feat: publishing infernet-container-starter v0.2.0

2024-03-29 10:50:13 -04:00
parent 41aaa152e6
commit 4545223364
155 changed files with 6086 additions and 257 deletions
--- a/projects/onnx-iris/contracts/src/IrisClassifier.sol
+++ b/projects/onnx-iris/contracts/src/IrisClassifier.sol
@ -0,0 +1,67 @@
+// SPDX-License-Identifier: BSD-3-Clause-Clear
+pragma solidity ^0.8.13;
+
+import {console2} from "forge-std/console2.sol";
+import {CallbackConsumer} from "infernet-sdk/consumer/Callback.sol";
+
+
+contract IrisClassifier is CallbackConsumer {
+    string private EXTREMELY_COOL_BANNER = "\n\n"
+    "_____  _____ _______ _    _         _\n"
+    "|  __ \\|_   _|__   __| |  | |  /\\   | |\n"
+    "| |__) | | |    | |  | |  | | /  \\  | |\n"
+    "|  _  /  | |    | |  | |  | |/ /\\ \\ | |\n"
+    "| | \\ \\ _| |_   | |  | |__| / ____ \\| |____\n"
+    "|_|  \\_\\_____|  |_|   \\____/_/    \\_\\______|\n\n";
+
+    constructor(address coordinator) CallbackConsumer(coordinator) {}
+
+    function classifyIris() public {
+        /// @dev Iris data is in the following format:
+        /// @dev [sepal_length, sepal_width, petal_length, petal_width]
+        /// @dev the following vector corresponds to the following properties:
+        ///     "sepal_length": 5.5cm
+        ///     "sepal_width": 2.4cm
+        ///     "petal_length": 3.8cm
+        ///     "petal_width": 1.1cm
+        /// @dev The data is normalized & scaled.
+        /// refer to [this function in the model's repository](https://github.com/ritual-net/simple-ml-models/blob/03ebc6fb15d33efe20b7782505b1a65ce3975222/iris_classification/iris_inference_pytorch.py#L13)
+        /// for more info on normalization.
+        /// @dev The data is adjusted by 6 decimals
+
+        uint256[] memory iris_data = new uint256[](4);
+        iris_data[0] = 1_038_004;
+        iris_data[1] = 558_610;
+        iris_data[2] = 1_103_782;
+        iris_data[3] = 1_712_096;
+
+        _requestCompute(
+            "onnx-iris",
+            abi.encode(iris_data),
+            20 gwei,
+            1_000_000,
+            1
+        );
+    }
+
+    function _receiveCompute(
+        uint32 subscriptionId,
+        uint32 interval,
+        uint16 redundancy,
+        address node,
+        bytes calldata input,
+        bytes calldata output,
+        bytes calldata proof
+    ) internal override {
+        console2.log(EXTREMELY_COOL_BANNER);
+        (bytes memory raw_output, bytes memory processed_output) = abi.decode(output, (bytes, bytes));
+        (uint256[] memory classes) = abi.decode(raw_output, (uint256[]));
+        uint256 setosa = classes[0];
+        uint256 versicolor = classes[1];
+        uint256 virginica = classes[2];
+        console2.log("predictions: (adjusted by 6 decimals, 1_000_000 = 100%, 1_000 = 0.1%)");
+        console2.log("Setosa: ", setosa);
+        console2.log("Versicolor: ", versicolor);
+        console2.log("Virginica: ", virginica);
+    }
+}