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🔨 Fix nncf key issue in nightly job #238

Merged
merged 8 commits into from
May 5, 2022
Merged

🔨 Fix nncf key issue in nightly job #238

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f4b812a
Enable nncf
Apr 19, 2022
899023c
check nncf key
Apr 21, 2022
1d589a0
🔀 Merge branch 'development' into fix/av/nncf
Apr 21, 2022
00e4aa0
match new trainer params + pr comments
Apr 22, 2022
b5087d6
🔀Merge branch 'development' into fix/av/nncf
May 4, 2022
f418d58
Add check for legacy params in config
May 4, 2022
8c6b7f5
🔥 remove legacy params from model configs
May 4, 2022
de42611
Revert removing legacy params and make numproc as null
May 4, 2022
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2 changes: 1 addition & 1 deletion anomalib/utils/callbacks/__init__.py
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Expand Up @@ -92,7 +92,7 @@ def get_callbacks(config: Union[ListConfig, DictConfig]) -> List[Callback]:
nncf_config = yaml.safe_load(OmegaConf.to_yaml(config.optimization.nncf))
callbacks.append(
nncf_callback(
config=nncf_config,
nncf_config=nncf_config,
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Since this is nncf_callback, config would indicate the nncf_config, wouldn't it? In this case, not sure if we need this renaming here.

export_dir=os.path.join(config.project.path, "compressed"),
)
)
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2 changes: 1 addition & 1 deletion tests/helpers/model.py
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Expand Up @@ -73,7 +73,7 @@ def setup_model_train(
config.model.weight_file = weight_file if not fast_run else "weights/last.ckpt"

if nncf:
config.optimization.nncf.apply = True
config.optimization["nncf"] = {"apply": True, "input_info": {"sample_size": None}}
config = update_nncf_config(config)
config.init_weights = None

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2 changes: 1 addition & 1 deletion tests/nightly/models/test_model_nightly.py
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Expand Up @@ -88,7 +88,7 @@ def _test_metrics(self, trainer, config, model, datamodule):
thresholds = OmegaConf.load("tests/nightly/models/performance_thresholds.yaml")

threshold = thresholds[config.model.name][config.dataset.category]
if "optimization" in config.keys() and config.optimization.nncf.apply:
if "optimization" in config.keys() and "nncf" in config.optimization.keys() and config.optimization.nncf.apply:
threshold = threshold.nncf
if not (
np.isclose(results["image_AUROC"], threshold["image_AUROC"], rtol=0.02)
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