{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Verify Onnx with Tensorflow" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' # Suppresses INFO and WARNING messages\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2024-07-22 13:30:26.411181: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "2024-07-22 13:30:26.598472: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "2024-07-22 13:30:26.640295: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", "2024-07-22 13:30:30.158004: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" ] } ], "source": [ "\n", "import tensorflow as tf\n", "import onnxruntime as ort\n", "\n", "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "\n", "test = pd.read_csv(\"hf://datasets/x-g85/x_g85_fn_dataset/\" + \"fn_test.csv\")\n", "\n", "X_test = test[\"text\"]\n", "y_test = test[\"label\"]\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Single Example of dataset\n", "# X_test = pd.Series(X_test[1]) # neccessary to converto to Series for inputs" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Senate confirms retired generals as first two ...\n", "1 Iraq foots the bill for its own destruction\n", "2 One Year Later, Palestinians Live in Rubble Wh...\n", "3 Peru drug clinic fire kills 14 people locked i...\n", "4 Police station attacked in S. Russia, suicide ...\n", "Name: text, dtype: object" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_test[:5]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Text Tokenization | Vectorization Parameters\n", "max_vocab_length = 5000 # how many unique words to use (i.e num rows in embedding vector)\n", "max_length = 300 # max number of words in a comment to use\n", "batch_size = 32" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Setup Text Tokenizer\n", "# from tensorflow.keras.preprocessing.text import tokenizer_from_json\n", "# from tensorflow.keras.preprocessing.sequence import pad_sequences\n", "\n", "# tokenizer = None\n", "\n", "# # Load the tokenizer from the JSON file\n", "# with open('model/tokenizer.json', 'r') as file:\n", "# tokenizer_json = file.read()\n", "# tokenizer = tokenizer_from_json(tokenizer_json)\n", "\n", "# # Use the tokenizer to transform test data\n", "# tokenized_test = tokenizer.texts_to_sequences(X_test)\n", "# X_test = pad_sequences(tokenized_test, maxlen=max_length)\n", "# X_test = X_test.astype(np.float32)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1721655037.708472 1084 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1721655038.184327 1084 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1721655038.184432 1084 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1721655038.188321 1084 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1721655038.188453 1084 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1721655038.188525 1084 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1721655038.499052 1084 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1721655038.499300 1084 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1721655038.499436 1084 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m149/149\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 26ms/step\n" ] } ], "source": [ "model = tf.keras.models.load_model(\"model/x_g85_lstm.keras\")\n", "results_tf = model.predict(X_test, batch_size=batch_size)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[9.9999988e-01],\n", " [5.0881273e-01],\n", " [5.1265997e-01],\n", " ...,\n", " [5.1503539e-01],\n", " [7.0983964e-07],\n", " [4.2161783e-03]], dtype=float32)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], 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"[1.9538294e-07]\n", "[0.58151215]\n", "[5.7726414e-08]\n", "[0.58479464]\n", "[8.170673e-08]\n", "[0.5416038]\n", "[0.5443517]\n", "[0.5327282]\n", "[0.5059621]\n", "[0.12098776]\n", "[5.7904618e-08]\n", "[0.00168928]\n", "[0.5214834]\n", "[1.15970575e-08]\n", "[0.51955754]\n", "[0.53829706]\n", "[0.5526386]\n", "[0.5476735]\n", "[0.58055687]\n", "[0.51942223]\n", "[1.2029823e-05]\n", "[5.3512344e-06]\n", "[1.7280168e-05]\n", "[0.5469572]\n", "[0.55971676]\n", "[0.52306277]\n", "[0.5879358]\n", "[0.5461231]\n", "[0.5707537]\n", "[2.0942377e-06]\n", "[0.51598513]\n", "[0.50436294]\n", "[0.00050376]\n", "[0.55643064]\n", "[1.697327e-07]\n", "[0.5368933]\n", "[4.34869e-06]\n", "[0.5647607]\n", "[0.5246039]\n", "[3.4945733e-06]\n", "[1.9093595e-05]\n", "[4.0072118e-07]\n", "[0.00044299]\n", "[0.50434715]\n", "[0.5567084]\n", "[0.54588664]\n", "[6.8752804e-08]\n", "[0.47572407]\n", "[1.2687067e-05]\n", "[8.468829e-08]\n", "[0.5183472]\n", "[7.7603977e-07]\n", "[1.0456072e-07]\n", "[0.56330854]\n", "[0.55338985]\n", "[0.5412045]\n", "[0.582195]\n", "[0.57299703]\n", "[0.5319386]\n", "[0.51388615]\n", "[0.5232715]\n", "[0.5452314]\n", "[4.2310546e-07]\n", "[0.5596685]\n", "[2.0491058e-07]\n", "[6.029193e-06]\n", "[0.5629379]\n", "[0.54867315]\n", "[0.53791946]\n", "[0.5010129]\n", "[0.5150354]\n", "[7.0983964e-07]\n", "[0.00421618]\n" ] } ], "source": [ "for x in results_tf:\n", " if x < 0.6:\n", " print(x)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tf.squeeze(tf.round(results_tf))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "\n", "# sess = ort.InferenceSession(\"model/x_g85_lstm.onnx\")\n", "\n", "# input_name = sess.get_inputs()[0].name\n", "# results_ort = sess.run(None, {input_name: X_test})\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# # convert from type `list` -> `np.array`\n", "# results_tf = np.array(results_ort)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "\n", "\n", "# for ort_res, tf_res in zip(results_ort, results_tf):\n", "# np.testing.assert_allclose(ort_res, tf_res, rtol=1e-5, atol=1e-5)\n", "\n", "# print(\"Results match\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "deep_learning", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 2 }