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roma_unsb/checkpoints/ROMA_UNSB_001/loss_log.txt

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add tau
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================ Training Loss (Sun Feb 23 15:46:44 2025) ================
================ Training Loss (Sun Feb 23 15:52:29 2025) ================
add num_timesteps
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================ Training Loss (Sun Feb 23 16:00:07 2025) ================
File "/home/openxs/jj/roma_unsb/models/roma_unsb_model.py", line 421, in forward bs = self.mutil_real_A0_tokens.size(0) AttributeError: 'list' object has no attribute 'size'
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================ Training Loss (Sun Feb 23 16:02:40 2025) ================
debug
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================ Training Loss (Sun Feb 23 16:05:19 2025) ================
debuging
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================ Training Loss (Sun Feb 23 16:06:44 2025) ================
http://47.108.14.56:3000/c/b78fb9d0-d385-4dce-81dd-5173e1cf037a
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================ Training Loss (Sun Feb 23 16:09:38 2025) ================
Traceback (most recent call last): File "/home/openxs/jj/roma_unsb/train.py", line 47, in <module> model.optimize_parameters() # calculate loss functions, get gradients, update network weights File "/home/openxs/jj/roma_unsb/models/roma_unsb_model.py", line 315, in optimize_parameters self.forward() File "/home/openxs/jj/roma_unsb/models/roma_unsb_model.py", line 445, in forward Xt_1 = self.netG(Xt, self.time, z) File "/home/openxs/anaconda3/envs/I2V/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/home/openxs/anaconda3/envs/I2V/lib/python3.9/site-packages/torch/nn/parallel/data_parallel.py", line 169, in forward return self.module(*inputs[0], **kwargs[0]) File "/home/openxs/anaconda3/envs/I2V/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/home/openxs/jj/roma_unsb/models/networks.py", line 980, in forward feat = layer(feat) File "/home/openxs/anaconda3/envs/I2V/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/home/openxs/anaconda3/envs/I2V/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 463, in forward return self._conv_forward(input, self.weight, self.bias) File "/home/openxs/anaconda3/envs/I2V/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 459, in _conv_forward return F.conv2d(input, weight, bias, self.stride, RuntimeError: Given groups=1, weight of size [64, 3, 7, 7], expected input[1, 1, 1734, 774] to have 3 channels, but got 1 channels instead
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================ Training Loss (Sun Feb 23 16:44:56 2025) ================
fix about netG
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================ Training Loss (Sun Feb 23 16:49:46 2025) ================
================ Training Loss (Sun Feb 23 16:51:03 2025) ================
================ Training Loss (Sun Feb 23 16:51:23 2025) ================
================ Training Loss (Sun Feb 23 18:04:02 2025) ================
================ Training Loss (Sun Feb 23 18:04:39 2025) ================
================ Training Loss (Sun Feb 23 18:05:17 2025) ================
================ Training Loss (Sun Feb 23 18:06:40 2025) ================
================ Training Loss (Sun Feb 23 18:11:48 2025) ================
================ Training Loss (Sun Feb 23 18:13:31 2025) ================
================ Training Loss (Sun Feb 23 18:14:11 2025) ================
================ Training Loss (Sun Feb 23 18:14:29 2025) ================
================ Training Loss (Sun Feb 23 18:16:27 2025) ================
================ Training Loss (Sun Feb 23 18:16:44 2025) ================
================ Training Loss (Sun Feb 23 18:20:39 2025) ================
================ Training Loss (Sun Feb 23 18:21:44 2025) ================
================ Training Loss (Sun Feb 23 18:35:27 2025) ================
================ Training Loss (Sun Feb 23 18:39:21 2025) ================
================ Training Loss (Sun Feb 23 18:40:15 2025) ================
================ Training Loss (Sun Feb 23 18:41:15 2025) ================
fixing CTN
2025-02-23 19:06:35 +08:00
================ Training Loss (Sun Feb 23 18:47:46 2025) ================
================ Training Loss (Sun Feb 23 18:48:36 2025) ================
================ Training Loss (Sun Feb 23 18:50:20 2025) ================
================ Training Loss (Sun Feb 23 18:51:50 2025) ================
================ Training Loss (Sun Feb 23 18:58:45 2025) ================
================ Training Loss (Sun Feb 23 18:59:52 2025) ================
================ Training Loss (Sun Feb 23 19:03:05 2025) ================
================ Training Loss (Sun Feb 23 19:03:57 2025) ================
use kun's ctn method
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================ Training Loss (Sun Feb 23 21:11:47 2025) ================
use kun's forward method
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================ Training Loss (Sun Feb 23 21:17:10 2025) ================
================ Training Loss (Sun Feb 23 21:20:14 2025) ================
================ Training Loss (Sun Feb 23 21:29:03 2025) ================
================ Training Loss (Sun Feb 23 21:34:57 2025) ================
================ Training Loss (Sun Feb 23 21:35:26 2025) ================
debug
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================ Training Loss (Sun Feb 23 22:28:43 2025) ================
================ Training Loss (Sun Feb 23 22:29:04 2025) ================
================ Training Loss (Sun Feb 23 22:29:52 2025) ================
================ Training Loss (Sun Feb 23 22:30:40 2025) ================
================ Training Loss (Sun Feb 23 22:33:48 2025) ================
debug
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================ Training Loss (Sun Feb 23 22:39:16 2025) ================
================ Training Loss (Sun Feb 23 22:39:48 2025) ================
without cnt
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================ Training Loss (Sun Feb 23 22:41:34 2025) ================
================ Training Loss (Sun Feb 23 22:42:01 2025) ================
================ Training Loss (Sun Feb 23 22:44:17 2025) ================
================ Training Loss (Sun Feb 23 22:45:53 2025) ================
================ Training Loss (Sun Feb 23 22:46:48 2025) ================
================ Training Loss (Sun Feb 23 22:47:42 2025) ================
================ Training Loss (Sun Feb 23 22:49:44 2025) ================
================ Training Loss (Sun Feb 23 22:50:29 2025) ================
================ Training Loss (Sun Feb 23 22:51:47 2025) ================
================ Training Loss (Sun Feb 23 22:55:56 2025) ================
================ Training Loss (Sun Feb 23 22:56:19 2025) ================
================ Training Loss (Sun Feb 23 22:57:58 2025) ================
================ Training Loss (Sun Feb 23 22:59:09 2025) ================
================ Training Loss (Sun Feb 23 23:02:36 2025) ================
================ Training Loss (Sun Feb 23 23:03:56 2025) ================
================ Training Loss (Sun Feb 23 23:09:21 2025) ================
================ Training Loss (Sun Feb 23 23:10:05 2025) ================
================ Training Loss (Sun Feb 23 23:11:43 2025) ================
================ Training Loss (Sun Feb 23 23:12:41 2025) ================
================ Training Loss (Sun Feb 23 23:13:05 2025) ================
================ Training Loss (Sun Feb 23 23:13:59 2025) ================
================ Training Loss (Sun Feb 23 23:14:59 2025) ================
withoutCNT
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================ Training Loss (Mon Feb 24 22:59:41 2025) ================
running without cnt named 001
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================ Training Loss (Mon Feb 24 23:01:03 2025) ================
================ Training Loss (Mon Feb 24 23:02:59 2025) ================
================ Training Loss (Mon Feb 24 23:07:07 2025) ================
(epoch: 1, iters: 100, time: 0.155, data: 0.229) G_GAN: 6.946 D_real_ViT: 0.807 D_fake_ViT: 0.309 G: 7.258 SB: 0.056
(epoch: 1, iters: 200, time: 0.174, data: 0.004) G_GAN: 5.494 D_real_ViT: 0.538 D_fake_ViT: 0.287 G: 5.808 SB: 0.054
(epoch: 1, iters: 300, time: 0.185, data: 0.003) G_GAN: 4.615 D_real_ViT: 0.424 D_fake_ViT: 0.302 G: 4.865 SB: 0.073
(epoch: 1, iters: 400, time: 0.191, data: 0.003) G_GAN: 4.170 D_real_ViT: 0.357 D_fake_ViT: 0.315 G: 4.751 SB: 0.055
(epoch: 1, iters: 500, time: 0.196, data: 0.004) G_GAN: 4.437 D_real_ViT: 0.323 D_fake_ViT: 0.265 G: 4.986 SB: 0.062
(epoch: 1, iters: 600, time: 0.199, data: 0.003) G_GAN: 4.024 D_real_ViT: 0.289 D_fake_ViT: 0.252 G: 4.247 SB: 0.073
(epoch: 1, iters: 700, time: 0.202, data: 0.004) G_GAN: 3.999 D_real_ViT: 0.273 D_fake_ViT: 0.232 G: 4.461 SB: 0.058
(epoch: 1, iters: 800, time: 0.204, data: 0.003) G_GAN: 3.754 D_real_ViT: 0.233 D_fake_ViT: 0.241 G: 4.065 SB: 0.054
(epoch: 1, iters: 900, time: 0.205, data: 0.003) G_GAN: 4.083 D_real_ViT: 0.249 D_fake_ViT: 0.207 G: 4.281 SB: 0.059
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