634b68ae50
Fix metavar for --input-size
2025-01-20 10:53:46 -08:00
47790637f2
Implement --no-console-results in inference.py
2025-01-15 11:37:11 +01:00
fc0609bcb6
Add --model-dtype (pure bfloat16/float16) support to inference.py
2025-01-14 11:00:16 -08:00
8ce197e33a
disable abbreviating csv inference output with ellipses
2025-01-14 17:39:48 +01:00
1766a01f96
Cleanup some amp related behaviour to better support different (non-cuda) devices
2024-10-18 13:54:16 -07:00
4d4bdd64a9
Add --torchcompile-mode args to train, validation, inference, benchmark scripts
2024-10-02 15:17:53 -07:00
195d12fdff
Add missing device argument to inference.py dataloader
2024-04-10 09:34:34 -07:00
0f2803de7a
Move ImageNet metadata (aka info) files to timm/data/_info. Add helper classes to make info available for labelling. Update inference.py for first use.
2023-02-06 17:45:03 -08:00
81ca323751
Davit update formatting and fix grad checkpointing ( #7 )
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fixed head to gap->norm->fc as per convnext, along with option for norm->gap->fc
failed tests due to clip convnext models, davit tests passed
2023-01-15 14:34:56 -08:00
927f031293
Major module / path restructure, timm.models.layers -> timm.layers, add _ prefix to all non model modules in timm.models
2022-12-06 15:00:06 -08:00
dbe7531aa3
Update scripts to support torch.compile(). Make --results_file arg more consistent across benchmark/validate/inference. Fix #1570
2022-12-05 10:21:34 -08:00
05637a4bb0
More inference script changes, arg naming, multiple output fmts at once
2022-12-05 10:21:34 -08:00
eceeb9409a
Significant ugprade to inference.py, support for different formats, formatting, etc.
2022-12-05 10:21:34 -08:00
d7e1e7144a
fix: the exeption when topk is 1
2021-05-10 16:52:40 +09:00
ffa90e04d3
fix: the exception not using default topk argument
2021-05-10 16:20:19 +09:00
5114c214fc
Change the Python interpreter to Python 3.x in the scripts
2021-02-09 21:20:28 +02:00
59ec7e6a53
Merge branch 'master' into imagenet21k_datasets_more
2021-01-04 12:11:05 -08:00
e42b140ade
Add --input-size option to scripts to specify full input dimensions from command-line
2021-01-04 00:25:29 +02:00
de6046e213
Initial commit for dataset / parser reorg to support additional datasets / types
2020-12-28 16:59:15 -08:00
f04bdc8c8e
don't forget this file
2020-10-02 23:23:44 +02:00
e8ca45854c
More models in sotabench, more control over sotabench run, dataset filename extraction consistency
2020-09-24 15:56:57 -07:00
7995295968
Merge branch 'logger' into features. Change 'logger' to '_logger'.
2020-07-27 18:00:46 -07:00
6c17d57a2c
Fix some attributions, add copyrights to some file docstrings
2020-07-27 13:44:56 -07:00
78fa0772cc
Leverage python hierachical logger
...
with this update one can tune the kind of logs generated by timm but
training and inference traces are unchanged
2020-06-09 18:28:48 +01:00
0ebcd2eca1
No default img size for inference script. Fixes #109
2020-04-09 01:43:49 -07:00
40fea63ebe
Add checkpoint averaging script. Add headers, shebangs, exec perms to all scripts
2020-01-03 14:57:46 -08:00
c6b32cbe73
A number of tweaks to arguments, epoch handling, config
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* reorganize train args
* allow resolve_data_config to be used with dict args, not just arparse
* stop incrementing epoch before save, more consistent naming vs csv, etc
* update resume and start epoch handling to match above
* stop auto-incrementing epoch in scheduler
2019-06-28 13:49:20 -07:00
6fc886acaf
Remove all prints, change most to logging calls, tweak alignment of batch logs, improve setup.py
2019-06-20 17:29:25 -07:00
aa4354f466
Big re-org, working towards making pip/module as 'timm'
2019-06-19 17:20:51 -07:00
c1a84ecb22
dataset not passed through PrefetchLoader for inference script. Fix #10
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* also, make top5 configurable for lower class count cases
2019-06-11 21:59:56 -07:00
0562b91c38
Add per model crop pct, interpolation defaults, tie it all together
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* create one resolve fn to pull together model defaults + cmd line args
* update attribution comments in some models
* test update train/validation/inference scripts
2019-04-12 22:55:24 -07:00
9c3859fb9c
Uniform pretrained model handling.
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* All models have 'default_cfgs' dict
* load/resume/pretrained helpers factored out
* pretrained load operates on state_dict based on default_cfg
* test all models in validate
* schedule, optim factor factored out
* test time pool wrapper applied based on default_cfg
2019-04-11 21:32:16 -07:00
0bc50e84f8
Lots of refactoring and cleanup.
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* Move 'test time pool' to Module that can be used by any model, remove from DPN
* Remove ResNext model file and combine with ResNet
* Remove fbresnet200 as it was an old conversion and pretrained performance not worth param count
* Cleanup adaptive avgmax pooling and add back conctat variant
* Factor out checkpoint load fn
2019-04-10 14:53:34 -07:00
1e23727f2f
Update inference script for new loader style
2019-04-05 11:58:16 -07:00
31055466fc
Fixup validate/inference script args, fix senet init for better test accuracy
2019-02-22 14:07:50 -08:00
5855b07ae0
Initial commit, puting some ol pieces together
2019-02-01 22:07:34 -08:00
Josua Rieder
Ross Wightman
Fredo Guan
comar
Csaba Kertesz
Matthijs Hollemans
Antoine Broyelle