downloading encoders

[harshays]

Hi, I get this error when I run ./scripts/download_pretrained_models/download_large_scale_encoders.sh:

Downloading pretrained model for dataset dead_leaves-mixed
--2022-05-01 17:40:19--  http://data.csail.mit.edu/noiselearning/encoders/large_scale/dead_leaves-mixed/checkpoint_0199.pth.tar
Resolving data.csail.mit.edu (data.csail.mit.edu)... 128.52.129.40
Connecting to data.csail.mit.edu (data.csail.mit.edu)|128.52.129.40|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 369874784 (353M) [application/octet-stream]
Saving to: ‘encoders/large_scale/dead_leaves-mixed/checkpoint_0199.pth.tar’

encoders/large_scale/dead_leaves-mixed/ 100%[=============================================================================>] 352.74M  75.4MB/s    in 4.7s

2022-05-01 17:40:24 (75.4 MB/s) - ‘encoders/large_scale/dead_leaves-mixed/checkpoint_0199.pth.tar’ saved [369874784/369874784]

Downloading pretrained model for dataset feature_vis-dead_leaves
--2022-05-01 17:40:24--  http://data.csail.mit.edu/noiselearning/encoders/large_scale/feature_vis-dead_leaves/checkpoint_0199.pth.tar
Resolving data.csail.mit.edu (data.csail.mit.edu)... 128.52.129.40
Connecting to data.csail.mit.edu (data.csail.mit.edu)|128.52.129.40|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 369735063 (353M) [application/octet-stream]
Saving to: ‘encoders/large_scale/feature_vis-dead_leaves/checkpoint_0199.pth.tar’

encoders/large_scale/feature_vis-dead_l 100%[=============================================================================>] 352.61M  84.1MB/s    in 4.2s

2022-05-01 17:40:29 (84.1 MB/s) - ‘encoders/large_scale/feature_vis-dead_leaves/checkpoint_0199.pth.tar’ saved [369735063/369735063]

Downloading pretrained model for dataset stat-spectrum_color_wmm
--2022-05-01 17:40:29--  http://data.csail.mit.edu/noiselearning/encoders/large_scale/stat-spectrum_color_wmm/checkpoint_0199.pth.tar
Resolving data.csail.mit.edu (data.csail.mit.edu)... 128.52.129.40
Connecting to data.csail.mit.edu (data.csail.mit.edu)|128.52.129.40|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 369735063 (353M) [application/octet-stream]
Saving to: ‘encoders/large_scale/stat-spectrum_color_wmm/checkpoint_0199.pth.tar’

encoders/large_scale/stat-spectrum_colo 100%[=============================================================================>] 352.61M  77.4MB/s    in 4.5s

2022-05-01 17:40:33 (78.4 MB/s) - ‘encoders/large_scale/stat-spectrum_color_wmm/checkpoint_0199.pth.tar’ saved [369735063/369735063]

Downloading pretrained model for dataset stylegan-oriented
--2022-05-01 17:40:34--  http://data.csail.mit.edu/noiselearning/encoders/large_scale/stylegan-oriented/checkpoint_0199.pth.tar
Resolving data.csail.mit.edu (data.csail.mit.edu)... 128.52.129.40
Connecting to data.csail.mit.edu (data.csail.mit.edu)|128.52.129.40|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 369735063 (353M) [application/octet-stream]
Saving to: ‘encoders/large_scale/stylegan-oriented/checkpoint_0199.pth.tar’

encoders/large_scale/stylegan-oriented/ 100%[=============================================================================>] 352.61M  83.1MB/s    in 4.3s

2022-05-01 17:40:38 (81.4 MB/s) - ‘encoders/large_scale/stylegan-oriented/checkpoint_0199.pth.tar’ saved [369735063/369735063]

Downloading pretrained model for dataset mixed-4
--2022-05-01 17:40:38--  http://data.csail.mit.edu/noiselearning/encoders/large_scale/mixed-4/checkpoint_0199.pth.tar
Resolving data.csail.mit.edu (data.csail.mit.edu)... 128.52.129.40
Connecting to data.csail.mit.edu (data.csail.mit.edu)|128.52.129.40|:80... connected.
HTTP request sent, awaiting response... 404 Not Found
2022-05-01 17:40:38 ERROR 404: Not Found.

[mbaradad]

Thanks for pointing this out! The reference to the checkpoint for mixed-4 was not correct and has been updated on the last commit. The correct checkpoint is checkpoint_0799.pth.tar instead of checkpoint_0199.pth.tar for this dataset, as we use 1.3M * 4 samples, but an epoch is still the same number of update iterations as when training with just 1.3M samples.