Repository: xenova/transformers.js
File: src/models.js (JavaScript)

Code before:
 * @param {InferenceSession} session The InferenceSession object that will be run.
 * @param {Record<string, Tensor>} inputs The inputs to check.
 * @returns {Record<string, Tensor>} A Promise that resolves to the checked inputs.
 * @throws {Error} If any inputs are missing.
 * @private
 */
function validateInputs(session, inputs) {
    /**
     * NOTE: Create either a shallow or deep copy based on `onnx.wasm.proxy`
     * @type {Record<string, Tensor>}
     */
    const checkedInputs = Object.create(null);
    const missingInputs = [];
    for (const inputName of session.inputNames) {
        const tensor = inputs[inputName];
        // Rare case where one of the model's input names corresponds to a built-in
        // object name (e.g., toString), which would cause a simple (!tensor) check to fail,
        // because it's not undefined but a function.
        if (!(tensor instanceof Tensor)) {
            missingInputs.push(inputName);
            continue;
        }
        // NOTE: When `env.wasm.proxy is true` the tensor is moved across the Worker
        // boundary, the ownership is transferred to the worker, invalidating the tensor.
        // So, in this case, we simply sacrifice a clone for it.
        checkedInputs[inputName] = env.wasm.proxy ? tensor.clone() : tensor;
    }
    if (missingInputs.length > 0) {
        throw new Error(
            `An error occurred during model execution: "Missing the following inputs: ${missingInputs.join(', ')}.`);
    }

    const numInputsProvided = Object.keys(inputs).length;
    const numInputsNeeded = session.inputNames.length;
    if (numInputsProvided > numInputsNeeded) {
        // No missing inputs, but too many inputs were provided.
        // Warn the user and ignore the extra inputs.
        let ignored = Object.keys(inputs).filter(inputName => !session.inputNames.includes(inputName));
        console.warn(`WARNING: Too many inputs were provided (${numInputsProvided} > ${numInputsNeeded}). The following inputs will be ignored: "${ignored.join(', ')}".`);
    }

    return checkedInputs;
}

/**
 * Executes an InferenceSession using the specified inputs.
 * NOTE: `inputs` must contain at least the input names of the model.
 *  - If additional inputs are passed, they will be ignored.
 *  - If inputs are missing, an error will be thrown.
 * 
 * @param {InferenceSession} session The InferenceSession object to run.

Review comment (suggestion):
```suggestion
        // NOTE: When `env.wasm.proxy is true` the tensor is moved across the Worker
        // boundary, transferring ownership to the worker and invalidating the tensor.
        // So, in this case, we simply sacrifice a clone for it.
        checkedInputs[inputName] = env.wasm.proxy ? tensor.clone() : tensor;
```

Code after:
 * @param {InferenceSession} session The InferenceSession object that will be run.
 * @param {Record<string, Tensor>} inputs The inputs to check.
 * @returns {Record<string, Tensor>} The checked inputs.
 * @throws {Error} If any inputs are missing.
 * @private
 */
function validateInputs(session, inputs) {
    /**
     * NOTE: Create either a shallow or deep copy based on `onnx.wasm.proxy`
     * @type {Record<string, Tensor>}
     */
    const checkedInputs = Object.create(null);
    const missingInputs = [];
    for (const inputName of session.inputNames) {
        const tensor = inputs[inputName];
        // Rare case where one of the model's input names corresponds to a built-in
        // object name (e.g., toString), which would cause a simple (!tensor) check to fail,
        // because it's not undefined but a function.
        if (!(tensor instanceof Tensor)) {
            missingInputs.push(inputName);
            continue;
        }
        // NOTE: When `env.wasm.proxy is true` the tensor is moved across the Worker
        // boundary, transferring ownership to the worker and invalidating the tensor.
        // So, in this case, we simply sacrifice a clone for it.
        checkedInputs[inputName] = env.wasm.proxy ? tensor.clone() : tensor;
    }
    if (missingInputs.length > 0) {
        throw new Error(
            `An error occurred during model execution: "Missing the following inputs: ${missingInputs.join(', ')}.`);
    }

    const numInputsProvided = Object.keys(inputs).length;
    const numInputsNeeded = session.inputNames.length;
    if (numInputsProvided > numInputsNeeded) {
        // No missing inputs, but too many inputs were provided.
        // Warn the user and ignore the extra inputs.
        let ignored = Object.keys(inputs).filter(inputName => !session.inputNames.includes(inputName));
        console.warn(`WARNING: Too many inputs were provided (${numInputsProvided} > ${numInputsNeeded}). The following inputs will be ignored: "${ignored.join(', ')}".`);
    }

    return checkedInputs;
}

/**
 * Executes an InferenceSession using the specified inputs.
 * NOTE: `inputs` must contain at least the input names of the model.
 *  - If additional inputs are passed, they will be ignored.
 *  - If inputs are missing, an error will be thrown.
 * 
 * @param {InferenceSession} session The InferenceSession object to run.