Refactor to allow for a wider model in TIMM

src/otx/algo/classification/backbones/timm.py

@@ -1,57 +1,47 @@
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-"""EfficientNetV2 model.
+"""Timm Backbone Class for OTX classification.
 
 Original papers:
 - 'EfficientNetV2: Smaller Models and Faster Training,' https://arxiv.org/abs/2104.00298,
 - 'Adversarial Examples Improve Image Recognition,' https://arxiv.org/abs/1911.09665.
 """
 from __future__ import annotations
 
-from typing import Literal
-
 import timm
 import torch
 from torch import nn
 
-TimmModelType = Literal[
-    "mobilenetv3_large_100_miil_in21k",
-    "mobilenetv3_large_100_miil",
-    "tresnet_m",
-    "tf_efficientnetv2_s.in21k",
-    "tf_efficientnetv2_s.in21ft1k",
-    "tf_efficientnetv2_m.in21k",
-    "tf_efficientnetv2_m.in21ft1k",
-    "tf_efficientnetv2_b0",
-]
-
 
 class TimmBackbone(nn.Module):
-    """Timm backbone model."""
+    """Timm backbone model.
+
+    Args:
+        model_name (str): The name of the model.
+            You can find available models at timm.list_models() or timm.list_pretrained().
+        pretrained (bool, optional): Whether to load pretrained weights. Defaults to False.
+    """
 
     def __init__(
         self,
-        backbone: TimmModelType,
+        model_name: str,
         pretrained: bool = False,
-        pooling_type: str = "avg",
         **kwargs,
     ):
         super().__init__(**kwargs)
-        self.backbone = backbone
+        self.model_name = model_name
         self.pretrained: bool | dict = pretrained
-        self.is_mobilenet = backbone.startswith("mobilenet")
 
         self.model = timm.create_model(
-            self.backbone,
+            self.model_name,
             pretrained=pretrained,
             num_classes=1000,
         )
 
         self.model.classifier = None  # Detach classifier. Only use 'backbone' part in otx.
         self.num_head_features = self.model.num_features
-        self.num_features = self.model.conv_head.in_channels if self.is_mobilenet else self.model.num_features
-        self.pooling_type = pooling_type
+        self.num_features = self.model.num_features
 
     def forward(self, x: torch.Tensor, **kwargs) -> tuple[torch.Tensor]:
         """Forward."""
@@ -60,11 +50,6 @@ def forward(self, x: torch.Tensor, **kwargs) -> tuple[torch.Tensor]:
 
     def extract_features(self, x: torch.Tensor) -> torch.Tensor:
         """Extract features."""
-        if self.is_mobilenet:
-            x = self.model.conv_stem(x)
-            x = self.model.bn1(x)
-            x = self.model.act1(x)
-            return self.model.blocks(x)
         return self.model.forward_features(x)
 
     def get_config_optim(self, lrs: list[float] | float) -> list[dict[str, float]]:

src/otx/algo/classification/timm_model.py

@@ -1,7 +1,7 @@
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-"""EfficientNetV2 model implementation."""
+"""TIMM wrapper model class for OTX."""
 
 from __future__ import annotations
 
@@ -12,7 +12,7 @@
 import torch
 from torch import nn
 
-from otx.algo.classification.backbones.timm import TimmBackbone, TimmModelType
+from otx.algo.classification.backbones.timm import TimmBackbone
 from otx.algo.classification.classifier import HLabelClassifier, ImageClassifier, SemiSLClassifier
 from otx.algo.classification.heads import (
     HierarchicalCBAMClsHead,
@@ -50,12 +50,38 @@
 
 
 class TimmModelForMulticlassCls(OTXMulticlassClsModel):
-    """TimmModel for multi-class classification task."""
+    """TimmModel for multi-class classification task.
+
+    Args:
+        label_info (LabelInfoTypes): The label information for the classification task.
+        model_name (str): The name of the model.
+            You can find available models at timm.list_models() or timm.list_pretrained().
+        input_size (tuple[int, int], optional): Model input size in the order of height and width.
+            Defaults to (224, 224).
+        pretrained (bool, optional): Whether to load pretrained weights. Defaults to True.
+        optimizer (OptimizerCallable, optional): The optimizer callable for training the model.
+        scheduler (LRSchedulerCallable | LRSchedulerListCallable, optional): The learning rate scheduler callable.
+        metric (MetricCallable, optional): The metric callable for evaluating the model.
+            Defaults to MultiClassClsMetricCallable.
+        torch_compile (bool, optional): Whether to compile the model using TorchScript. Defaults to False.
+        train_type (Literal[OTXTrainType.SUPERVISED, OTXTrainType.SEMI_SUPERVISED], optional): The training type.
+
+    Example:
+        1. API
+            >>> model = TimmModelForMulticlassCls(
+            ...     model_name="tf_efficientnetv2_s.in21k",
+            ...     label_info=<Number-of-classes>,
+            ... )
+        2. CLI
+            >>> otx train \
+            ... --model otx.algo.classification.timm_model.TimmModelForMulticlassCls \
+            ... --model.model_name tf_efficientnetv2_s.in21k
+    """
 
     def __init__(
         self,
         label_info: LabelInfoTypes,
-        backbone: TimmModelType,
+        model_name: str,
         input_size: tuple[int, int] = (224, 224),  # input size of default classification data recipe
         pretrained: bool = True,
         optimizer: OptimizerCallable = DefaultOptimizerCallable,
@@ -64,7 +90,7 @@ def __init__(
         torch_compile: bool = False,
         train_type: Literal[OTXTrainType.SUPERVISED, OTXTrainType.SEMI_SUPERVISED] = OTXTrainType.SUPERVISED,
     ) -> None:
-        self.backbone = backbone
+        self.model_name = model_name
         self.pretrained = pretrained
 
         super().__init__(
@@ -92,7 +118,7 @@ def _create_model(self) -> nn.Module:
         return model
 
     def _build_model(self, num_classes: int) -> nn.Module:
-        backbone = TimmBackbone(backbone=self.backbone, pretrained=self.pretrained)
+        backbone = TimmBackbone(model_name=self.model_name, pretrained=self.pretrained)
         neck = GlobalAveragePooling(dim=2)
         if self.train_type == OTXTrainType.SEMI_SUPERVISED:
             return SemiSLClassifier(
@@ -142,20 +168,45 @@ def forward_for_tracing(self, image: torch.Tensor) -> torch.Tensor | dict[str, t
 
 
 class TimmModelForMultilabelCls(OTXMultilabelClsModel):
-    """TimmModel for multi-label classification task."""
+    """TimmModel for multi-label classification task.
+
+    Args:
+        label_info (LabelInfoTypes): The label information for the classification task.
+        model_name (str): The name of the model.
+            You can find available models at timm.list_models() or timm.list_pretrained().
+        input_size (tuple[int, int], optional): Model input size in the order of height and width.
+            Defaults to (224, 224).
+        pretrained (bool, optional): Whether to load pretrained weights. Defaults to True.
+        optimizer (OptimizerCallable, optional): The optimizer callable for training the model.
+        scheduler (LRSchedulerCallable | LRSchedulerListCallable, optional): The learning rate scheduler callable.
+        metric (MetricCallable, optional): The metric callable for evaluating the model.
+            Defaults to MultiLabelClsMetricCallable.
+        torch_compile (bool, optional): Whether to compile the model using TorchScript. Defaults to False.
+
+    Example:
+        1. API
+            >>> model = TimmModelForMultilabelCls(
+            ...     model_name="tf_efficientnetv2_s.in21k",
+            ...     label_info=<Number-of-classes>,
+            ... )
+        2. CLI
+            >>> otx train \
+            ... --model otx.algo.classification.timm_model.TimmModelForMultilabelCls \
+            ... --model.model_name tf_efficientnetv2_s.in21k
+    """
 
     def __init__(
         self,
         label_info: LabelInfoTypes,
-        backbone: TimmModelType,
+        model_name: str,
         input_size: tuple[int, int] = (224, 224),  # input size of default classification data recipe
         pretrained: bool = True,
         optimizer: OptimizerCallable = DefaultOptimizerCallable,
         scheduler: LRSchedulerCallable | LRSchedulerListCallable = DefaultSchedulerCallable,
         metric: MetricCallable = MultiLabelClsMetricCallable,
         torch_compile: bool = False,
     ) -> None:
-        self.backbone = backbone
+        self.model_name = model_name
         self.pretrained = pretrained
 
         super().__init__(
@@ -182,7 +233,7 @@ def _create_model(self) -> nn.Module:
         return model
 
     def _build_model(self, num_classes: int) -> nn.Module:
-        backbone = TimmBackbone(backbone=self.backbone, pretrained=self.pretrained)
+        backbone = TimmBackbone(model_name=self.model_name, pretrained=self.pretrained)
         return ImageClassifier(
             backbone=backbone,
             neck=GlobalAveragePooling(dim=2),
@@ -222,22 +273,47 @@ def forward_for_tracing(self, image: torch.Tensor) -> torch.Tensor | dict[str, t
 
 
 class TimmModelForHLabelCls(OTXHlabelClsModel):
-    """EfficientNetV2 Model for hierarchical label classification task."""
+    """Timm Model for hierarchical label classification task.
+
+    Args:
+        label_info (HLabelInfo): The label information for the classification task.
+        model_name (str): The name of the model.
+            You can find available models at timm.list_models() or timm.list_pretrained().
+        input_size (tuple[int, int], optional): Model input size in the order of height and width.
+            Defaults to (224, 224).
+        pretrained (bool, optional): Whether to load pretrained weights. Defaults to True.
+        optimizer (OptimizerCallable, optional): The optimizer callable for training the model.
+        scheduler (LRSchedulerCallable | LRSchedulerListCallable, optional): The learning rate scheduler callable.
+        metric (MetricCallable, optional): The metric callable for evaluating the model.
+            Defaults to HLabelClsMetricCallable.
+        torch_compile (bool, optional): Whether to compile the model using TorchScript. Defaults to False.
+
+    Example:
+        1. API
+            >>> model = TimmModelForHLabelCls(
+            ...     model_name="tf_efficientnetv2_s.in21k",
+            ...     label_info=<h-label-info>,
+            ... )
+        2. CLI
+            >>> otx train \
+            ... --model otx.algo.classification.timm_model.TimmModelForHLabelCls \
+            ... --model.model_name tf_efficientnetv2_s.in21k
+    """
 
     label_info: HLabelInfo
 
     def __init__(
         self,
         label_info: HLabelInfo,
-        backbone: TimmModelType,
+        model_name: str,
         input_size: tuple[int, int] = (224, 224),  # input size of default classification data recipe
         pretrained: bool = True,
         optimizer: OptimizerCallable = DefaultOptimizerCallable,
         scheduler: LRSchedulerCallable | LRSchedulerListCallable = DefaultSchedulerCallable,
         metric: MetricCallable = HLabelClsMetricCallable,
         torch_compile: bool = False,
     ) -> None:
-        self.backbone = backbone
+        self.model_name = model_name
         self.pretrained = pretrained
 
         super().__init__(
@@ -267,7 +343,7 @@ def _create_model(self) -> nn.Module:
         return model
 
     def _build_model(self, head_config: dict) -> nn.Module:
-        backbone = TimmBackbone(backbone=self.backbone, pretrained=self.pretrained)
+        backbone = TimmBackbone(model_name=self.model_name, pretrained=self.pretrained)
         copied_head_config = copy(head_config)
         copied_head_config["step_size"] = (ceil(self.input_size[0] / 32), ceil(self.input_size[1] / 32))
         return HLabelClassifier(

src/otx/recipe/classification/h_label_cls/efficientnet_v2.yaml

@@ -1,7 +1,7 @@
 model:
   class_path: otx.algo.classification.timm_model.TimmModelForHLabelCls
   init_args:
-    backbone: tf_efficientnetv2_s.in21k
+    model_name: tf_efficientnetv2_s.in21k
 
     optimizer:
       class_path: torch.optim.SGD

src/otx/recipe/classification/multi_class_cls/efficientnet_v2.yaml

@@ -2,7 +2,7 @@ model:
   class_path: otx.algo.classification.timm_model.TimmModelForMulticlassCls
   init_args:
     label_info: 1000
-    backbone: tf_efficientnetv2_s.in21k
+    model_name: tf_efficientnetv2_s.in21k
 
     optimizer:
       class_path: torch.optim.SGD

src/otx/recipe/classification/multi_class_cls/semisl/efficientnet_v2_semisl.yaml

@@ -2,7 +2,7 @@ model:
   class_path: otx.algo.classification.timm_model.TimmModelForMulticlassCls
   init_args:
     label_info: 1000
-    backbone: tf_efficientnetv2_s.in21k
+    model_name: tf_efficientnetv2_s.in21k
     train_type: SEMI_SUPERVISED
 
     optimizer:

src/otx/recipe/classification/multi_label_cls/efficientnet_v2.yaml

@@ -2,7 +2,7 @@ model:
   class_path: otx.algo.classification.timm_model.TimmModelForMultilabelCls
   init_args:
     label_info: 1000
-    backbone: tf_efficientnetv2_s.in21k
+    model_name: tf_efficientnetv2_s.in21k
 
     optimizer:
       class_path: torch.optim.SGD

tests/unit/algo/classification/backbones/test_timm.py

@@ -11,11 +11,11 @@
 
 class TestOTXEfficientNetV2:
     def test_forward(self):
-        model = TimmBackbone(backbone="tf_efficientnetv2_s.in21k")
+        model = TimmBackbone(model_name="tf_efficientnetv2_s.in21k")
         assert model(torch.randn(1, 3, 244, 244))[0].shape == torch.Size([1, 1280, 8, 8])
 
     def test_get_config_optim(self):
-        model = TimmBackbone(backbone="tf_efficientnetv2_s.in21k")
+        model = TimmBackbone(model_name="tf_efficientnetv2_s.in21k")
         assert model.get_config_optim([0.01])[0]["lr"] == 0.01
         assert model.get_config_optim(0.01)[0]["lr"] == 0.01
 
@@ -24,5 +24,5 @@ def test_check_pretrained_weight_download(self):
         if target.exists():
             shutil.rmtree(target)
         assert not target.exists()
-        TimmBackbone(backbone="tf_efficientnetv2_s.in21k", pretrained=True)
+        TimmBackbone(model_name="tf_efficientnetv2_s.in21k", pretrained=True)
         assert target.exists()

tests/unit/algo/classification/test_timm_model.py

@@ -21,7 +21,7 @@
 def fxt_multi_class_cls_model():
     return TimmModelForMulticlassCls(
         label_info=10,
-        backbone="tf_efficientnetv2_s.in21k",
+        model_name="tf_efficientnetv2_s.in21k",
     )
 
 
@@ -59,7 +59,7 @@ def test_predict_step(self, fxt_multi_class_cls_model, fxt_multiclass_cls_batch_
 def fxt_multi_label_cls_model():
     return TimmModelForMultilabelCls(
         label_info=10,
-        backbone="tf_efficientnetv2_s.in21k",
+        model_name="tf_efficientnetv2_s.in21k",
     )
 
 
@@ -97,7 +97,7 @@ def test_predict_step(self, fxt_multi_label_cls_model, fxt_multilabel_cls_batch_
 def fxt_h_label_cls_model(fxt_hlabel_cifar):
     return TimmModelForHLabelCls(
         label_info=fxt_hlabel_cifar,
-        backbone="tf_efficientnetv2_s.in21k",
+        model_name="tf_efficientnetv2_s.in21k",
     )