health_multimodal.image.inference_engine
Classes
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Encapsulate inference-time operations on an image model. |
- class health_multimodal.image.inference_engine.ImageInferenceEngine(image_model, transform)[source]
Encapsulate inference-time operations on an image model.
- Parameters
img_model – Trained image model
transform (
Compose) – Transform to apply to the image after loading. Must return a torch.Tensor that can be input directly to the image model.
- get_projected_global_embedding(image_path)[source]
Compute global image embedding in the joint latent space.
- Parameters
image_path (
Path) – Path to the image to compute embeddings for.- Return type
Tensor- Returns
Torch tensor containing l2-normalised global image embedding [joint_feature_dim,] where joint_feature_dim is the dimensionality of the joint latent space.
- get_projected_patch_embeddings(image_path)[source]
Compute image patch embeddings in the joint latent space, preserving the image grid.
- Parameters
image_path (
Path) – Path to the image to compute embeddings for.- Return type
Tuple[Tensor,Tuple[int,int]]- Returns
A tuple containing the image patch embeddings and the shape of the original image (width, height) before applying transforms.
- load_and_transform_input_image(image_path, transform)[source]
Read an image and apply the transform to it.
Read the image from the given path
Apply transform
Add the batch dimension
Move to the correct device
- Parameters
return_original_shape – Whether to return an extra tuple that has the original shape of the image before the transforms. The tuple returned contains (width, height).
- Return type
Tuple[Tensor,Tuple[int,int]]