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Improve the correctness_performance tutorial, ignore other files in output
1 parent c310262 commit d1b5640

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Lines changed: 78 additions & 39 deletions

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‎.gitignore‎

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.ipynb_checkpoints
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/tutorials/output/*
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/tutorials/output/*/
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!/tutorials/output/README.md

‎tutorials/CorrectnessVerificationAndPerformanceComparison.ipynb‎

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@@ -20,9 +20,18 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"WARNING:root:This caffe2 python run does not have GPU support. Will run in CPU only mode.\n",
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"WARNING:root:Debug message: No module named caffe2_pybind11_state_gpu\n"
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]
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}
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],
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"source": [
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"from __future__ import absolute_import\n",
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"from __future__ import division\n",
@@ -33,6 +42,8 @@
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"import numpy as np\n",
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"import torch\n",
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"import onnx\n",
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"import torch.nn as nn\n",
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"import torch.nn.functional as F\n",
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"\n",
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"from caffe2.proto import caffe2_pb2\n",
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"from caffe2.python import core\n",
@@ -42,24 +53,32 @@
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" benchmark_caffe2_model, benchmark_pytorch_model\n",
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"\n",
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"\n",
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"class MyModel(torch.nn.Module):\n",
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" '''\n",
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" This is simple model for demonstration purpose.\n",
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" It requires two 2-D tensors as input,\n",
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" and returns the multiply of the two inputs.\n",
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" '''\n",
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"class MNIST(nn.Module):\n",
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"\n",
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" def __init__(self):\n",
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" super(MyModel, self).__init__()\n",
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" super(MNIST, self).__init__()\n",
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" self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n",
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" self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n",
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" self.conv2_drop = nn.Dropout2d()\n",
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" self.fc1 = nn.Linear(320, 50)\n",
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" self.fc2 = nn.Linear(50, 10)\n",
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"\n",
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" def forward(self, m1, m2):\n",
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" return torch.mm(m1, m2)\n",
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" def forward(self, x):\n",
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" x = F.relu(F.max_pool2d(self.conv1(x), 2))\n",
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" x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n",
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" x = x.view(-1, 320)\n",
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" x = F.relu(self.fc1(x))\n",
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" x = F.dropout(x, training=self.training)\n",
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" x = self.fc2(x)\n",
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" return F.log_softmax(x)\n",
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"\n",
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"\n",
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"# Create a pytorch model.\n",
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"pytorch_model = MyModel()\n",
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"pytorch_model = MNIST()\n",
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"pytorch_model.train(False)\n",
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"\n",
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"# Make the inputs in tuple format.\n",
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"inputs = (Variable(torch.randn(3, 4)), Variable(torch.randn(4, 5)))"
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"inputs = (Variable(torch.randn(3, 1, 28, 28), requires_grad=True), )"
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]
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},
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{
@@ -71,18 +90,40 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"graph(%0 : Float(3, 4)\n",
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" %1 : Float(4, 5)) {\n",
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" %2 : UNKNOWN_TYPE = Constant[value={0}]()\n",
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" %3 : Float(3, 5) = Gemm[alpha=1, beta=0, broadcast=1](%0, %1, %2)\n",
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" return (%3);\n",
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"graph(%0 : Float(3, 1, 28, 28)\n",
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" %1 : Float(10, 1, 5, 5)\n",
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" %2 : Float(10)\n",
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" %3 : Float(20, 10, 5, 5)\n",
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" %4 : Float(20)\n",
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" %5 : Float(50, 320)\n",
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" %6 : Float(50)\n",
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" %7 : Float(10, 50)\n",
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" %8 : Float(10)) {\n",
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" %9 : UNKNOWN_TYPE = Conv[kernel_shape=[5, 5], strides=[1, 1], pads=[0, 0, 0, 0], dilations=[1, 1], group=1](%0, %1)\n",
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" %10 : Float(3, 10, 24, 24) = Add[broadcast=1, axis=1](%9, %2)\n",
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" %11 : Float(3, 10, 12, 12) = MaxPool[kernel_shape=[2, 2], pads=[0, 0], strides=[2, 2]](%10)\n",
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" %12 : Float(3, 10, 12, 12) = Relu(%11)\n",
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" %13 : UNKNOWN_TYPE = Conv[kernel_shape=[5, 5], strides=[1, 1], pads=[0, 0, 0, 0], dilations=[1, 1], group=1](%12, %3)\n",
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" %14 : Float(3, 20, 8, 8) = Add[broadcast=1, axis=1](%13, %4)\n",
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" %15 : Float(3, 20, 4, 4) = MaxPool[kernel_shape=[2, 2], pads=[0, 0], strides=[2, 2]](%14)\n",
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" %16 : Float(3, 20, 4, 4) = Relu(%15)\n",
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" %17 : Float(3, 320) = Reshape[shape=[-1, 320]](%16)\n",
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" %18 : Float(320!, 50!) = Transpose[perm=[1, 0]](%5)\n",
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" %20 : Float(3, 50) = Gemm[alpha=1, beta=1, broadcast=1](%17, %18, %6)\n",
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" %21 : Float(3, 50) = Relu(%20)\n",
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" %22 : Float(3, 50), %23 : UNKNOWN_TYPE = Dropout[is_test=1, ratio=0.5](%21)\n",
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" %24 : Float(50!, 10!) = Transpose[perm=[1, 0]](%7)\n",
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" %26 : Float(3, 10) = Gemm[alpha=1, beta=1, broadcast=1](%22, %24, %8)\n",
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" %27 : Float(3, 10) = Softmax[axis=1](%26)\n",
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" %28 : Float(3, 10) = Log(%27)\n",
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" return (%28);\n",
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"}\n",
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"\n",
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"Check the ONNX model.\n"
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"source": [
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"# Convert the ONNX model to a Caffe2 model.\n",
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"print(\"Convert the model to a Caffe2 model.\")\n",
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"init_net, predict_net = Caffe2Backend.onnx_graph_to_caffe2_net(onnx_model.graph)\n",
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"\n",
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"# Set the device option.\n",
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"device_opts = core.DeviceOption(caffe2_pb2.CPU, 0)\n",
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"init_net.device_option.CopyFrom(device_opts)\n",
131-
"predict_net.device_option.CopyFrom(device_opts)"
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"init_net, predict_net = Caffe2Backend.onnx_graph_to_caffe2_net(onnx_model.graph, device=\"CPU\")"
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]
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},
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{
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 4,
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"metadata": {
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"collapsed": true
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},
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 5,
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"metadata": {
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"collapsed": true
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},
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 6,
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"metadata": {
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"collapsed": true
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},
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"# Check the decimal precision of the exported Caffe2.\n",
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"expected_decimal = 5\n",
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"for p, c in zip([pytorch_results], caffe2_results):\n",
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" if device_opts.device_type == caffe2_pb2.CUDA:\n",
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" p.cpu()\n",
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" np.testing.assert_almost_equal(p.data.cpu().numpy(), c, decimal=expected_decimal)\n",
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"print(\"The exported model achieves {}-decimal precision.\".format(expected_decimal))"
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]
@@ -241,24 +275,26 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"PyTorch model's execution time is 0.00998973846436 / iteration.\n",
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"Caffe2 model's execution time is 0.00594469998032 / iteration.\n"
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"PyTorch model's execution time is 0.637650489807 milliseconds/ iteration, 1568.25724434 iterations per second.\n",
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"Caffe2 model's execution time is 0.43121188879 milliseconds / iteration, 2319.04552262 iterations per second\n"
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]
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}
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],
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"source": [
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"pytorch_times = benchmark_pytorch_model(pytorch_model, inputs)\n",
258-
"caffe2_times = benchmark_caffe2_model(init_net, predict_net)\n",
291+
"pytorch_time = benchmark_pytorch_model(pytorch_model, inputs)\n",
292+
"caffe2_time = benchmark_caffe2_model(init_net, predict_net)\n",
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"\n",
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"print(\"PyTorch model's execution time is {} / iteration.\".format(pytorch_times[0]))\n",
261-
"print(\"Caffe2 model's execution time is {} / iteration.\".format(caffe2_times[0]))"
294+
"print(\"PyTorch model's execution time is {} milliseconds/ iteration, {} iterations per second.\".format(\n",
295+
" pytorch_time, 1000 / pytorch_time))\n",
296+
"print(\"Caffe2 model's execution time is {} milliseconds / iteration, {} iterations per second\".format(\n",
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" caffe2_time, 1000 / caffe2_time))"
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]
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}
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],

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