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jax2tf2onnx - jax.lax.switch with functions.ipynb
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notebook.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"name": "jax2tf2onnx - jax.lax.switch with functions.ipynb",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"gpuClass": "premium"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/josephrocca/db146f00593f86e86a0ecc87c49453d3/notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"source": [
"!pip install git+https://github.com/onnx/[email protected]\n",
"!pip install --upgrade jax jaxlib"
],
"metadata": {
"id": "0YHLndloz1U_"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import jax\n",
"import jax.numpy as jnp\n",
"import tensorflow as tf\n",
"from jax.experimental import jax2tf\n",
"import tf2onnx"
],
"metadata": {
"id": "UFMtdmPeyxpi"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def test_jax(n, operand):\n",
"\n",
" def fn1(a):\n",
" return a+2\n",
" \n",
" def fn2(a):\n",
" return a*2\n",
"\n",
" result = jax.lax.switch(\n",
" n,\n",
" [fn1, fn2],\n",
" operand,\n",
" )\n",
"\n",
" # This approach is incompatible with JAX\n",
" # if n == 0:\n",
" # result = fn1(operand)\n",
" # if n == 1:\n",
" # result = fn2(operand)\n",
"\n",
" # This approach is also incompatible with JAX\n",
" # fns = [fn1, fn2]\n",
" # result = fns[n](operand)\n",
"\n",
" return result\n"
],
"metadata": {
"id": "m0nGZcx_ZAnU"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"jax.jit(test_jax)(1, 2)"
],
"metadata": {
"id": "qrvnptD7GW5c"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"my_model = tf.Module()\n",
"my_model.f = tf.function(jax2tf.convert(test_jax, enable_xla=False), jit_compile=True, autograph=False, input_signature=[\n",
" tf.TensorSpec([], tf.uint32, name=\"n\"),\n",
" tf.TensorSpec([], tf.uint32, name=\"operand\"),\n",
"])\n",
"tf.saved_model.save(my_model, './test')"
],
"metadata": {
"id": "Mvk1dkd20zqm"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# NOT WORKING:\n",
"# ValueError: You passed in an iterable attribute but I cannot figure out its applicable type.\n",
"tf2onnx.convert.from_function(my_model.f, input_signature=[\n",
" tf.TensorSpec([], tf.uint32, name=\"n\"),\n",
" tf.TensorSpec([], tf.uint32, name=\"operand\"),\n",
"], opset=17, output_path=\"test.onnx\")"
],
"metadata": {
"id": "HKFd3XqHJ7L9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "9637614f-51d9-4db8-d548-74fa93ebda92"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tf2onnx/tf_loader.py:715: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
"ERROR:tf2onnx.tf_utils:pass1 convert failed for name: \"jax2tf_test_jax_/switch_case/indexed_case\"\n",
"op: \"StatelessCase\"\n",
"input: \"jax2tf_test_jax_/clip_by_value\"\n",
"input: \"operand\"\n",
"attr {\n",
" key: \"Tin\"\n",
" value {\n",
" list {\n",
" type: DT_UINT32\n",
" }\n",
" }\n",
"}\n",
"attr {\n",
" key: \"Tout\"\n",
" value {\n",
" list {\n",
" type: DT_UINT32\n",
" }\n",
" }\n",
"}\n",
"attr {\n",
" key: \"_read_only_resource_inputs\"\n",
" value {\n",
" list {\n",
" }\n",
" }\n",
"}\n",
"attr {\n",
" key: \"_xla_propagate_compile_time_consts\"\n",
" value {\n",
" b: true\n",
" }\n",
"}\n",
"attr {\n",
" key: \"branches\"\n",
" value {\n",
" list {\n",
" func {\n",
" name: \"jax2tf_test_jax__switch_case_indexed_case_branch0_225\"\n",
" }\n",
" func {\n",
" name: \"jax2tf_test_jax__switch_case_indexed_case_branch1_226\"\n",
" }\n",
" func {\n",
" name: \"jax2tf_test_jax__switch_case_indexed_case_branch2_227\"\n",
" }\n",
" func {\n",
" name: \"jax2tf_test_jax__switch_case_indexed_case_branch3_228\"\n",
" }\n",
" }\n",
" }\n",
"}\n",
"attr {\n",
" key: \"output_shapes\"\n",
" value {\n",
" list {\n",
" shape {\n",
" }\n",
" }\n",
" }\n",
"}\n",
", ex=You passed in an iterable attribute but I cannot figure out its applicable type.\n"
]
},
{
"output_type": "error",
"ename": "ValueError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-e084234668ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensorSpec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muint32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensorSpec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muint32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"operand\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m ], opset=17, output_path=\"test.onnx\")\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tf2onnx/convert.py\u001b[0m in \u001b[0;36mfrom_function\u001b[0;34m(function, input_signature, opset, custom_ops, custom_op_handlers, custom_rewriter, inputs_as_nchw, outputs_as_nchw, extra_opset, shape_override, target, large_model, output_path)\u001b[0m\n\u001b[1;32m 577\u001b[0m \u001b[0mtensors_to_rename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtensors_to_rename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 578\u001b[0m \u001b[0minitialized_tables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitialized_tables\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 579\u001b[0;31m output_path=output_path)\n\u001b[0m\u001b[1;32m 580\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 581\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_proto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexternal_tensor_storage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tf2onnx/convert.py\u001b[0m in \u001b[0;36m_convert_common\u001b[0;34m(frozen_graph, name, large_model, output_path, output_frozen_graph, custom_ops, custom_op_handlers, optimizers, **kwargs)\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimport_graph_def\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfrozen_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 164\u001b[0m g = process_tf_graph(tf_graph, const_node_values=const_node_values,\n\u001b[0;32m--> 165\u001b[0;31m custom_op_handlers=custom_op_handlers, **kwargs)\n\u001b[0m\u001b[1;32m 166\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mconstants\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mENV_TF2ONNX_CATCH_ERRORS\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 167\u001b[0m \u001b[0mcatch_errors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconstants\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mENV_TF2ONNX_CATCH_ERRORS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"TRUE\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tf2onnx/tfonnx.py\u001b[0m in \u001b[0;36mprocess_tf_graph\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 458\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 459\u001b[0m main_g, subgraphs = graphs_from_tf(tf_graph, input_names, output_names, shape_override, const_node_values,\n\u001b[0;32m--> 460\u001b[0;31m ignore_default, use_default)\n\u001b[0m\u001b[1;32m 461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 462\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmain_g\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msubgraphs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tf2onnx/tfonnx.py\u001b[0m in \u001b[0;36mgraphs_from_tf\u001b[0;34m(tf_graph, input_names, output_names, shape_override, const_node_values, ignore_default, use_default)\u001b[0m\n\u001b[1;32m 472\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mshape_override\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 473\u001b[0m \u001b[0mshape_override\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 474\u001b[0;31m \u001b[0mordered_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresolve_functions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 475\u001b[0m \u001b[0msubgraphs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 476\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mordered_func\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tf2onnx/tf_loader.py\u001b[0m in \u001b[0;36mresolve_functions\u001b[0;34m(tf_graph)\u001b[0m\n\u001b[1;32m 766\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdep\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mordered\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdep\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mordered\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 767\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 768\u001b[0;31m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunctions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtflist_to_onnx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 769\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 770\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfdef\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtf_graph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_functions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tf2onnx/tf_utils.py\u001b[0m in \u001b[0;36mtflist_to_onnx\u001b[0;34m(g, shape_override, const_node_values, ignore_default, use_default)\u001b[0m\n\u001b[1;32m 459\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtakeit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 460\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 461\u001b[0;31m \u001b[0monnx_node\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhelper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_node\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mattr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 462\u001b[0m \u001b[0monnx_nodes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0monnx_node\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 463\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/onnx/helper.py\u001b[0m in \u001b[0;36mmake_node\u001b[0;34m(op_type, inputs, outputs, name, doc_string, domain, **kwargs)\u001b[0m\n\u001b[1;32m 118\u001b[0m node.attribute.extend(\n\u001b[1;32m 119\u001b[0m \u001b[0mmake_attribute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 121\u001b[0m if value is not None)\n\u001b[1;32m 122\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/onnx/helper.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[0mmake_attribute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m if value is not None)\n\u001b[0m\u001b[1;32m 122\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/onnx/helper.py\u001b[0m in \u001b[0;36mmake_attribute\u001b[0;34m(key, value, doc_string)\u001b[0m\n\u001b[1;32m 538\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 539\u001b[0m raise ValueError(\n\u001b[0;32m--> 540\u001b[0;31m \u001b[0;34m\"You passed in an iterable attribute but I cannot figure out \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 541\u001b[0m \"its applicable type.\")\n\u001b[1;32m 542\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: You passed in an iterable attribute but I cannot figure out its applicable type."
]
}
]
},
{
"cell_type": "code",
"source": [
"# This doesn't work either - same error.\n",
"# !python -m tf2onnx.convert --opset 17 --saved-model test --output test.onnx"
],
"metadata": {
"id": "QgU9FaVLNDnt"
},
"execution_count": null,
"outputs": []
}
]
}
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