n11004 0.2607 0.0210 0.0016 : 74%|███████▍ | 26/35 [00:45<00:17, 1.98s/it]\n11004 0.2607 0.0210 0.0016 : 77%|███████▋ | 27/35 [00:48<00:16, 2.06s/it]\n11004 0.2607 0.0210 0.0016 : 80%|████████ | 28/35 [00:50<00:14, 2.02s/it]\n11004 0.2607 0.0210 0.0016 : 83%|████████▎ | 29/35 [00:51<00:11, 1.86s/it]\n11004 0.2607 0.0210 0.0016 : 86%|████████▌ | 30/35 [00:54<00:10, 2.03s/it]\n11004 0.2607 0.0210 0.0016 : 89%|████████▊ | 31/35 [00:57<00:09, 2.32s/it]\n11004 0.2607 0.0210 0.0016 : 91%|█████████▏| 32/35 [00:59<00:06, 2.22s/it]\n11004 0.2607 0.0210 0.0016 : 94%|█████████▍| 33/35 [01:01<00:04, 2.31s/it]\n11004 0.2607 0.0210 0.0016 : 97%|█████████▋| 34/35 [01:03<00:02, 2.09s/it]\n11004 0.2607 0.0210 0.0016 : 100%|██████████| 35/35 [01:05<00:00, 2.21s/it]\n11004 0.2607 0.0210 0.0016 : 100%|██████████| 35/35 [01:05<00:00, 1.87s/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/data/llff/room/images_4\nLoaded LLFF data /root/.cache/arf/data/llff/room 10.706691140704915 91.66782174279389\nrecentered (3, 4)\n[1.7806027 6.0023217]\nOverriding offset 250-> 250\ndmin = 1.780603, dmax = 6.002322, invz = 0, offset = 250\n\n 0%| | 0/6 [00:00<?, ?it/s]\n 50%|█████ | 3/6 [00:00<00:00, 28.65it/s]\n100%|██████████| 6/6 [00:00<00:00, 30.90it/s]\n\n 0%| | 0/120 [00:00<?, ?it/s]\n100%|██████████| 120/120 [00:00<00:00, 71677.08it/s]\nz_bounds from LLFF: [1.7806026935577393, 6.002321720123291] (not used)\nscene_radius [1.496031746031746, 1.6613756613756614, 1.0]\nWriting to /root/.cache/arf/ckpt_arf/llff/room/80f5a27b-4005-4718-9bd6-3bbeeb39a878/test_renders_path\n\n 0%| | 0/120 [00:00<?, ?it/s]\n 1%| | 1/120 [00:00<01:09, 1.71it/s]\n 2%|▏ | 2/120 [00:01<01:04, 1.83it/s]\n 2%|▎ | 3/120 [00:01<01:03, 1.83it/s]\n 3%|▎ | 4/120 [00:02<01:03, 1.83it/s]\n 4%|▍ | 5/120 [00:02<01:03, 1.82it/s]\n 5%|▌ | 6/120 [00:03<01:02, 1.81it/s]\n 6%|▌ | 7/120 [00:03<01:02, 1.81it/s]\n 7%|▋ | 8/120 [00:04<01:01, 1.82it/s]\n 8%|▊ | 9/120 [00:04<01:00, 1.83it/s]\n 8%|▊ | 10/120 [00:05<00:59, 1.85it/s]\n 9%|▉ | 11/120 [00:05<00:57, 1.89it/s]\n 10%|█ | 12/120 [00:06<00:56, 1.92it/s]\n 11%|█ | 13/120 [00:06<00:54, 1.97it/s]\n 12%|█▏ | 14/120 [00:07<00:52, 2.01it/s]\n 12%|█▎ | 15/120 [00:07<00:51, 2.05it/s]\n 13%|█▎ | 16/120 [00:08<00:50, 2.08it/s]\n 14%|█▍ | 17/120 [00:08<00:49, 2.10it/s]\n 15%|█▌ | 18/120 [00:09<00:48, 2.11it/s]\n 16%|█▌ | 19/120 [00:09<00:48, 2.09it/s]\n 17%|█▋ | 20/120 [00:10<00:48, 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2.15it/s]\n 90%|█████████ | 108/120 [00:57<00:05, 2.15it/s]\n 91%|█████████ | 109/120 [00:57<00:05, 2.15it/s]\n 92%|█████████▏| 110/120 [00:58<00:04, 2.13it/s]\n 92%|█████████▎| 111/120 [00:58<00:04, 2.09it/s]\n 93%|█████████▎| 112/120 [00:59<00:03, 2.04it/s]\n 94%|█████████▍| 113/120 [00:59<00:03, 2.00it/s]\n 95%|█████████▌| 114/120 [01:00<00:03, 1.95it/s]\n 96%|█████████▌| 115/120 [01:00<00:02, 1.91it/s]\n 97%|█████████▋| 116/120 [01:01<00:02, 1.88it/s]\n 98%|█████████▊| 117/120 [01:01<00:01, 1.85it/s]\n 98%|█████████▊| 118/120 [01:02<00:01, 1.84it/s]\n 99%|█████████▉| 119/120 [01:02<00:00, 1.84it/s]\n100%|██████████| 120/120 [01:03<00:00, 1.84it/s]\n100%|██████████| 120/120 [01:03<00:00, 1.89it/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": 1034.115792,
"total_time": 1034.463041
},
"output": "https://replicate.delivery/mgxm/6e852189-87aa-44df-92a8-275be7e17148/test_renders_path.mp4",
"started_at": "2022-06-24T05:34:27.310746Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/2wdxxvwgcnbipeebxbhfzr4te4",
"cancel": "https://api.replicate.com/v1/predictions/2wdxxvwgcnbipeebxbhfzr4te4/cancel"
},
"version": "7bb81e4046b96358001e6275e36eee3e7228fe9f1a0e1515a5f553cbbcad07e7"
}Generated in
Downloading checkpoint for room
Detected LLFF dataset
Using pre-scaled images from /root/.cache/arf/data/llff/room/images_4
Loaded LLFF data /root/.cache/arf/data/llff/room 10.706691140704915 91.66782174279389
recentered (3, 4)
[1.7806027 6.0023217]
Overriding offset 250-> 250
dmin = 1.780603, dmax = 6.002322, invz = 0, offset = 250
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z_bounds from LLFF: [1.7806026935577393, 6.002321720123291] (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/ckpt_svox2/llff/room/ckpt.npz'
ic| grid.basis_dim: 1
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
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)
ic| "Style image: ": 'Style image: '
args.style: '/tmp/tmpb6u9aqcp941d4f18-97f8-4bdf-a0e6-bec0acd53301.png'
style_img.shape: torch.Size([504, 420, 3])
ic| "Training epoch: ": 'Training epoch: '
epoch_id: 0
epoch_size: 26671680
batches_per_epoch: 5335
batch_size: 5000
optim_type: 'ray'
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4859 24.8576 : 91%|█████████ | 4866/5335 [00:54<00:04, 108.01it/s]
4859 24.8576 : 91%|█████████▏| 4878/5335 [00:54<00:04, 102.07it/s]
4879 24.9813 : 91%|█████████▏| 4878/5335 [00:54<00:04, 102.07it/s]
4879 24.9813 : 92%|█████████▏| 4889/5335 [00:54<00:04, 93.78it/s]
4879 24.9813 : 92%|█████████▏| 4899/5335 [00:54<00:04, 90.27it/s]
4899 24.6033 : 92%|█████████▏| 4899/5335 [00:54<00:04, 90.27it/s]
4899 24.6033 : 92%|█████████▏| 4909/5335 [00:54<00:04, 90.99it/s]
4899 24.6033 : 92%|█████████▏| 4919/5335 [00:54<00:04, 93.30it/s]
4919 24.1499 : 92%|█████████▏| 4919/5335 [00:54<00:04, 93.30it/s]
4919 24.1499 : 92%|█████████▏| 4930/5335 [00:54<00:04, 95.75it/s]
4939 24.9934 : 92%|█████████▏| 4930/5335 [00:54<00:04, 95.75it/s]
4939 24.9934 : 93%|█████████▎| 4940/5335 [00:54<00:04, 96.83it/s]
4939 24.9934 : 93%|█████████▎| 4951/5335 [00:54<00:03, 99.44it/s]
4959 23.9928 : 93%|█████████▎| 4951/5335 [00:55<00:03, 99.44it/s]
4959 23.9928 : 93%|█████████▎| 4962/5335 [00:55<00:03, 100.71it/s]
4959 23.9928 : 93%|█████████▎| 4973/5335 [00:55<00:03, 100.88it/s]
4979 24.6053 : 93%|█████████▎| 4973/5335 [00:55<00:03, 100.88it/s]
4979 24.6053 : 93%|█████████▎| 4984/5335 [00:55<00:03, 96.81it/s]
4979 24.6053 : 94%|█████████▎| 4994/5335 [00:55<00:03, 89.95it/s]
4999 24.9791 : 94%|█████████▎| 4994/5335 [00:55<00:03, 89.95it/s]
4999 24.9791 : 94%|█████████▍| 5004/5335 [00:55<00:04, 81.92it/s]
4999 24.9791 : 94%|█████████▍| 5013/5335 [00:55<00:04, 75.82it/s]
5019 25.1175 : 94%|█████████▍| 5013/5335 [00:55<00:04, 75.82it/s]
5019 25.1175 : 94%|█████████▍| 5021/5335 [00:55<00:04, 70.74it/s]
5019 25.1175 : 94%|█████████▍| 5029/5335 [00:55<00:04, 68.68it/s]
5019 25.1175 : 94%|█████████▍| 5039/5335 [00:56<00:03, 75.45it/s]
5039 27.5497 : 94%|█████████▍| 5039/5335 [00:56<00:03, 75.45it/s]
5039 27.5497 : 95%|█████████▍| 5050/5335 [00:56<00:03, 82.49it/s]
5059 25.4145 : 95%|█████████▍| 5050/5335 [00:56<00:03, 82.49it/s]
5059 25.4145 : 95%|█████████▍| 5062/5335 [00:56<00:02, 91.14it/s]
5059 25.4145 : 95%|█████████▌| 5075/5335 [00:56<00:02, 100.02it/s]
5079 26.6469 : 95%|█████████▌| 5075/5335 [00:56<00:02, 100.02it/s]
5079 26.6469 : 95%|█████████▌| 5088/5335 [00:56<00:02, 107.68it/s]
5099 27.8851 : 95%|█████████▌| 5088/5335 [00:56<00:02, 107.68it/s]
5099 27.8851 : 96%|█████████▌| 5101/5335 [00:56<00:02, 113.16it/s]
5099 27.8851 : 96%|█████████▌| 5114/5335 [00:56<00:01, 117.56it/s]
5119 26.5010 : 96%|█████████▌| 5114/5335 [00:56<00:01, 117.56it/s]
5119 26.5010 : 96%|█████████▌| 5127/5335 [00:56<00:01, 119.48it/s]
5139 24.4466 : 96%|█████████▌| 5127/5335 [00:56<00:01, 119.48it/s]
5139 24.4466 : 96%|█████████▋| 5140/5335 [00:56<00:01, 116.40it/s]
5139 24.4466 : 97%|█████████▋| 5152/5335 [00:57<00:01, 108.78it/s]
5159 24.9924 : 97%|█████████▋| 5152/5335 [00:57<00:01, 108.78it/s]
5159 24.9924 : 97%|█████████▋| 5164/5335 [00:57<00:01, 99.70it/s]
5159 24.9924 : 97%|█████████▋| 5175/5335 [00:57<00:01, 92.12it/s]
5179 25.8780 : 97%|█████████▋| 5175/5335 [00:57<00:01, 92.12it/s]
5179 25.8780 : 97%|█████████▋| 5185/5335 [00:57<00:01, 90.72it/s]
5179 25.8780 : 97%|█████████▋| 5197/5335 [00:57<00:01, 97.67it/s]
5199 27.1586 : 97%|█████████▋| 5197/5335 [00:57<00:01, 97.67it/s]
5199 27.1586 : 98%|█████████▊| 5211/5335 [00:57<00:01, 106.94it/s]
5219 26.3455 : 98%|█████████▊| 5211/5335 [00:57<00:01, 106.94it/s]
5219 26.3455 : 98%|█████████▊| 5226/5335 [00:57<00:00, 117.38it/s]
5239 27.0302 : 98%|█████████▊| 5226/5335 [00:57<00:00, 117.38it/s]
5239 27.0302 : 98%|█████████▊| 5242/5335 [00:57<00:00, 127.24it/s]
5239 27.0302 : 99%|█████████▊| 5258/5335 [00:57<00:00, 134.99it/s]
5259 26.0070 : 99%|█████████▊| 5258/5335 [00:58<00:00, 134.99it/s]
5259 26.0070 : 99%|█████████▉| 5274/5335 [00:58<00:00, 140.71it/s]
5279 24.9669 : 99%|█████████▉| 5274/5335 [00:58<00:00, 140.71it/s]
5279 24.9669 : 99%|█████████▉| 5289/5335 [00:58<00:00, 140.97it/s]
5299 25.1446 : 99%|█████████▉| 5289/5335 [00:58<00:00, 140.97it/s]
5299 25.1446 : 99%|█████████▉| 5304/5335 [00:58<00:00, 135.41it/s]
5299 25.1446 : 100%|█████████▉| 5318/5335 [00:58<00:00, 124.45it/s]
5319 24.7313 : 100%|█████████▉| 5318/5335 [00:58<00:00, 124.45it/s]
5319 24.7313 : 100%|█████████▉| 5331/5335 [00:58<00:00, 115.46it/s]
5319 24.7313 : 100%|██████████| 5335/5335 [00:58<00:00, 91.00it/s]
ic| "Training epoch: ": 'Training epoch: '
epoch_id: 1
epoch_size: 26671680
batches_per_epoch: 5335
batch_size: 5000
optim_type: 'ray'
0%| | 0/5335 [00:00<?, ?it/s]
0%| | 3/5335 [00:00<03:02, 29.23it/s]
0%| | 6/5335 [00:00<03:13, 27.49it/s]
0%| | 11/5335 [00:00<02:30, 35.43it/s]
0%| | 16/5335 [00:00<02:11, 40.50it/s]
5354 19.5023 : 0%| | 16/5335 [00:00<02:11, 40.50it/s]
5354 19.5023 : 0%| | 22/5335 [00:00<01:58, 44.87it/s]
5354 19.5023 : 1%| | 28/5335 [00:00<01:52, 47.21it/s]
5354 19.5023 : 1%| | 34/5335 [00:00<01:47, 49.42it/s]
5374 22.0903 : 1%| | 34/5335 [00:00<01:47, 49.42it/s]
5374 22.0903 : 1%| | 41/5335 [00:00<01:38, 53.69it/s]
5374 22.0903 : 1%| | 49/5335 [00:00<01:29, 59.19it/s]
5374 22.0903 : 1%| | 57/5335 [00:01<01:21, 65.06it/s]
5394 22.4892 : 1%| | 57/5335 [00:01<01:21, 65.06it/s]
5394 22.4892 : 1%| | 65/5335 [00:01<01:16, 68.99it/s]
5394 22.4892 : 1%|▏ | 73/5335 [00:01<01:12, 72.11it/s]
5414 22.9025 : 1%|▏ | 73/5335 [00:01<01:12, 72.11it/s]
5414 22.9025 : 2%|▏ | 82/5335 [00:01<01:10, 74.77it/s]
5414 22.9025 : 2%|▏ | 91/5335 [00:01<01:08, 76.74it/s]
5434 22.8703 : 2%|▏ | 91/5335 [00:01<01:08, 76.74it/s]
5434 22.8703 : 2%|▏ | 100/5335 [00:01<01:07, 77.73it/s]
5434 22.8703 : 2%|▏ | 109/5335 [00:01<01:06, 79.05it/s]
5434 22.8703 : 2%|▏ | 117/5335 [00:01<01:06, 78.03it/s]
5454 21.3939 : 2%|▏ | 117/5335 [00:01<01:06, 78.03it/s]
5454 21.3939 : 2%|▏ | 125/5335 [00:01<01:09, 74.59it/s]
5454 21.3939 : 2%|▏ | 133/5335 [00:02<01:14, 69.71it/s]
5474 21.1332 : 2%|▏ | 133/5335 [00:02<01:14, 69.71it/s]
5474 21.1332 : 3%|▎ | 141/5335 [00:02<01:19, 65.04it/s]
5474 21.1332 : 3%|▎ | 148/5335 [00:02<01:24, 61.62it/s]
5474 21.1332 : 3%|▎ | 155/5335 [00:02<01:24, 61.60it/s]
5494 22.5178 : 3%|▎ | 155/5335 [00:02<01:24, 61.60it/s]
5494 22.5178 : 3%|▎ | 162/5335 [00:02<01:23, 62.08it/s]
5494 22.5178 : 3%|▎ | 169/5335 [00:02<01:23, 62.03it/s]
5494 22.5178 : 3%|▎ | 176/5335 [00:02<01:21, 62.99it/s]
5514 25.1444 : 3%|▎ | 176/5335 [00:02<01:21, 62.99it/s]
5514 25.1444 : 3%|▎ | 183/5335 [00:02<01:19, 64.56it/s]
5514 25.1444 : 4%|▎ | 191/5335 [00:03<01:16, 67.40it/s]
5534 22.9705 : 4%|▎ | 191/5335 [00:03<01:16, 67.40it/s]
5534 22.9705 : 4%|▎ | 200/5335 [00:03<01:10, 72.66it/s]
5534 22.9705 : 4%|▍ | 210/5335 [00:03<01:04, 79.93it/s]
5554 24.5430 : 4%|▍ | 210/5335 [00:03<01:04, 79.93it/s]
5554 24.5430 : 4%|▍ | 221/5335 [00:03<00:59, 86.13it/s]
5554 24.5430 : 4%|▍ | 232/5335 [00:03<00:56, 90.42it/s]
5574 26.6955 : 4%|▍ | 232/5335 [00:03<00:56, 90.42it/s]
5574 26.6955 : 5%|▍ | 242/5335 [00:03<00:55, 91.99it/s]
5574 26.6955 : 5%|▍ | 252/5335 [00:03<00:54, 93.86it/s]
5594 25.3195 : 5%|▍ | 252/5335 [00:03<00:54, 93.86it/s]
5594 25.3195 : 5%|▍ | 263/5335 [00:03<00:52, 96.03it/s]
5594 25.3195 : 5%|▌ | 273/5335 [00:03<00:53, 94.43it/s]
5614 24.9697 : 5%|▌ | 273/5335 [00:03<00:53, 94.43it/s]
5614 24.9697 : 5%|▌ | 283/5335 [00:04<00:57, 88.23it/s]
5614 24.9697 : 5%|▌ | 292/5335 [00:04<01:01, 82.39it/s]
5634 24.3198 : 5%|▌ | 292/5335 [00:04<01:01, 82.39it/s]
5634 24.3198 : 6%|▌ | 301/5335 [00:04<01:05, 77.40it/s]
5634 24.3198 : 6%|▌ | 309/5335 [00:04<01:08, 73.43it/s]
5634 24.3198 : 6%|▌ | 317/5335 [00:04<01:14, 67.22it/s]
5654 24.8458 : 6%|▌ | 317/5335 [00:04<01:14, 67.22it/s]
5654 24.8458 : 6%|▌ | 324/5335 [00:04<01:17, 64.34it/s]
5654 24.8458 : 6%|▌ | 331/5335 [00:04<01:18, 63.40it/s]
5654 24.8458 : 6%|▋ | 338/5335 [00:04<01:18, 63.45it/s]
5674 24.1731 : 6%|▋ | 338/5335 [00:04<01:18, 63.45it/s]
5674 24.1731 : 6%|▋ | 346/5335 [00:05<01:15, 66.37it/s]
5674 24.1731 : 7%|▋ | 355/5335 [00:05<01:09, 71.52it/s]
5694 26.9016 : 7%|▋ | 355/5335 [00:05<01:09, 71.52it/s]
5694 26.9016 : 7%|▋ | 365/5335 [00:05<01:03, 78.23it/s]
5694 26.9016 : 7%|▋ | 375/5335 [00:05<00:59, 83.66it/s]
5714 23.6531 : 7%|▋ | 375/5335 [00:05<00:59, 83.66it/s]
5714 23.6531 : 7%|▋ | 385/5335 [00:05<00:57, 86.83it/s]
5714 23.6531 : 7%|▋ | 395/5335 [00:05<00:55, 88.86it/s]
5734 25.7088 : 7%|▋ | 395/5335 [00:05<00:55, 88.86it/s]
5734 25.7088 : 8%|▊ | 405/5335 [00:05<00:55, 89.61it/s]
5734 25.7088 : 8%|▊ | 415/5335 [00:05<00:54, 90.66it/s]
5754 24.9477 : 8%|▊ | 415/5335 [00:05<00:54, 90.66it/s]
5754 24.9477 : 8%|▊ | 425/5335 [00:05<00:55, 88.73it/s]
5754 24.9477 : 8%|▊ | 434/5335 [00:05<00:58, 83.47it/s]
5774 24.3233 : 8%|▊ | 434/5335 [00:06<00:58, 83.47it/s]
5774 24.3233 : 8%|▊ | 443/5335 [00:06<01:02, 77.96it/s]
5774 24.3233 : 8%|▊ | 451/5335 [00:06<01:06, 73.93it/s]
5774 24.3233 : 9%|▊ | 459/5335 [00:06<01:09, 69.86it/s]
5794 18.7561 : 9%|▊ | 459/5335 [00:06<01:09, 69.86it/s]
5794 18.7561 : 9%|▉ | 467/5335 [00:06<01:20, 60.36it/s]
5794 18.7561 : 9%|▉ | 474/5335 [00:06<01:29, 54.42it/s]
5814 22.5768 : 9%|▉ | 474/5335 [00:06<01:29, 54.42it/s]
5814 22.5768 : 9%|▉ | 480/5335 [00:06<01:32, 52.27it/s]
5814 22.5768 : 9%|▉ | 486/5335 [00:06<01:34, 51.54it/s]
5814 22.5768 : 9%|▉ | 492/5335 [00:07<01:34, 51.48it/s]
5814 22.5768 : 9%|▉ | 498/5335 [00:07<01:30, 53.43it/s]
5834 25.0249 : 9%|▉ | 498/5335 [00:07<01:30, 53.43it/s]
5834 25.0249 : 9%|▉ | 505/5335 [00:07<01:25, 56.63it/s]
5834 25.0249 : 10%|▉ | 513/5335 [00:07<01:18, 61.26it/s]
5854 26.6588 : 10%|▉ | 513/5335 [00:07<01:18, 61.26it/s]
5854 26.6588 : 10%|▉ | 521/5335 [00:07<01:13, 65.44it/s]
5854 26.6588 : 10%|▉ | 529/5335 [00:07<01:10, 68.65it/s]
5854 26.6588 : 10%|█ | 537/5335 [00:07<01:07, 70.70it/s]
5874 25.4218 : 10%|█ | 537/5335 [00:07<01:07, 70.70it/s]
5874 25.4218 : 10%|█ | 545/5335 [00:07<01:06, 71.94it/s]
5874 25.4218 : 10%|█ | 553/5335 [00:07<01:05, 72.79it/s]
5894 24.7635 : 10%|█ | 553/5335 [00:08<01:05, 72.79it/s]
5894 24.7635 : 11%|█ | 561/5335 [00:08<01:04, 73.97it/s]
5894 24.7635 : 11%|█ | 569/5335 [00:08<01:04, 73.86it/s]
5894 24.7635 : 11%|█ | 577/5335 [00:08<01:06, 71.07it/s]
5914 24.6983 : 11%|█ | 577/5335 [00:08<01:06, 71.07it/s]
5914 24.6983 : 11%|█ | 585/5335 [00:08<01:11, 66.28it/s]
5914 24.6983 : 11%|█ | 592/5335 [00:08<01:15, 62.76it/s]
5914 24.6983 : 11%|█ | 599/5335 [00:08<01:21, 58.44it/s]
5934 23.6205 : 11%|█ | 599/5335 [00:08<01:21, 58.44it/s]
5934 23.6205 : 11%|█▏ | 605/5335 [00:08<01:24, 56.16it/s]
5934 23.6205 : 11%|█▏ | 611/5335 [00:08<01:25, 55.48it/s]
5934 23.6205 : 12%|█▏ | 617/5335 [00:09<01:27, 54.14it/s]
5954 24.0384 : 12%|█▏ | 617/5335 [00:09<01:27, 54.14it/s]
5954 24.0384 : 12%|█▏ | 623/5335 [00:09<01:28, 53.35it/s]
5954 24.0384 : 12%|█▏ | 629/5335 [00:09<01:29, 52.87it/s]
5954 24.0384 : 12%|█▏ | 635/5335 [00:09<01:28, 53.28it/s]
5974 26.3694 : 12%|█▏ | 635/5335 [00:09<01:28, 53.28it/s]
5974 26.3694 : 12%|█▏ | 641/5335 [00:09<01:26, 54.13it/s]
5974 26.3694 : 12%|█▏ | 648/5335 [00:09<01:20, 58.14it/s]
5974 26.3694 : 12%|█▏ | 656/5335 [00:09<01:14, 62.60it/s]
5994 26.1644 : 12%|█▏ | 656/5335 [00:09<01:14, 62.60it/s]
5994 26.1644 : 12%|█▏ | 665/5335 [00:09<01:08, 68.00it/s]
5994 26.1644 : 13%|█▎ | 674/5335 [00:09<01:04, 72.64it/s]
6014 25.1925 : 13%|█▎ | 674/5335 [00:09<01:04, 72.64it/s]
6014 25.1925 : 13%|█▎ | 683/5335 [00:10<01:01, 75.61it/s]
6014 25.1925 : 13%|█▎ | 692/5335 [00:10<00:59, 78.30it/s]
6034 26.0099 : 13%|█▎ | 692/5335 [00:10<00:59, 78.30it/s]
6034 26.0099 : 13%|█▎ | 701/5335 [00:10<00:58, 79.10it/s]
6034 26.0099 : 13%|█▎ | 710/5335 [00:10<00:57, 80.26it/s]
6034 26.0099 : 13%|█▎ | 719/5335 [00:10<00:57, 80.30it/s]
6054 24.1608 : 13%|█▎ | 719/5335 [00:10<00:57, 80.30it/s]
6054 24.1608 : 14%|█▎ | 728/5335 [00:10<00:59, 77.80it/s]
6054 24.1608 : 14%|█▍ | 736/5335 [00:10<01:02, 73.75it/s]
6074 25.3972 : 14%|█▍ | 736/5335 [00:10<01:02, 73.75it/s]
6074 25.3972 : 14%|█▍ | 744/5335 [00:10<01:06, 68.54it/s]
6074 25.3972 : 14%|█▍ | 751/5335 [00:10<01:12, 63.65it/s]
6074 25.3972 : 14%|█▍ | 758/5335 [00:11<01:14, 61.13it/s]
6094 24.3552 : 14%|█▍ | 758/5335 [00:11<01:14, 61.13it/s]
6094 24.3552 : 14%|█▍ | 765/5335 [00:11<01:15, 60.91it/s]
6094 24.3552 : 14%|█▍ | 772/5335 [00:11<01:14, 61.50it/s]
6094 24.3552 : 15%|█▍ | 779/5335 [00:11<01:11, 63.31it/s]
6114 25.6687 : 15%|█▍ | 779/5335 [00:11<01:11, 63.31it/s]
6114 25.6687 : 15%|█▍ | 786/5335 [00:11<01:10, 64.61it/s]
6114 25.6687 : 15%|█▍ | 794/5335 [00:11<01:07, 67.31it/s]
6134 26.6438 : 15%|█▍ | 794/5335 [00:11<01:07, 67.31it/s]
6134 26.6438 : 15%|█▌ | 803/5335 [00:11<01:01, 73.20it/s]
6134 26.6438 : 15%|█▌ | 813/5335 [00:11<00:56, 80.05it/s]
6154 26.9284 : 15%|█▌ | 813/5335 [00:11<00:56, 80.05it/s]
6154 26.9284 : 15%|█▌ | 824/5335 [00:11<00:51, 87.10it/s]
6154 26.9284 : 16%|█▌ | 834/5335 [00:12<00:49, 90.69it/s]
6174 27.0347 : 16%|█▌ | 834/5335 [00:12<00:49, 90.69it/s]
6174 27.0347 : 16%|█▌ | 845/5335 [00:12<00:47, 94.25it/s]
6174 27.0347 : 16%|█▌ | 856/5335 [00:12<00:46, 96.31it/s]
6194 25.7735 : 16%|█▌ | 856/5335 [00:12<00:46, 96.31it/s]
6194 25.7735 : 16%|█▋ | 867/5335 [00:12<00:45, 98.08it/s]
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ic| "Training epoch: ": 'Training epoch: '
epoch_id: 2
epoch_size: 35
batches_per_epoch: 35
batch_size: 1
optim_type: 'image'
0%| | 0/35 [00:00<?, ?it/s]/root/.pyenv/versions/3.8.8/lib/python3.8/site-packages/torch/nn/functional.py:3609: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. 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: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: 35
batches_per_epoch: 35
batch_size: 1
optim_type: 'image'
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ic| "Training epoch: ": 'Training epoch: '
epoch_id: 4
epoch_size: 35
batches_per_epoch: 35
batch_size: 1
optim_type: 'image'
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ic| "Training epoch: ": 'Training epoch: '
epoch_id: 5
epoch_size: 35
batches_per_epoch: 35
batch_size: 1
optim_type: 'image'
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ic| "Training epoch: ": 'Training epoch: '
epoch_id: 6
epoch_size: 35
batches_per_epoch: 35
batch_size: 1
optim_type: 'image'
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ic| "Training epoch: ": 'Training epoch: '
epoch_id: 7
epoch_size: 35
batches_per_epoch: 35
batch_size: 1
optim_type: 'image'
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ic| "Training epoch: ": 'Training epoch: '
epoch_id: 8
epoch_size: 35
batches_per_epoch: 35
batch_size: 1
optim_type: 'image'
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ic| "Training epoch: ": 'Training epoch: '
epoch_id: 9
epoch_size: 35
batches_per_epoch: 35
batch_size: 1
optim_type: 'image'
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ic| "Training epoch: ": 'Training epoch: '
epoch_id: 10
epoch_size: 35
batches_per_epoch: 35
batch_size: 1
optim_type: 'image'
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ic| "Training epoch: ": 'Training epoch: '
epoch_id: 11
epoch_size: 35
batches_per_epoch: 35
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/data/llff/room/images_4
Loaded LLFF data /root/.cache/arf/data/llff/room 10.706691140704915 91.66782174279389
recentered (3, 4)
[1.7806027 6.0023217]
Overriding offset 250-> 250
dmin = 1.780603, dmax = 6.002322, invz = 0, offset = 250
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z_bounds from LLFF: [1.7806026935577393, 6.002321720123291] (not used)
scene_radius [1.496031746031746, 1.6613756613756614, 1.0]
Writing to /root/.cache/arf/ckpt_arf/llff/room/80f5a27b-4005-4718-9bd6-3bbeeb39a878/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).