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<div class="nbinput nblast docutils container">
<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.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
</pre></div>
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<p><img alt="0651fd225e2f45ecadc9b43b60a4e5ff" src="https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_ssd_notebook.png" /></p>
<section id="Object-Detection-with-Torch-TensorRT-(SSD)">
<h1>Object Detection with Torch-TensorRT (SSD)<a class="headerlink" href="#Object-Detection-with-Torch-TensorRT-(SSD)" title="Permalink to this headline">¶</a></h1>
<hr class="docutils" />
<section id="Overview">
<h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this headline">¶</a></h2>
<p>In the practice of developing machine learning models, there are few tools as approachable as PyTorch for developing and experimenting in designing machine learning models. The power of PyTorch comes from its deep integration into Python, its flexibility and its approach to automatic differentiation and execution (eager execution). However, when moving from research into production, the requirements change and we may no longer want that deep Python integration and we want optimization to get the
best performance we can on our deployment platform. In PyTorch 1.0, TorchScript was introduced as a method to separate your PyTorch model from Python, make it portable and optimizable. TorchScript uses PyTorch’s JIT compiler to transform your normal PyTorch code which gets interpreted by the Python interpreter to an intermediate representation (IR) which can have optimizations run on it and at runtime can get interpreted by the PyTorch JIT interpreter. For PyTorch this has opened up a whole new
world of possibilities, including deployment in other languages like C++. It also introduces a structured graph based format that we can use to do down to the kernel level optimization of models for inference.</p>
<p>When deploying on NVIDIA GPUs TensorRT, NVIDIA’s Deep Learning Optimization SDK and Runtime is able to take models from any major framework and specifically tune them to perform better on specific target hardware in the NVIDIA family be it an A100, TITAN V, Jetson Xavier or NVIDIA’s Deep Learning Accelerator. TensorRT performs a couple sets of optimizations to achieve this. TensorRT fuses layers and tensors in the model graph, it then uses a large kernel library to select implementations that
perform best on the target GPU. TensorRT also has strong support for reduced operating precision execution which allows users to leverage the Tensor Cores on Volta and newer GPUs as well as reducing memory and computation footprints on device.</p>
<p>Torch-TensorRT is a compiler that uses TensorRT to optimize TorchScript code, compiling standard TorchScript modules into ones that internally run with TensorRT optimizations. This enables you to continue to remain in the PyTorch ecosystem, using all the great features PyTorch has such as module composability, its flexible tensor implementation, data loaders and more. Torch-TensorRT is available to use with both PyTorch and LibTorch.</p>
<section id="Learning-objectives">
<h3>Learning objectives<a class="headerlink" href="#Learning-objectives" title="Permalink to this headline">¶</a></h3>
<p>This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a pretrained SSD network, and running it to test the speedup obtained.</p>
</section>
</section>
<section id="Contents">
<h2>Contents<a class="headerlink" href="#Contents" title="Permalink to this headline">¶</a></h2>
<ol class="arabic simple">
<li><p><a class="reference external" href="#1">Requirements</a></p></li>
<li><p><a class="reference external" href="#2">SSD Overview</a></p></li>
<li><p><a class="reference external" href="#3">Creating TorchScript modules</a></p></li>
<li><p><a class="reference external" href="#4">Compiling with Torch-TensorRT</a></p></li>
<li><p><a class="reference external" href="#5">Running Inference</a></p></li>
<li><p><a class="reference external" href="#6">Measuring Speedup</a></p></li>
<li><p><a class="reference external" href="#7">Conclusion</a></p></li>
</ol>
<hr class="docutils" />
<p>## 1. Requirements</p>
<p>Follow the steps in <code class="docutils literal notranslate"><span class="pre">notebooks/README</span></code> to prepare a Docker container, within which you can run this demo notebook.</p>
<p>In addition to that, run the following cell to obtain additional libraries specific to this demo.</p>
<div class="nbinput docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
</pre></div>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>!pip install scikit-image==0.19.1
!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org
</pre></div>
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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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<p>## 2. SSD</p>
<section id="Single-Shot-MultiBox-Detector-model-for-object-detection">
<h3>Single Shot MultiBox Detector model for object detection<a class="headerlink" href="#Single-Shot-MultiBox-Detector-model-for-object-detection" title="Permalink to this headline">¶</a></h3>
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<tr class="row-odd"><th class="head"><p>_</p></th>
<th class="head"><p>_</p></th>
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<tr class="row-even"><td><p><img alt="alt" src="https://pytorch.org/assets/images/ssd_diagram.png" /></p></td>
<td><p><img alt="image1" src="https://pytorch.org/assets/images/ssd.png" /></p></td>
</tr>
</tbody>
</table>
<p>PyTorch has a model repository called the PyTorch Hub, which is a source for high quality implementations of common models. We can get our SSD model pretrained on <a class="reference external" href="https://cocodataset.org/#home">COCO</a> from there.</p>
</section>
<section id="Model-Description">
<h3>Model Description<a class="headerlink" href="#Model-Description" title="Permalink to this headline">¶</a></h3>
<p>This SSD300 model is based on the <a class="reference external" href="https://arxiv.org/abs/1512.02325">SSD: Single Shot MultiBox Detector</a> paper, which describes SSD as “a method for detecting objects in images using a single deep neural network”. The input size is fixed to 300x300.</p>
<p>The main difference between this model and the one described in the paper is in the backbone. Specifically, the VGG model is obsolete and is replaced by the ResNet-50 model.</p>
<p>From the <a class="reference external" href="https://arxiv.org/abs/1611.10012">Speed/accuracy trade-offs for modern convolutional object detectors</a> paper, the following enhancements were made to the backbone: * The conv5_x, avgpool, fc and softmax layers were removed from the original classification model. * All strides in conv4_x are set to 1x1.</p>
<p>The backbone is followed by 5 additional convolutional layers. In addition to the convolutional layers, we attached 6 detection heads: * The first detection head is attached to the last conv4_x layer. * The other five detection heads are attached to the corresponding 5 additional layers.</p>
<p>Detector heads are similar to the ones referenced in the paper, however, they are enhanced by additional BatchNorm layers after each convolution.</p>
<p>More information about this SSD model is available at Nvidia’s “DeepLearningExamples” Github <a class="reference external" href="https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Detection/SSD">here</a>.</p>
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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
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># List of available models in PyTorch Hub from Nvidia/DeepLearningExamples
torch.hub.list('NVIDIA/DeepLearningExamples:torchhub')
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Downloading: "https://github.com/NVIDIA/DeepLearningExamples/archive/torchhub.zip" to /root/.cache/torch/hub/torchhub.zip
/root/.cache/torch/hub/NVIDIA_DeepLearningExamples_torchhub/PyTorch/Classification/ConvNets/image_classification/models/efficientnet.py:17: UserWarning: pytorch_quantization module not found, quantization will not be available
warnings.warn(
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['nvidia_convnets_processing_utils',
'nvidia_efficientnet',
'nvidia_efficientnet_b0',
'nvidia_efficientnet_b4',
'nvidia_efficientnet_widese_b0',
'nvidia_efficientnet_widese_b4',
'nvidia_resneXt',
'nvidia_resnet50',
'nvidia_resnext101_32x4d',
'nvidia_se_resnext101_32x4d',
'nvidia_ssd',
'nvidia_ssd_processing_utils',
'nvidia_tacotron2',
'nvidia_tts_utils',
'nvidia_waveglow']
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precision = 'fp32'
ssd300 = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_ssd', model_math=precision);
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Using cache found in /root/.cache/torch/hub/NVIDIA_DeepLearningExamples_torchhub
Downloading: "https://download.pytorch.org/models/resnet50-0676ba61.pth" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth
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Downloading checkpoint from https://api.ngc.nvidia.com/v2/models/nvidia/ssd_pyt_ckpt_amp/versions/20.06.0/files/nvidia_ssdpyt_amp_200703.pt
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<p>Setting <code class="docutils literal notranslate"><span class="pre">precision="fp16"</span></code> will load a checkpoint trained with mixed precision into architecture enabling execution on Tensor Cores. Handling mixed precision data requires the Apex library.</p>
</section>
<section id="Sample-Inference">
<h3>Sample Inference<a class="headerlink" href="#Sample-Inference" title="Permalink to this headline">¶</a></h3>
<p>We can now run inference on the model. This is demonstrated below using sample images from the COCO 2017 Validation set.</p>
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uris = [
'http://images.cocodataset.org/val2017/000000397133.jpg',
'http://images.cocodataset.org/val2017/000000037777.jpg',
'http://images.cocodataset.org/val2017/000000252219.jpg'
]
# For convenient and comprehensive formatting of input and output of the model, load a set of utility methods.
utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_ssd_processing_utils')
# Format images to comply with the network input
inputs = [utils.prepare_input(uri) for uri in uris]
tensor = utils.prepare_tensor(inputs, False)
# The model was trained on COCO dataset, which we need to access in order to
# translate class IDs into object names.
classes_to_labels = utils.get_coco_object_dictionary()
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Downloading COCO annotations.
Downloading finished.
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model = ssd300.eval().to("cuda")
detections_batch = model(tensor)
# By default, raw output from SSD network per input image contains 8732 boxes with
# localization and class probability distribution.
# Let’s filter this output to only get reasonable detections (confidence>40%) in a more comprehensive format.
results_per_input = utils.decode_results(detections_batch)
best_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]
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<section id="Visualize-results">
<h3>Visualize results<a class="headerlink" href="#Visualize-results" title="Permalink to this headline">¶</a></h3>
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import matplotlib.patches as patches
# The utility plots the images and predicted bounding boxes (with confidence scores).
def plot_results(best_results):
for image_idx in range(len(best_results)):
fig, ax = plt.subplots(1)
# Show original, denormalized image...
image = inputs[image_idx] / 2 + 0.5
ax.imshow(image)
# ...with detections
bboxes, classes, confidences = best_results[image_idx]
for idx in range(len(bboxes)):
left, bot, right, top = bboxes[idx]
x, y, w, h = [val * 300 for val in [left, bot, right - left, top - bot]]
rect = patches.Rectangle((x, y), w, h, linewidth=1, edgecolor='r', facecolor='none')
ax.add_patch(rect)
ax.text(x, y, "{} {:.0f}%".format(classes_to_labels[classes[idx] - 1], confidences[idx]*100), bbox=dict(facecolor='white', alpha=0.5))
plt.show()
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plot_results(best_results_per_input)
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<section id="Benchmark-utility">
<h3>Benchmark utility<a class="headerlink" href="#Benchmark-utility" title="Permalink to this headline">¶</a></h3>