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<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in C++</a></li>
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<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
</pre></div>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Copyright 2020 NVIDIA Corporation. All Rights Reserved.
#
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</pre></div>
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<section id="Torch-TensorRT---Using-Dynamic-Shapes">
<h1>Torch-TensorRT - Using Dynamic Shapes<a class="headerlink" href="#Torch-TensorRT---Using-Dynamic-Shapes" title="Permalink to this headline">¶</a></h1>
<p>Torch-TensorRT is a compiler for PyTorch/TorchScript, targeting NVIDIA GPUs via NVIDIA’s TensorRT Deep Learning Optimizer and Runtime. Unlike PyTorch’s Just-In-Time (JIT) compiler, Torch-TensorRT is an Ahead-of-Time (AOT) compiler, meaning that before you deploy your TorchScript code, you go through an explicit compile step to convert a standard TorchScript program into an module targeting a TensorRT engine. Torch-TensorRT operates as a PyTorch extention and compiles modules that integrate into
the JIT runtime seamlessly. After compilation using the optimized graph should feel no different than running a TorchScript module. You also have access to TensorRT’s suite of configurations at compile time, so you are able to specify operating precision (FP32/FP16/INT8) and other settings for your module.</p>
<p>We highly encorage users to use our NVIDIA’s <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch">PyTorch container</a> to run this notebook. It comes packaged with a host of NVIDIA libraries and optimizations to widely used third party libraries. This container is tested and updated on a monthly cadence!</p>
<p>This notebook has the following sections: 1. <a class="reference external" href="#1">TL;DR Explanation</a> 1. <a class="reference external" href="#2">Setting up the model</a> 1. <a class="reference external" href="#3">Working with Dynamic shapes in Torch TRT</a></p>
<p>torch_tensorrt.Input( min_shape=(1, 224, 224, 3), opt_shape=(1, 512, 512, 3), max_shape=(1, 1024, 1024, 3), dtype=torch.int32 format=torch.channel_last ) … ``` In this example, we are going to use a simple ResNet model to demonstrate the use of the API. We will be using different batch sizes in the example, but you can use the same method to alter any of the dimensions of the tensor.</p>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>!nvidia-smi
!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org
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Mon May 2 20:40:30 2022
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<span class="ansi-yellow-fg">WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv</span>
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<section id="Setting-up-the-model">
<h2>Setting up the model<a class="headerlink" href="#Setting-up-the-model" title="Permalink to this headline">¶</a></h2>
<p>In this section, we will: * Get sample data. * Download model from torch hub. * Build simple utility functions</p>
<section id="Getting-sample-data">
<h3>Getting sample data<a class="headerlink" href="#Getting-sample-data" title="Permalink to this headline">¶</a></h3>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>!mkdir -p ./data
!wget -O ./data/img0.JPG "https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&width=630"
!wget -O ./data/img1.JPG "https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg"
!wget -O ./data/img2.JPG "https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg"
!wget -O ./data/img3.JPG "https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg"
!wget -O ./data/imagenet_class_index.json "https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json"
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--2022-05-02 20:40:33-- https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&width=630
Resolving d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)... 18.65.227.37, 18.65.227.99, 18.65.227.223, ...
Connecting to d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)|18.65.227.37|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 24112 (24K) [image/jpeg]
Saving to: ‘./data/img0.JPG’
./data/img0.JPG 100%[===================>] 23.55K --.-KB/s in 0.005s
2022-05-02 20:40:33 (4.69 MB/s) - ‘./data/img0.JPG’ saved [24112/24112]
--2022-05-02 20:40:34-- https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg
Resolving www.hakaimagazine.com (www.hakaimagazine.com)... 164.92.73.117
Connecting to www.hakaimagazine.com (www.hakaimagazine.com)|164.92.73.117|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 452718 (442K) [image/jpeg]
Saving to: ‘./data/img1.JPG’
./data/img1.JPG 100%[===================>] 442.11K --.-KB/s in 0.02s
2022-05-02 20:40:34 (26.2 MB/s) - ‘./data/img1.JPG’ saved [452718/452718]
--2022-05-02 20:40:34-- https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg
Resolving www.artis.nl (www.artis.nl)... 94.75.225.20
Connecting to www.artis.nl (www.artis.nl)|94.75.225.20|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 361413 (353K) [image/jpeg]
Saving to: ‘./data/img2.JPG’
./data/img2.JPG 100%[===================>] 352.94K 608KB/s in 0.6s
2022-05-02 20:40:36 (608 KB/s) - ‘./data/img2.JPG’ saved [361413/361413]
--2022-05-02 20:40:37-- https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg
Resolving www.familyhandyman.com (www.familyhandyman.com)... 104.18.201.107, 104.18.202.107, 2606:4700::6812:c96b, ...
Connecting to www.familyhandyman.com (www.familyhandyman.com)|104.18.201.107|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 90994 (89K) [image/jpeg]
Saving to: ‘./data/img3.JPG’
./data/img3.JPG 100%[===================>] 88.86K --.-KB/s in 0.006s
2022-05-02 20:40:37 (15.4 MB/s) - ‘./data/img3.JPG’ saved [90994/90994]
--2022-05-02 20:40:37-- https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json
Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.33.238
Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.33.238|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 35363 (35K) [application/octet-stream]
Saving to: ‘./data/imagenet_class_index.json’
./data/imagenet_cla 100%[===================>] 34.53K --.-KB/s in 0.07s
2022-05-02 20:40:38 (489 KB/s) - ‘./data/imagenet_class_index.json’ saved [35363/35363]
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># visualizing the downloaded images
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt
import json
fig, axes = plt.subplots(nrows=2, ncols=2)
for i in range(4):
img_path = './data/img%d.JPG'%i
img = Image.open(img_path)
preprocess = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
input_tensor = preprocess(img)
plt.subplot(2,2,i+1)
plt.imshow(img)
plt.axis('off')
# loading labels
with open("./data/imagenet_class_index.json") as json_file:
d = json.load(json_file)
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</section>
<section id="Download-model-from-torch-hub.">
<h3>Download model from torch hub.<a class="headerlink" href="#Download-model-from-torch-hub." title="Permalink to this headline">¶</a></h3>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import torch
torch.hub._validate_not_a_forked_repo=lambda a,b,c: True
resnet50_model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet50', pretrained=True)
resnet50_model.eval()
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Using cache found in /root/.cache/torch/hub/pytorch_vision_v0.10.0
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ResNet(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer2): Sequential(
(0): Bottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer3): Sequential(
(0): Bottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)