[Export Refactor][Image Classification] create_model function

src/sparseml/core/session.py

@@ -100,7 +100,7 @@ def pre_initialize_structure(
         This will run the pre-initialize structure method for each modifier in the
         session's lifecycle. This will also set the session's state to the
         pre-initialized state. Takes care of cases when the model(s) structure
-        has been previosuly modified by a modifier.
+        has been previously modified by a modifier.
 
         :param model: the model to pre-initialize the structure for
         :param recipe: the recipe to use for the sparsification, can be a path to a

src/sparseml/export/__init__.py

@@ -0,0 +1,13 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

src/sparseml/export.py → src/sparseml/export/export.py

@@ -14,53 +14,19 @@
 
 import logging
 from pathlib import Path
-from typing import Any, Callable, List, Optional, Union
-
-from pydantic import BaseModel, Field
+from typing import Any, List, Optional, Union
 
+from sparseml.integration_helper_functions import (
+    IntegrationHelperFunctions,
+    infer_integration,
+)
 from sparseml.pytorch.opset import TORCH_DEFAULT_ONNX_OPSET
-from sparsezoo.utils.registry import RegistryMixin
 
 
 _LOGGER = logging.getLogger(__name__)
 AVAILABLE_DEPLOYMENT_TARGETS = ["deepsparse", "onnxruntime"]
 
 
-class IntegrationHelperFunctions(BaseModel, RegistryMixin):
-    """
-    Registry that maps integration names to helper functions
-    for creation/export/manipulation of models for a specific
-    integration.
-    """
-
-    create_model: Optional[Callable] = Field(
-        description="A function that creates a (sparse) "
-        "PyTorch model from a source path."
-    )
-    create_dummy_input: Optional[Callable] = Field(
-        description="A function that creates a dummy input "
-        "given a (sparse) PyTorch model."
-    )
-    export_model: Optional[Callable] = Field(
-        description="A function that exports a (sparse) PyTorch "
-        "model to an ONNX format appropriate for a "
-        "deployment target."
-    )
-    apply_optimizations: Optional[Callable] = Field(
-        description="A function that takes a set of "
-        "optimizations and applies them to an ONNX model."
-    )
-    export_sample_inputs_outputs: Optional[Callable] = Field(
-        description="A function that exports input/output samples given "
-        "a (sparse) PyTorch model."
-    )
-    create_deployment_folder: Optional[Callable] = Field(
-        description="A function that creates a "
-        "deployment folder for the exporter ONNX model"
-        "with the appropriate structure."
-    )
-
-
 def export(
     source_path: Union[Path, str],
     target_path: Union[Path, str],
@@ -166,20 +132,6 @@ def export(
     )
 
 
-def infer_integration(source_path: Union[Path, str]) -> str:
-    """
-    Infer the integration to use for exporting the model from the source_path.
-    For example:
-        - for transformers model the integration
-            can be inferred from `config.json`
-        - for computer vision, the integration
-            can be inferred from the model architecture (`arch_key`)
-    :param source_path: The path to the PyTorch model to export.
-    :return: The name of the integration to use for exporting the model.
-    """
-    raise NotImplementedError
-
-
 def validate_correctness(deployment_path: Union[Path, str]):
     """
     Validate the correctness of the exported model.

src/sparseml/integration_helper_functions.py

@@ -0,0 +1,92 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from enum import Enum
+from pathlib import Path
+from typing import Callable, Optional, Union
+
+from pydantic import BaseModel, Field
+
+from sparsezoo.utils.registry import RegistryMixin
+
+
+__all__ = ["IntegrationHelperFunctions", "infer_integration"]
+
+
+class Integrations(Enum):
+    """
+    Holds the names of the available integrations.
+    """
+
+    image_classification = "image-classification"
+
+
+class IntegrationHelperFunctions(RegistryMixin, BaseModel):
+    """
+    Registry that maps names to helper functions
+    for creation/export/manipulation of models for a specific
+    integration.
+    """
+
+    create_model: Optional[Callable] = Field(
+        description="A function that creates a (sparse) "
+        "PyTorch model from a source path and additional "
+        "arguments"
+    )
+    create_dummy_input: Optional[Callable] = Field(
+        description="A function that creates a dummy input "
+        "given a (sparse) PyTorch model."
+    )
+    export_model: Optional[Callable] = Field(
+        description="A function that exports a (sparse) PyTorch "
+        "model to an ONNX format appropriate for a "
+        "deployment target."
+    )
+    apply_optimizations: Optional[Callable] = Field(
+        description="A function that takes a set of "
+        "optimizations and applies them to an ONNX model."
+    )
+    export_sample_inputs_outputs: Optional[Callable] = Field(
+        description="A function that exports input/output samples given "
+        "a (sparse) PyTorch model."
+    )
+    create_deployment_folder: Optional[Callable] = Field(
+        description="A function that creates a "
+        "deployment folder for the exporter ONNX model"
+        "with the appropriate structure."
+    )
+
+
+def infer_integration(source_path: Union[Path, str]) -> str:
+    """
+    Infer the integration to use for exporting the model from the source_path.
+
+    :param source_path: The path to the PyTorch model to export.
+    :return: The name of the integration to use for exporting the model.
+    """
+    from sparseml.pytorch.image_classification.utils.helpers import (
+        is_image_classification_model,
+    )
+
+    if is_image_classification_model(source_path):
+        # import to register the image_classification integration helper functions
+        import sparseml.pytorch.image_classification.integration_helper_functions  # noqa F401
+
+        return Integrations.image_classification.value
+    else:
+        raise ValueError(
+            f"Could not infer integration from source_path: {source_path}."
+            f"Please specify an argument `integration` from one of"
+            f"the available integrations: {list(Integrations)}."
+        )

src/sparseml/pytorch/image_classification/integration_helper_functions.py

@@ -0,0 +1,43 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from pathlib import Path
+from typing import Any, Union
+
+import torch
+from pydantic import Field
+
+from src.sparseml.integration_helper_functions import (
+    IntegrationHelperFunctions,
+    Integrations,
+)
+from src.sparseml.pytorch.image_classification.utils.helpers import (
+    create_model as create_image_classification_model,
+)
+
+
+def create_model(source_path: Union[Path, str], **kwargs) -> torch.nn.Module:
+    """
+    A contract to create a model from a source path
+
+    :param source_path: The path to the model
+    :return: The torch model
+    """
+    model, *_ = create_image_classification_model(checkpoint_path=source_path, **kwargs)
+    return model
+
+
+@IntegrationHelperFunctions.register(name=Integrations.image_classification.value)
+class ImageClassification(IntegrationHelperFunctions):
+    create_model: Any = Field(default=create_model)

src/sparseml/pytorch/image_classification/utils/helpers.py

@@ -21,6 +21,7 @@
 import warnings
 from contextlib import nullcontext
 from enum import Enum, auto, unique
+from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch
@@ -47,6 +48,7 @@
     default_device,
     download_framework_model_by_recipe_type,
     early_stop_data_loader,
+    load_model,
     model_to_device,
     set_deterministic_seeds,
     torch_distributed_zero_first,
@@ -344,17 +346,25 @@ def get_dataset_and_dataloader(
 # Model creation Helpers
 def create_model(
     checkpoint_path: str,
-    num_classes: int,
+    dataset_name: Optional[str] = None,
+    dataset_path: Optional[str] = None,
+    num_classes: Optional[int] = None,
     recipe_path: Optional[str] = None,
     arch_key: Optional[str] = None,
     pretrained: Union[bool, str] = False,
     pretrained_dataset: Optional[str] = None,
+    one_shot: Optional[str] = None,
+    image_size: int = 224,
     local_rank: int = -1,
     **model_kwargs,
 ) -> Tuple[Module, str, str]:
     """
     :param checkpoint_path: Path to the checkpoint to load. `zoo` for
         downloading weights with respect to a SparseZoo recipe
+    :param dataset_name: The name of the dataset to use for model creation.
+        Defaults to `None`
+    :param dataset_path: The path to the dataset to use for model creation.
+        Defaults to `None`
     :param num_classes: Integer representing the number of output classes
     :param recipe_path: Path or SparseZoo stub to the recipe for downloading,
         respective model. Defaults to `None`
@@ -364,11 +374,38 @@ def create_model(
         False
     :param pretrained_dataset: The dataset to used for pretraining. Defaults to
         None
+    :param one_shot: The recipe to be applied in one-shot manner,
+        before exporting. Defaults to None
+    :param image_size: The image size to use for inference of num_classes
+        (in case num_classes is None) . Defaults to 224
     :param local_rank: The local rank of the process. Defaults to -1
     :param model_kwargs: Additional keyword arguments to pass to the model
     :returns: A tuple containing the mode, the model's arch_key, and the
         checkpoint path
     """
+    _validate_dataset_num_classes(
+        dataset_path=dataset_path, dataset=dataset_name, num_classes=num_classes
+    )
+
+    if num_classes is None:
+        val_dataset, _ = get_dataset_and_dataloader(
+            dataset_name=dataset_name,
+            dataset_path=dataset_path,
+            batch_size=1,
+            image_size=image_size,
+            training=False,
+            loader_num_workers=1,
+            loader_pin_memory=False,
+            max_samples=1,
+        )
+
+        num_classes = infer_num_classes(
+            train_dataset=None,
+            val_dataset=val_dataset,
+            dataset=dataset_name,
+            model_kwargs=model_kwargs,
+        )
+
     with torch_distributed_zero_first(local_rank):
         # only download once locally
         if checkpoint_path and checkpoint_path.startswith("zoo"):
@@ -399,6 +436,16 @@ def create_model(
         else:
             model, arch_key = result
 
+        # TODO: discuss how this is related to the above application of recipes
+        if recipe_path is not None:
+            # TODO: replace this with a new manager introduced by @satrat
+            ScheduledModifierManager.from_yaml(recipe_path).apply_structure(model)
+            if checkpoint_path:
+                load_model(checkpoint_path, model, strict=True)
+
+        if one_shot is not None:
+            ScheduledModifierManager.from_yaml(file_path=one_shot).apply(module=model)
+
         return model, arch_key, checkpoint_path
 
 
@@ -643,3 +690,37 @@ def _download_model_from_zoo_using_recipe(
         Model(recipe_stub),
         recipe_name=recipe_type,
     )
+
+
+def is_image_classification_model(source_path: Union[Path, str]) -> bool:
+    """
+    :param source_path: The path to the model
+    :return: Whether the model is an image classification model or not
+    """
+    if not os.isfile(source_path):
+        checkpoint_path = os.path.join(source_path, "model.pth")
+    else:
+        checkpoint_path = source_path
+    try:
+        checkpoint = torch.load(checkpoint_path)
+        arch_key = checkpoint.get("arch_key")
+        if arch_key:
+            return True
+    except Exception:
+        return False
+
+
+def _validate_dataset_num_classes(
+    dataset: str,
+    dataset_path: str,
+    num_classes: int,
+):
+    if dataset and not dataset_path:
+        raise ValueError(f"found dataset {dataset} but dataset_path not specified")
+    if dataset_path and not dataset:
+        raise ValueError(f"found dataset_path {dataset_path} but dataset not specified")
+    if num_classes is None and (not dataset or not dataset_path):
+        raise ValueError(
+            "If num_classes is not provided, both dataset and dataset_path must be "
+            "set to infer num_classes"
+        )

tests/sparseml/export/__init__.py

@@ -0,0 +1,13 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.