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README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

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+{
+  "architectures": [
+    "BoneAgeCropModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration.BoneAgeCropConfig",
+    "AutoModel": "modeling.BoneAgeCropModel"
+  },
+  "backbone": "mobilenetv3_small_100",
+  "dropout": 0.1,
+  "feature_dim": 1024,
+  "in_chans": 1,
+  "model_type": "bone_age_crop",
+  "num_classes": 4,
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.0"
+}

configuration.py

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+from transformers import PretrainedConfig
+
+
+class BoneAgeCropConfig(PretrainedConfig):
+    model_type = "bone_age_crop"
+
+    def __init__(
+        self,
+        backbone="mobilenetv3_small_100",
+        feature_dim=1024,
+        dropout=0.1,
+        num_classes=4,
+        in_chans=1,
+        **kwargs,
+    ):
+        self.backbone = backbone
+        self.feature_dim = feature_dim
+        self.dropout = dropout
+        self.num_classes = num_classes
+        self.in_chans = in_chans
+        super().__init__(**kwargs)

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:194f32fc60c8797353b95647611078db2130de944250879a56060b77a81aea3e
+size 6159428

modeling.py

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+import albumentations as A
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from numpy.typing import NDArray
+from transformers import PreTrainedModel
+from timm import create_model
+from typing import Optional
+
+from .configuration import BoneAgeCropConfig
+
+_PYDICOM_AVAILABLE = False
+try:
+    from pydicom import dcmread
+    from pydicom.pixels import apply_voi_lut
+
+    _PYDICOM_AVAILABLE = True
+except ModuleNotFoundError:
+    pass
+
+
+class GeM(nn.Module):
+    def __init__(
+        self, p: int = 3, eps: float = 1e-6, dim: int = 2, flatten: bool = True
+    ):
+        super().__init__()
+        self.p = nn.Parameter(torch.ones(1) * p)
+        self.eps = eps
+        assert dim in {2, 3}, f"dim must be one of [2, 3], not {dim}"
+        self.dim = dim
+        if self.dim == 2:
+            self.func = F.adaptive_avg_pool2d
+        elif self.dim == 3:
+            self.func = F.adaptive_avg_pool3d
+        self.flatten = nn.Flatten(1) if flatten else nn.Identity()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # assumes x.shape is (n, c, [t], h, w)
+        x = self.func(x.clamp(min=self.eps).pow(self.p), output_size=1).pow(
+            1.0 / self.p
+        )
+        return self.flatten(x)
+
+
+class BoneAgeCropModel(PreTrainedModel):
+    config_class = BoneAgeCropConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.backbone = create_model(
+            model_name=config.backbone,
+            pretrained=False,
+            num_classes=0,
+            global_pool="",
+            features_only=False,
+            in_chans=config.in_chans,
+        )
+        self.pooling = GeM(p=3, dim=2)
+        self.dropout = nn.Dropout(p=config.dropout)
+        self.linear = nn.Linear(config.feature_dim, config.num_classes)
+
+    def normalize(self, x: torch.Tensor) -> torch.Tensor:
+        # [0, 255] -> [-1, 1]
+        mini, maxi = 0.0, 255.0
+        x = (x - mini) / (maxi - mini)
+        x = (x - 0.5) * 2.0
+        return x
+
+    @staticmethod
+    def load_image_from_dicom(path: str) -> Optional[NDArray]:
+        if not _PYDICOM_AVAILABLE:
+            print("`pydicom` is not installed, returning None ...")
+            return None
+        dicom = dcmread(path)
+        arr = apply_voi_lut(dicom.pixel_array, dicom)
+        if dicom.PhotometricInterpretation == "MONOCHROME1":
+            # invert image if needed
+            arr = arr.max() - arr
+
+        arr = arr - arr.min()
+        arr = arr / arr.max()
+        arr = (arr * 255).astype("uint8")
+        return arr
+
+    @staticmethod
+    def preprocess(x: NDArray) -> NDArray:
+        return A.Resize(512, 512, p=1)(image=x)["image"]
+
+    def forward(
+        self, x: torch.Tensor, img_shape: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        # if img_shape is provided, will provide rescaled coordinates
+        # otherwise, provide normalized [0, 1] coordinates
+        # coords format is xywh
+        assert x.size(0) == img_shape.size(
+            0
+        ), f"x.size(0) [{x.size(0)}] must equal img_shape.size(0) [{img_shape.size(0)}]"
+        # img_shape = (batch_dim, 2)
+        # img_shape[:, 0] = height, img_shape[:, 1] = width
+
+        x = self.normalize(x)
+        features = self.pooling(self.backbone(x))
+        coords = self.linear(features).sigmoid()
+
+        if img_shape is None:
+            return coords
+
+        rescaled_coords = coords.clone()
+        rescaled_coords[:, 0] = rescaled_coords[:, 0] * img_shape[:, 1]
+        rescaled_coords[:, 1] = rescaled_coords[:, 1] * img_shape[:, 0]
+        rescaled_coords[:, 2] = rescaled_coords[:, 2] * img_shape[:, 1]
+        rescaled_coords[:, 3] = rescaled_coords[:, 3] * img_shape[:, 0]
+        return rescaled_coords.int()