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Update nn-meter_predictor_for_different_model_format.ipynb (#52)
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@Forchapeatl
Forchapeatl committed Dec 23, 2021
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255 changes: 128 additions & 127 deletions examples/nn-meter_predictor_for_different_model_format.ipynb
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Expand Up @@ -2,26 +2,20 @@
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Use nn-Meter for different model format\n",
"In this notebook, we showed nn-Meter examples of latency prediction for different model formats of Tensorflow, PyTorch, ONNX."
],
"metadata": {}
]
},
{
"cell_type": "code",
"execution_count": 1,
"source": [
"# list all supporting latency predictors\n",
"import nn_meter\n",
"predictors = nn_meter.list_latency_predictors()\n",
"for p in predictors:\n",
" print(f\"[Predictor] {p['name']}: version={p['version']}\")"
],
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"[Predictor] cortexA76cpu_tflite21: version=1.0\n",
"[Predictor] adreno640gpu_tflite21: version=1.0\n",
Expand All @@ -30,13 +24,22 @@
]
}
],
"metadata": {}
"source": [
"# list all supporting latency predictors\n",
"import nn_meter\n",
"predictors = nn_meter.list_latency_predictors()\n",
"for p in predictors:\n",
" print(f\"[Predictor] {p['name']}: version={p['version']}\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# define basic information\n",
"import os\n",
"__test_models_folder__ = '../data'\n",
"os.makedirs(__test_models_folder__, exist_ok=True)\n",
"\n",
Expand All @@ -46,49 +49,23 @@
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
],
"outputs": [],
"metadata": {}
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Use nn-Meter for Tensorflow pb File"
],
"metadata": {}
]
},
{
"cell_type": "code",
"execution_count": 3,
"source": [
"import os\n",
"from glob import glob\n",
"import nn_meter\n",
"\n",
"# download data and unzip\n",
"ppath = os.path.join(__test_models_folder__, \"pb_models\")\n",
"if not os.path.isdir(ppath):\n",
" os.mkdir(ppath)\n",
" url = \"https://github.com/microsoft/nn-Meter/releases/download/v1.0-data/pb_models.zip\"\n",
" nn_meter.download_from_url(url, ppath)\n",
"\n",
"test_model_list = glob(ppath + \"/**.pb\")\n",
"\n",
"# load predictor\n",
"predictor = nn_meter.load_latency_predictor(predictor_name, predictor_version)\n",
"\n",
"# predict latency\n",
"result = {}\n",
"for test_model in test_model_list:\n",
" latency = predictor.predict(test_model, model_type=\"pb\") # in unit of ms\n",
" result[os.path.basename(test_model)] = latency\n",
" print(f'[RESULT] predict latency for {test_model}: {latency} ms')\n",
" "
],
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"[RESULT] predict latency for ../data/pb_models/alexnet_0.pb: 13.124763483485053 ms\n",
"[RESULT] predict latency for ../data/pb_models/densenet_0.pb: 73.65728637938379 ms\n",
Expand All @@ -111,18 +88,63 @@
]
}
],
"metadata": {}
"source": [
"import os\n",
"from glob import glob\n",
"import nn_meter\n",
"\n",
"# download data and unzip\n",
"ppath = os.path.join(__test_models_folder__, \"pb_models\")\n",
"if not os.path.isdir(ppath):\n",
" os.mkdir(ppath)\n",
" url = \"https://github.com/microsoft/nn-Meter/releases/download/v1.0-data/pb_models.zip\"\n",
" nn_meter.download_from_url(url, ppath)\n",
"\n",
"test_model_list = glob(ppath + \"/**.pb\")\n",
"\n",
"# load predictor\n",
"predictor = nn_meter.load_latency_predictor(predictor_name, predictor_version)\n",
"\n",
"# predict latency\n",
"result = {}\n",
"for test_model in test_model_list:\n",
" latency = predictor.predict(test_model, model_type=\"pb\") # in unit of ms\n",
" result[os.path.basename(test_model)] = latency\n",
" print(f'[RESULT] predict latency for {test_model}: {latency} ms')\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Use nn-Meter for PyTorch model\n"
],
"metadata": {}
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[RESULT] predict latency for resnet18: 39.32351677226426 ms\n",
"[RESULT] predict latency for alexnet: 13.126684104716283 ms\n",
"[RESULT] predict latency for vgg16: 219.2647723703139 ms\n",
"[RESULT] predict latency for squeezenet: 18.674223659837843 ms\n",
"[RESULT] predict latency for densenet161: 186.56037984132988 ms\n",
"[RESULT] predict latency for inception_v3: 127.98419924992326 ms\n",
"[RESULT] predict latency for googlenet: 32.758087458683384 ms\n",
"[RESULT] predict latency for shufflenet_v2: 5.423898780782251 ms\n",
"[RESULT] predict latency for mobilenet_v2: 9.920667346583885 ms\n",
"[RESULT] predict latency for resnext50_32x4d: 230.96098225315293 ms\n",
"[RESULT] predict latency for wide_resnet50_2: 230.96098225315293 ms\n",
"[RESULT] predict latency for mnasnet: 11.630591102084342 ms\n"
]
}
],
"source": [
"import os\n",
"import torchvision.models as models\n",
Expand All @@ -149,67 +171,23 @@
"for model_name in torchvision_models:\n",
" latency = predictor.predict(torchvision_models[model_name], model_type=\"torch\", input_shape=(1, 3, 224, 224)) \n",
" print(f'[RESULT] predict latency for {model_name}: {latency} ms')"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[RESULT] predict latency for resnet18: 39.32351677226426 ms\n",
"[RESULT] predict latency for alexnet: 13.126684104716283 ms\n",
"[RESULT] predict latency for vgg16: 219.2647723703139 ms\n",
"[RESULT] predict latency for squeezenet: 18.674223659837843 ms\n",
"[RESULT] predict latency for densenet161: 186.56037984132988 ms\n",
"[RESULT] predict latency for inception_v3: 127.98419924992326 ms\n",
"[RESULT] predict latency for googlenet: 32.758087458683384 ms\n",
"[RESULT] predict latency for shufflenet_v2: 5.423898780782251 ms\n",
"[RESULT] predict latency for mobilenet_v2: 9.920667346583885 ms\n",
"[RESULT] predict latency for resnext50_32x4d: 230.96098225315293 ms\n",
"[RESULT] predict latency for wide_resnet50_2: 230.96098225315293 ms\n",
"[RESULT] predict latency for mnasnet: 11.630591102084342 ms\n"
]
}
],
"metadata": {}
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Use nn-Meter for ONNX File"
],
"metadata": {}
]
},
{
"cell_type": "code",
"execution_count": 21,
"source": [
"import os\n",
"from glob import glob\n",
"import nn_meter\n",
"\n",
"# download data and unzip\n",
"ppath = os.path.join(__test_models_folder__, \"onnx_models\")\n",
"if not os.path.isdir(ppath):\n",
" os.mkdir(ppath)\n",
" url = \"https://github.com/microsoft/nn-Meter/releases/download/v1.0-data/onnx_models.zip\"\n",
" nn_meter.download_from_url(url, ppath)\n",
"\n",
"test_model_list = glob(ppath + \"/**.onnx\")\n",
"\n",
"# load predictor\n",
"predictor = nn_meter.load_latency_predictor(predictor_name, predictor_version)\n",
"\n",
"# predict latency\n",
"result = {}\n",
"for test_model in test_model_list:\n",
" latency = predictor.predict(test_model, model_type=\"onnx\") # in unit of ms\n",
" result[os.path.basename(test_model)] = latency\n",
" print(f'[RESULT] predict latency for {os.path.basename(test_model)}: {latency} ms')"
],
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"[RESULT] predict latency for alexnet_0.onnx: 13.12668410471628 ms\n",
"[RESULT] predict latency for densenet_0.onnx: 186.5603798413299 ms\n",
Expand All @@ -225,46 +203,46 @@
]
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"# Use nn-Meter for nn-Meter IR Graph"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 23,
"source": [
"import os\n",
"from glob import glob\n",
"import nn_meter\n",
"\n",
"# download data and unzip\n",
"ppath = os.path.join(__test_models_folder__, \"nnmeter_ir_graphs\")\n",
"ppath = os.path.join(__test_models_folder__, \"onnx_models\")\n",
"if not os.path.isdir(ppath):\n",
" os.mkdir(ppath)\n",
" url = \"https://github.com/microsoft/nn-Meter/releases/download/v1.0-data/ir_graphs.zip\"\n",
" url = \"https://github.com/microsoft/nn-Meter/releases/download/v1.0-data/onnx_models.zip\"\n",
" nn_meter.download_from_url(url, ppath)\n",
"\n",
"test_model_list = glob(ppath + \"/**.json\")\n",
"test_model_list = glob(ppath + \"/**.onnx\")\n",
"\n",
"# load predictor\n",
"predictor = nn_meter.load_latency_predictor(predictor_name, predictor_version)\n",
"\n",
"# predict latency\n",
"result = {}\n",
"for test_model in test_model_list:\n",
" latency = predictor.predict(test_model, model_type=\"nnmeter-ir\") # in unit of ms\n",
" latency = predictor.predict(test_model, model_type=\"onnx\") # in unit of ms\n",
" result[os.path.basename(test_model)] = latency\n",
" print(f'[RESULT] predict latency for {os.path.basename(test_model)}: {latency} ms')"
],
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Use nn-Meter for nn-Meter IR Graph"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"[RESULT] predict latency for alexnet_0.json: 13.124763483485058 ms\n",
"[RESULT] predict latency for densenet_0.json: 73.65728637938379 ms\n",
Expand All @@ -287,31 +265,54 @@
]
}
],
"metadata": {}
"source": [
"import os\n",
"from glob import glob\n",
"import nn_meter\n",
"\n",
"# download data and unzip\n",
"ppath = os.path.join(__test_models_folder__, \"nnmeter_ir_graphs\")\n",
"if not os.path.isdir(ppath):\n",
" os.mkdir(ppath)\n",
" url = \"https://github.com/microsoft/nn-Meter/releases/download/v1.0-data/ir_graphs.zip\"\n",
" nn_meter.download_from_url(url, ppath)\n",
"\n",
"test_model_list = glob(ppath + \"/**.json\")\n",
"\n",
"# load predictor\n",
"predictor = nn_meter.load_latency_predictor(predictor_name, predictor_version)\n",
"\n",
"# predict latency\n",
"result = {}\n",
"for test_model in test_model_list:\n",
" latency = predictor.predict(test_model, model_type=\"nnmeter-ir\") # in unit of ms\n",
" result[os.path.basename(test_model)] = latency\n",
" print(f'[RESULT] predict latency for {os.path.basename(test_model)}: {latency} ms')"
]
}
],
"metadata": {
"orig_nbformat": 4,
"interpreter": {
"hash": "725f784512f384579f6470be215ba42b52a9bbd25ecead6d24b2cff5bb6ad2c7"
},
"kernelspec": {
"display_name": "Python 3.6.10 64-bit ('py36': conda)",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.6.10",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"file_extension": ".py"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3.6.10 64-bit ('py36': conda)"
"pygments_lexer": "ipython3",
"version": "3.6.10"
},
"interpreter": {
"hash": "725f784512f384579f6470be215ba42b52a9bbd25ecead6d24b2cff5bb6ad2c7"
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"orig_nbformat": 4
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"nbformat": 4,
"nbformat_minor": 2
}
}

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