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marcoleewow/coremltools 4.0b1 pytorch LSTM conversion.ipynb

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coremltools==4.0b1, torch==1.5.0
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coremltools 4.0b1 pytorch LSTM conversion.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:root:scikit-learn version 0.22 is not supported. Minimum required version: 0.17. Maximum required version: 0.19.2. Disabling scikit-learn conversion API.\n",
"WARNING:root:TensorFlow version 2.0.0 is not supported. Minimum required version: 2.1.0 .TensorFlow conversion will be disabled.\n"
]
},
{
"data": {
"text/plain": [
"StackedBLSTM(\n",
" (lstm): LSTM(10, 256, num_layers=2, bidirectional=True)\n",
")"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import torch\n",
"from torch import nn\n",
"import coremltools as ct\n",
"\n",
"# hyperparameters\n",
"num_layers = 2\n",
"batch_size = 1\n",
"num_timesteps = 5\n",
"input_size = 10\n",
"hidden_size = 256\n",
"\n",
"\n",
"class StackedBLSTM(nn.Module):\n",
"\n",
" def __init__(self,\n",
" input_size,\n",
" hidden_size,\n",
" num_layers): \n",
" super().__init__()\n",
" self.lstm = nn.LSTM(input_size,\n",
" hidden_size,\n",
" num_layers,\n",
" bidirectional=True,\n",
" batch_first=False)\n",
"\n",
" def forward(self, x, h_0, c_0):\n",
" x, _ = self.lstm(x, (h_0, c_0))\n",
" return x\n",
" \n",
"pytorch_model = StackedBLSTM(input_size, hidden_size, num_layers)\n",
"pytorch_model.eval()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# dummy inputs for tracing\n",
"dummy_input = torch.rand(num_timesteps, batch_size, input_size)\n",
"h_0 = torch.zeros(num_layers * 2, batch_size, hidden_size)\n",
"c_0 = torch.zeros(num_layers * 2, batch_size, hidden_size)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"StackedBLSTM(\n",
" original_name=StackedBLSTM\n",
" (lstm): LSTM(original_name=LSTM)\n",
")"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"traced_model = torch.jit.trace(pytorch_model, (dummy_input, h_0, c_0))\n",
"traced_model"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Converting Frontend ==> MIL Ops: 89%|████████▉ | 8/9 [00:00<00:00, 3502.92 ops/s]\n"
]
},
{
"ename": "ValueError",
"evalue": "CoreML does not support stacked LSTM layers (LSTM with num_layers > 1). Received 2. Redefine as multiple layers if this is the desired implementation.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-c2eff6b187ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m inputs=[ct.TensorType(name=\"X\", shape=dummy_input.shape),\n\u001b[1;32m 4\u001b[0m \u001b[0mct\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensorType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"h_0\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mh_0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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----> 5\u001b[0;31m ct.TensorType(name=\"c_0\", shape=c_0.shape)]\n\u001b[0m\u001b[1;32m 6\u001b[0m )\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/_converters_entry.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(model, source, inputs, outputs, classifier_config, minimum_deployment_target, **kwargs)\u001b[0m\n\u001b[1;32m 297\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 298\u001b[0m \u001b[0mclassifier_config\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclassifier_config\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 299\u001b[0;31m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 300\u001b[0m )\n\u001b[1;32m 301\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/mil/converter.py\u001b[0m in \u001b[0;36m_convert\u001b[0;34m(model, convert_from, convert_to, converter_registry, **kwargs)\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0mbackend_converter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbackend_converter_type\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 119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m \u001b[0mprog\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfrontend_converter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 \u001b[0mcommon_pass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbackend_converter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/mil/converter.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mfrontend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtorch\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/mil/frontend/torch/load.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(model_spec, debug, **kwargs)\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mprog\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/mil/frontend/torch/load.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(model_spec, debug, **kwargs)\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 75\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---> 76\u001b[0;31m \u001b[0mprog\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\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 77\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mRuntimeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 78\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdebug\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"convert function\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\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/opt/conda/lib/python3.6/site-packages/coremltools/converters/mil/frontend/torch/converter.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 301\u001b[0m \u001b[0;31m# Add the rest of the operations\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m \u001b[0mconvert_nodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\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 303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[0mgraph_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/mil/frontend/torch/ops.py\u001b[0m in \u001b[0;36mconvert_nodes\u001b[0;34m(context, graph)\u001b[0m\n\u001b[1;32m 53\u001b[0m )\n\u001b[1;32m 54\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;32m---> 55\u001b[0;31m \u001b[0m_add_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode\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 56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;31m# We've generated all the outputs the graph needs, terminate conversion.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/mil/frontend/torch/ops.py\u001b[0m in \u001b[0;36mlstm\u001b[0;34m(context, node)\u001b[0m\n\u001b[1;32m 946\u001b[0m \u001b[0;34m\"with num_layers > 1). Received {}. Redefine as \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 947\u001b[0m \u001b[0;34m\" multiple layers if this is the desired \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 948\u001b[0;31m \u001b[0;34m\"implementation.\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_layers\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 949\u001b[0m )\n\u001b[1;32m 950\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: CoreML does not support stacked LSTM layers (LSTM with num_layers > 1). Received 2. Redefine as multiple layers if this is the desired implementation."
]
}
],
"source": [
"mlmodel = ct.convert(\n",
" traced_model,\n",
" inputs=[ct.TensorType(name=\"X\", shape=dummy_input.shape),\n",
" ct.TensorType(name=\"h_0\", shape=h_0.shape),\n",
" ct.TensorType(name=\"c_0\", shape=c_0.shape)]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RecursiveScriptModule(\n",
" original_name=StackedBLSTM\n",
" (lstm): RecursiveScriptModule(original_name=LSTM)\n",
")"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scripted_model = torch.jit.script(pytorch_model)\n",
"scripted_model"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"ename": "RuntimeError",
"evalue": "isTensor() INTERNAL ASSERT FAILED at /pytorch/aten/src/ATen/core/ivalue_inl.h:111, please report a bug to PyTorch. Expected Tensor but got Bool",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-5568a41a23b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m inputs=[ct.TensorType(name=\"X\", shape=dummy_input.shape),\n\u001b[1;32m 4\u001b[0m \u001b[0mct\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensorType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"h_0\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mh_0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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----> 5\u001b[0;31m ct.TensorType(name=\"c_0\", shape=c_0.shape)]\n\u001b[0m\u001b[1;32m 6\u001b[0m )\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/_converters_entry.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(model, source, inputs, outputs, classifier_config, minimum_deployment_target, **kwargs)\u001b[0m\n\u001b[1;32m 297\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 298\u001b[0m \u001b[0mclassifier_config\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclassifier_config\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 299\u001b[0;31m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 300\u001b[0m )\n\u001b[1;32m 301\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/mil/converter.py\u001b[0m in \u001b[0;36m_convert\u001b[0;34m(model, convert_from, convert_to, converter_registry, **kwargs)\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0mbackend_converter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbackend_converter_type\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 119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m \u001b[0mprog\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfrontend_converter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 \u001b[0mcommon_pass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbackend_converter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/mil/converter.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mfrontend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtorch\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/mil/frontend/torch/load.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(model_spec, debug, **kwargs)\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"outputs\"\u001b[0m\u001b[0;34m,\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 72\u001b[0m \u001b[0mcut_at_symbols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cut_at_symbols\"\u001b[0m\u001b[0;34m,\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[0;32m---> 73\u001b[0;31m \u001b[0mconverter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTorchConverter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorchscript\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcut_at_symbols\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 74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 75\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/opt/conda/lib/python3.6/site-packages/coremltools/converters/mil/frontend/torch/converter.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, torchscript, inputs, outputs, cut_at_symbols)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTranscriptionContext\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--> 140\u001b[0;31m \u001b[0mraw_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_expand_and_optimize_ir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtorchscript\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 141\u001b[0m self.graph = InternalTorchIRGraph(\n\u001b[1;32m 142\u001b[0m \u001b[0mraw_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcut_at_symbols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/mil/frontend/torch/converter.py\u001b[0m in \u001b[0;36m_expand_and_optimize_ir\u001b[0;34m(torchscript)\u001b[0m\n\u001b[1;32m 327\u001b[0m \u001b[0;31m# inputs to the graph.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 328\u001b[0m graph, params = _torch._C._jit_pass_lower_graph(\n\u001b[0;32m--> 329\u001b[0;31m \u001b[0mtorchscript\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorchscript\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_c\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 330\u001b[0m )\n\u001b[1;32m 331\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mRuntimeError\u001b[0m: isTensor() INTERNAL ASSERT FAILED at /pytorch/aten/src/ATen/core/ivalue_inl.h:111, please report a bug to PyTorch. Expected Tensor but got Bool"
]
}
],
"source": [
"mlmodel = ct.convert(\n",
" scripted_model,\n",
" inputs=[ct.TensorType(name=\"X\", shape=dummy_input.shape),\n",
" ct.TensorType(name=\"h_0\", shape=h_0.shape),\n",
" ct.TensorType(name=\"c_0\", shape=c_0.shape)]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BLSTM(\n",
" (lstm0): LSTM(10, 256, bidirectional=True)\n",
" (lstm1): LSTM(512, 256, bidirectional=True)\n",
")"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Redefine as multiple layers\n",
"\n",
"class BLSTM(nn.Module):\n",
" def __init__(self,\n",
" input_size,\n",
" hidden_size): \n",
" super().__init__()\n",
" self.lstm0 = nn.LSTM(input_size, hidden_size, bidirectional=True, batch_first=False)\n",
" self.lstm1 = nn.LSTM(hidden_size * 2, hidden_size, bidirectional=True, batch_first=False)\n",
" \n",
" def forward(self, x, h_0, c_0):\n",
" x, _ = self.lstm0(x, (h_0[:2], c_0[:2]))\n",
" x, _ = self.lstm1(x, (h_0[2:4], c_0[2:4]))\n",
" return x\n",
"\n",
"pytorch_model = BLSTM(input_size, hidden_size)\n",
"pytorch_model.eval()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BLSTM(\n",
" original_name=BLSTM\n",
" (lstm0): LSTM(original_name=LSTM)\n",
" (lstm1): LSTM(original_name=LSTM)\n",
")"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"traced_model = torch.jit.trace(pytorch_model, (dummy_input, h_0, c_0))\n",
"traced_model"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Converting Frontend ==> MIL Ops: 97%|█████████▋| 37/38 [00:00<00:00, 100.85 ops/s]\n",
"Running MIL optimization passes: 100%|██████████| 13/13 [00:01<00:00, 10.36 passes/s]\n",
"Translating MIL ==> MLModel Ops: 77%|███████▋ | 46/60 [00:00<00:00, 359.89 ops/s]\n"
]
},
{
"ename": "ValueError",
"evalue": "Layer with name \"_lstm_h0_reshaped\" has already been added. Please use a unique name.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-c2eff6b187ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m inputs=[ct.TensorType(name=\"X\", shape=dummy_input.shape),\n\u001b[1;32m 4\u001b[0m \u001b[0mct\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensorType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"h_0\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mh_0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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----> 5\u001b[0;31m ct.TensorType(name=\"c_0\", shape=c_0.shape)]\n\u001b[0m\u001b[1;32m 6\u001b[0m )\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/_converters_entry.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(model, source, inputs, outputs, classifier_config, minimum_deployment_target, **kwargs)\u001b[0m\n\u001b[1;32m 297\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 298\u001b[0m \u001b[0mclassifier_config\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclassifier_config\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 299\u001b[0;31m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 300\u001b[0m )\n\u001b[1;32m 301\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/mil/converter.py\u001b[0m in \u001b[0;36m_convert\u001b[0;34m(model, convert_from, convert_to, converter_registry, **kwargs)\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0mprog\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfrontend_converter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0mcommon_pass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbackend_converter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/mil/converter.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/mil/backend/nn/load.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(prog, **kwargs)\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[0mbuilder\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 238\u001b[0m \u001b[0mprog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunctions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"main\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperations\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 239\u001b[0;31m \u001b[0mprog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunctions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"main\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\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 240\u001b[0m )\n\u001b[1;32m 241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/mil/backend/nn/op_mapping.py\u001b[0m in \u001b[0;36mconvert_ops\u001b[0;34m(const_context, builder, ops, outputs)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mop_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menclosing_block\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 49\u001b[0m \u001b[0;31m# const is globally shared in nn.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m \u001b[0mmapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconst_context\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuilder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\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 51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mov\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/mil/backend/nn/op_mapping.py\u001b[0m in \u001b[0;36msqueeze\u001b[0;34m(const_context, builder, op)\u001b[0m\n\u001b[1;32m 1200\u001b[0m \u001b[0moutput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\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[0m\n\u001b[1;32m 1201\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1202\u001b[0;31m \u001b[0msqueeze_all\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxes\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[0m\u001b[1;32m 1203\u001b[0m )\n\u001b[1;32m 1204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/models/neural_network/builder.py\u001b[0m in \u001b[0;36madd_squeeze\u001b[0;34m(self, name, input_name, output_name, axes, squeeze_all)\u001b[0m\n\u001b[1;32m 7085\u001b[0m \"\"\"\n\u001b[1;32m 7086\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7087\u001b[0;31m \u001b[0mspec_layer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_generic_layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0minput_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0moutput_name\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 7088\u001b[0m \u001b[0mspec_layer_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mspec_layer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7089\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0maxes\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/models/neural_network/builder.py\u001b[0m in \u001b[0;36m_add_generic_layer\u001b[0;34m(self, name, input_names, output_names, input_ranks, input_shapes, output_ranks, output_shapes)\u001b[0m\n\u001b[1;32m 1027\u001b[0m raise ValueError(\n\u001b[1;32m 1028\u001b[0m \u001b[0;34m'Layer with name \"%s\" has already been added. Please use a unique name.'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1029\u001b[0;31m \u001b[0;34m%\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1030\u001b[0m )\n\u001b[1;32m 1031\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer_specs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgeneric_layer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Layer with name \"_lstm_h0_reshaped\" has already been added. Please use a unique name."
]
}
],
"source": [
"mlmodel = ct.convert(\n",
" traced_model,\n",
" inputs=[ct.TensorType(name=\"X\", shape=dummy_input.shape),\n",
" ct.TensorType(name=\"h_0\", shape=h_0.shape),\n",
" ct.TensorType(name=\"c_0\", shape=c_0.shape)]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RecursiveScriptModule(\n",
" original_name=BLSTM\n",
" (lstm0): RecursiveScriptModule(original_name=LSTM)\n",
" (lstm1): RecursiveScriptModule(original_name=LSTM)\n",
")"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scripted_model = torch.jit.script(pytorch_model)\n",
"scripted_model"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"ename": "RuntimeError",
"evalue": "isTensor() INTERNAL ASSERT FAILED at /pytorch/aten/src/ATen/core/ivalue_inl.h:111, please report a bug to PyTorch. Expected Tensor but got Bool",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-11-5568a41a23b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m inputs=[ct.TensorType(name=\"X\", shape=dummy_input.shape),\n\u001b[1;32m 4\u001b[0m \u001b[0mct\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensorType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"h_0\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mh_0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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----> 5\u001b[0;31m ct.TensorType(name=\"c_0\", shape=c_0.shape)]\n\u001b[0m\u001b[1;32m 6\u001b[0m )\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/_converters_entry.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(model, source, inputs, outputs, classifier_config, minimum_deployment_target, **kwargs)\u001b[0m\n\u001b[1;32m 297\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 298\u001b[0m \u001b[0mclassifier_config\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclassifier_config\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 299\u001b[0;31m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 300\u001b[0m )\n\u001b[1;32m 301\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/mil/converter.py\u001b[0m in \u001b[0;36m_convert\u001b[0;34m(model, convert_from, convert_to, converter_registry, **kwargs)\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0mbackend_converter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbackend_converter_type\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 119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m \u001b[0mprog\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfrontend_converter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 \u001b[0mcommon_pass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbackend_converter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/mil/converter.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mfrontend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtorch\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/mil/frontend/torch/load.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(model_spec, debug, **kwargs)\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"outputs\"\u001b[0m\u001b[0;34m,\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 72\u001b[0m \u001b[0mcut_at_symbols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cut_at_symbols\"\u001b[0m\u001b[0;34m,\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[0;32m---> 73\u001b[0;31m \u001b[0mconverter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTorchConverter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorchscript\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcut_at_symbols\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 74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 75\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/opt/conda/lib/python3.6/site-packages/coremltools/converters/mil/frontend/torch/converter.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, torchscript, inputs, outputs, cut_at_symbols)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTranscriptionContext\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--> 140\u001b[0;31m \u001b[0mraw_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_expand_and_optimize_ir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtorchscript\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 141\u001b[0m self.graph = InternalTorchIRGraph(\n\u001b[1;32m 142\u001b[0m \u001b[0mraw_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcut_at_symbols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/coremltools/converters/mil/frontend/torch/converter.py\u001b[0m in \u001b[0;36m_expand_and_optimize_ir\u001b[0;34m(torchscript)\u001b[0m\n\u001b[1;32m 327\u001b[0m \u001b[0;31m# inputs to the graph.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 328\u001b[0m graph, params = _torch._C._jit_pass_lower_graph(\n\u001b[0;32m--> 329\u001b[0;31m \u001b[0mtorchscript\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorchscript\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_c\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 330\u001b[0m )\n\u001b[1;32m 331\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mRuntimeError\u001b[0m: isTensor() INTERNAL ASSERT FAILED at /pytorch/aten/src/ATen/core/ivalue_inl.h:111, please report a bug to PyTorch. Expected Tensor but got Bool"
]
}
],
"source": [
"mlmodel = ct.convert(\n",
" scripted_model,\n",
" inputs=[ct.TensorType(name=\"X\", shape=dummy_input.shape),\n",
" ct.TensorType(name=\"h_0\", shape=h_0.shape),\n",
" ct.TensorType(name=\"c_0\", shape=c_0.shape)]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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