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Feature/upgrade timm mlflow onnxconverter (#155)
* feat: upgraded mlflow, timm and onnxconverter-common + added new backbones * fix: mlflow unbound properties when mlflow is not enabled * feat: update timm import * fix: log message and fasternet t2 backbone configuration * feat: added cleanup step in tests * feat: added cleanup step in tests * feat: added cleanup step in tests * feat: added cleanup step in tests * feat: removed cleanup step * fix: Fix wrong field checked in automatic batch size --------- Co-authored-by: Lorenzo Mammana <lorenzo.mammana@orobix.com>
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CHANGELOG.md

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**Note**: If a tag for the current version already exists, the workflow will skip tag and release creation to avoid duplicates.
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### [2.8.0]
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#### Added
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- 28 new backbone configurations: csatv2, dinov3_vitb16, dinov3_vits16, edgenext_s, efficientnet_em, efficientnetv2_b1, fasternet_t0, fasternet_t1, fasternet_t2, ghostnet_v3, hgnet_v2, mobilenet_edgetpu_v2, mobilenetv4_m, mobilenetv4_s, regnety_016, repvit, rexnet, shvit_s1, shvit_s2, shvit_s3, shvit_s4, starnet_s2, starnet_s3, starnet_s4, swiftformer_l1, swiftformer_l3, swiftformer_s, swiftformer_xs
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#### Updated
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- MLflow-skinny from ^2.3.1 to 3.8.1
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- Timm from 0.9.12 to 1.0.24
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- Onnxconverter-common from ^1.14.0 to 1.16.0
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#### Fixed
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- Avoid unbound variable errors on mlflow client properties if mlflow is not enabled.
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- Fix wrong field checked in automatic batch size corner case that could cause an infinite loop.
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### [2.7.3]
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#### Fixed
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Add checks to ensure mlflow client properties can't be called if mlflow is not enabled to avoid unbound variable errors.
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- Add checks to ensure mlflow client properties can't be called if mlflow is not enabled to avoid unbound variable errors.
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### [2.7.2]
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poetry.lock

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pyproject.toml

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[tool.poetry]
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name = "quadra"
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version = "2.7.3"
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version = "2.8.0"
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description = "Deep Learning experiment orchestration library"
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authors = [
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"Federico Belotti <federico.belotti@orobix.com>",
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hydra_core = "~1.3"
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hydra_colorlog = "~1.2"
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hydra_optuna_sweeper = "~1.2"
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mlflow-skinny = "^2.3.1"
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mlflow-skinny = "3.8.1"
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boto3 = "~1.26"
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minio = "~7.1"
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tensorboard = "~2.20"
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scikit_multilearn = "~0.2"
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tripy = "~1.0"
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h5py = "~3.8"
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timm = "0.9.12"
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timm = "1.0.24"
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segmentation_models_pytorch = "0.5.0"
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anomalib-orobix = "0.7.0.dev150"
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onnx = { version = "1.15.0", optional = true }
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onnxsim = { version = "0.4.28", optional = true }
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onnxruntime_gpu = { version = "1.23.2", optional = true }
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onnxconverter-common = { version = "^1.14.0", optional = true }
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onnxconverter-common = { version = "1.16.0", optional = true }
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[[tool.poetry.source]]
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name = "torch_cu128"

quadra/__init__.py

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__version__ = "2.7.3"
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__version__ = "2.8.0"
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def get_version():
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model:
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_target_: quadra.models.classification.TimmNetworkBuilder
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model_name: csatv2.r512_in1k
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pretrained: true
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freeze: false
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metadata:
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input_size: 512
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output_dim: 386
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nb_heads: 8
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model:
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_target_: quadra.models.classification.TimmNetworkBuilder
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model_name: vit_base_patch16_dinov3
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pretrained: true
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freeze: false
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metadata:
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input_size: 224
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output_dim: 768
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patch_size: 16
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nb_heads: 12
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model:
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_target_: quadra.models.classification.TimmNetworkBuilder
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model_name: vit_small_patch16_dinov3
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pretrained: true
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freeze: false
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metadata:
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input_size: 224
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output_dim: 384
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patch_size: 16
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nb_heads: 6
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model:
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_target_: quadra.models.classification.TimmNetworkBuilder
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model_name: edgenext_small.usi_in1k
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pretrained: true
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freeze: false
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metadata:
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input_size: 256
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output_dim: 304
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nb_heads: 8
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model:
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_target_: quadra.models.classification.TimmNetworkBuilder
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model_name: efficientnet_em.ra2_in1k
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pretrained: true
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freeze: false
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metadata:
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input_size: 240
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output_dim: 1280
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model:
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_target_: quadra.models.classification.TimmNetworkBuilder
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model_name: tf_efficientnetv2_b1.in1k
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pretrained: true
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freeze: false
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metadata:
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input_size: 191
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output_dim: 1280

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