.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_tutorials/resnet50_fusion.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_tutorials_resnet50_fusion.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_tutorials_resnet50_fusion.py:


ResNet50 deployment with TensorRT Patterns
==========================================

This quickstart shows how to prepare a PyTorch ResNet50 model for
deployment with ``embedl_deploy``.

The core idea is simple:

1. A ``transform`` applies a list of graph rewrite patterns.
2. A ``plan`` lets you inspect and edit every match before applying it.

In the current public release, the packaged backend is **TensorRT**.
The core API is backend-agnostic, and additional backends may be added
over time.

For TensorRT, the pattern library is split into three buckets:

- **Conversions**: structural rewrites run first to normalize graphs.
- **Fusions**: combine layer sequences into fused modules.
- **Quantized patterns**: quantization-focused rewrites (currently empty
  in this release, but included in the API).

.. note::

   This tutorial uses random weights for illustration.  In practice you
   would start from a pre-trained checkpoint.

.. GENERATED FROM PYTHON SOURCE LINES 31-33

.. code-block:: Python
   :dedent: 1










.. GENERATED FROM PYTHON SOURCE LINES 37-41

Setup
-----

We start by loading a standard ``torchvision`` model in eval mode.

.. GENERATED FROM PYTHON SOURCE LINES 41-51

.. code-block:: Python


    import torch
    from torchvision.models import resnet50

    model = resnet50(weights=None).eval()
    example_input = torch.randn(1, 3, 224, 224)

    print(f"Model: {type(model).__name__}")
    print(f"Parameters: {sum(p.numel() for p in model.parameters()):,}")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Model: ResNet
    Parameters: 25,557,032




.. GENERATED FROM PYTHON SOURCE LINES 52-57

One-shot transformation with ``transform()``
----------------------------------------------

The simplest way to prepare a model is to call
:func:`~embedl_deploy.transform` with a pattern list:

.. GENERATED FROM PYTHON SOURCE LINES 57-83

.. code-block:: Python


    from embedl_deploy import transform
    from embedl_deploy.tensorrt import (
        TENSORRT_CONVERSION_PATTERNS,
        TENSORRT_FUSION_PATTERNS,
        TENSORRT_PATTERNS,
        TENSORRT_QUANTIZED_PATTERNS,
    )

    print("\nTensorRT pattern groups:")
    print(f"  conversions: {len(TENSORRT_CONVERSION_PATTERNS)}")
    print(f"  fusions:     {len(TENSORRT_FUSION_PATTERNS)}")
    print(f"  quantized:   {len(TENSORRT_QUANTIZED_PATTERNS)}")
    print(f"  total:       {len(TENSORRT_PATTERNS)}")

    if TENSORRT_QUANTIZED_PATTERNS:
        print("Quantized patterns in this build:")
        for pat in TENSORRT_QUANTIZED_PATTERNS:
            print(f"  - {type(pat).__name__}")
    else:
        print(
            "Quantized patterns are exposed in the API but empty in this release."
        )

    deployed = transform(model, patterns=TENSORRT_PATTERNS).model





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    TensorRT pattern groups:
      conversions: 7
      fusions:     11
      quantized:   4
      total:       18
    Quantized patterns in this build:
      - InsertQuantStubsPattern
      - PropagateQuantStubPattern
      - DeduplicateQuantStubsPattern
      - SurroundWithQuantStubsPattern
    Skipping <built-in method flatten of type object at 0x78884ebd6c40>: missing shape metadata on input node
    Skipping avgpool: missing shape metadata on input node




.. GENERATED FROM PYTHON SOURCE LINES 84-89

Inspect the fused modules
~~~~~~~~~~~~~~~~~~~~~~~~~~

The transformed model contains fused ``nn.Module`` subclasses instead
of the original separate ``Conv``, ``BatchNorm``, and ``ReLU`` layers.

.. GENERATED FROM PYTHON SOURCE LINES 89-125

.. code-block:: Python


    from collections import Counter

    from embedl_deploy.tensorrt.modules import (
        FusedAdaptiveAvgPool2d,
        FusedConvBN,
        FusedConvBNAct,
        FusedConvBNActMaxPool,
        FusedConvBNAddAct,
    )

    FUSED_MODULE_TYPES = (
        FusedConvBN,
        FusedConvBNAddAct,
        FusedConvBNAct,
        FusedConvBNActMaxPool,
        FusedAdaptiveAvgPool2d,
    )

    fused_counts = Counter(
        type(m).__name__
        for m in deployed.modules()
        if isinstance(m, FUSED_MODULE_TYPES)
    )

    print("Fused modules in the transformed model:\n")
    for name, count in sorted(fused_counts.items()):
        print(f"  {name:<25s} {count}")
    print(f"  {'TOTAL':<25s} {sum(fused_counts.values())}")

    # In ResNet50, common fusions include:
    # - Stem block: ``Conv`` + ``BatchNorm`` + ``ReLU`` + ``MaxPool``
    # - Main path: ``Conv`` + ``BatchNorm`` or ``Conv`` + ``BatchNorm`` + ``ReLU``
    # - Residual blocks: ``Conv`` + ``BatchNorm`` + ``Add`` + ``ReLU``
    # - Tail rewrite: ``AdaptiveAvgPool`` handling before classifier export





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Fused modules in the transformed model:

      FusedAdaptiveAvgPool2d    1
      FusedConvBN               4
      FusedConvBNAct            32
      FusedConvBNActMaxPool     1
      FusedConvBNAddAct         16
      TOTAL                     54




.. GENERATED FROM PYTHON SOURCE LINES 126-131

Verify numerical equivalence
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The fused model must produce bit-for-bit identical outputs (no weight
folding has happened yet — that is left to the TensorRT compiler).

.. GENERATED FROM PYTHON SOURCE LINES 131-141

.. code-block:: Python


    with torch.no_grad():
        y_original = model(example_input)
        y_deployed = deployed(example_input)

    max_diff = (y_original - y_deployed).abs().max().item()
    print(f"Max output difference: {max_diff:.2e}")
    assert max_diff < 1e-5, f"Numerical mismatch: {max_diff}"
    print("✓ Fused model is numerically equivalent to the original.")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Max output difference: 0.00e+00
    ✓ Fused model is numerically equivalent to the original.




.. GENERATED FROM PYTHON SOURCE LINES 142-151

Plan-based workflow
--------------------

For full control, use the two-step workflow:
:func:`~embedl_deploy.get_transformation_plan` to discover matches,
then :func:`~embedl_deploy.apply_transformation_plan` to apply them.

The plan is **editable** — you can toggle ``match.apply = False``
to skip specific matches before applying.

.. GENERATED FROM PYTHON SOURCE LINES 151-162

.. code-block:: Python


    from embedl_deploy import apply_transformation_plan, get_transformation_plan

    graph_module = torch.fx.symbolic_trace(model)
    plan = get_transformation_plan(graph_module, patterns=TENSORRT_PATTERNS)

    print(f"\nDiscovered {sum(len(v) for v in plan.matches.values())} matches:\n")
    for node_name, patterns in plan.matches.items():
        for pat_name, match in patterns.items():
            print(f"  {node_name}: {pat_name} (apply={match.apply})")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Skipping <built-in method flatten of type object at 0x78884ebd6c40>: missing shape metadata on input node
    Skipping avgpool: missing shape metadata on input node

    Discovered 158 matches:

      maxpool: StemConvBNActMaxPoolPattern (apply=True)
      layer4_2_relu_2: ConvBNAddActPattern (apply=True)
      layer4_1_relu_2: ConvBNAddActPattern (apply=True)
      layer4_0_relu_2: ConvBNAddActPattern (apply=True)
      layer3_5_relu_2: ConvBNAddActPattern (apply=True)
      layer3_4_relu_2: ConvBNAddActPattern (apply=True)
      layer3_3_relu_2: ConvBNAddActPattern (apply=True)
      layer3_2_relu_2: ConvBNAddActPattern (apply=True)
      layer3_1_relu_2: ConvBNAddActPattern (apply=True)
      layer3_0_relu_2: ConvBNAddActPattern (apply=True)
      layer2_3_relu_2: ConvBNAddActPattern (apply=True)
      layer2_2_relu_2: ConvBNAddActPattern (apply=True)
      layer2_1_relu_2: ConvBNAddActPattern (apply=True)
      layer2_0_relu_2: ConvBNAddActPattern (apply=True)
      layer1_2_relu_2: ConvBNAddActPattern (apply=True)
      layer1_1_relu_2: ConvBNAddActPattern (apply=True)
      layer1_0_relu_2: ConvBNAddActPattern (apply=True)
      layer4_2_relu_1: ConvBNActPattern (apply=True)
      layer4_2_relu: ConvBNActPattern (apply=True)
      layer4_1_relu_1: ConvBNActPattern (apply=True)
      layer4_1_relu: ConvBNActPattern (apply=True)
      layer4_0_relu_1: ConvBNActPattern (apply=True)
      layer4_0_relu: ConvBNActPattern (apply=True)
      layer3_5_relu_1: ConvBNActPattern (apply=True)
      layer3_5_relu: ConvBNActPattern (apply=True)
      layer3_4_relu_1: ConvBNActPattern (apply=True)
      layer3_4_relu: ConvBNActPattern (apply=True)
      layer3_3_relu_1: ConvBNActPattern (apply=True)
      layer3_3_relu: ConvBNActPattern (apply=True)
      layer3_2_relu_1: ConvBNActPattern (apply=True)
      layer3_2_relu: ConvBNActPattern (apply=True)
      layer3_1_relu_1: ConvBNActPattern (apply=True)
      layer3_1_relu: ConvBNActPattern (apply=True)
      layer3_0_relu_1: ConvBNActPattern (apply=True)
      layer3_0_relu: ConvBNActPattern (apply=True)
      layer2_3_relu_1: ConvBNActPattern (apply=True)
      layer2_3_relu: ConvBNActPattern (apply=True)
      layer2_2_relu_1: ConvBNActPattern (apply=True)
      layer2_2_relu: ConvBNActPattern (apply=True)
      layer2_1_relu_1: ConvBNActPattern (apply=True)
      layer2_1_relu: ConvBNActPattern (apply=True)
      layer2_0_relu_1: ConvBNActPattern (apply=True)
      layer2_0_relu: ConvBNActPattern (apply=True)
      layer1_2_relu_1: ConvBNActPattern (apply=True)
      layer1_2_relu: ConvBNActPattern (apply=True)
      layer1_1_relu_1: ConvBNActPattern (apply=True)
      layer1_1_relu: ConvBNActPattern (apply=True)
      layer1_0_relu_1: ConvBNActPattern (apply=True)
      layer1_0_relu: ConvBNActPattern (apply=True)
      relu: ConvBNActPattern (apply=False)
      layer4_2_bn3: ConvBNPattern (apply=False)
      layer4_2_conv3: ConvBNPattern (apply=False)
      layer4_2_bn2: ConvBNPattern (apply=False)
      layer4_2_conv2: ConvBNPattern (apply=False)
      layer4_2_bn1: ConvBNPattern (apply=False)
      layer4_2_conv1: ConvBNPattern (apply=False)
      layer4_1_bn3: ConvBNPattern (apply=False)
      layer4_1_conv3: ConvBNPattern (apply=False)
      layer4_1_bn2: ConvBNPattern (apply=False)
      layer4_1_conv2: ConvBNPattern (apply=False)
      layer4_1_bn1: ConvBNPattern (apply=False)
      layer4_1_conv1: ConvBNPattern (apply=False)
      layer4_0_downsample_1: ConvBNPattern (apply=True)
      layer4_0_downsample_0: ConvBNPattern (apply=False)
      layer4_0_bn3: ConvBNPattern (apply=False)
      layer4_0_conv3: ConvBNPattern (apply=False)
      layer4_0_bn2: ConvBNPattern (apply=False)
      layer4_0_conv2: ConvBNPattern (apply=False)
      layer4_0_bn1: ConvBNPattern (apply=False)
      layer4_0_conv1: ConvBNPattern (apply=False)
      layer3_5_bn3: ConvBNPattern (apply=False)
      layer3_5_conv3: ConvBNPattern (apply=False)
      layer3_5_bn2: ConvBNPattern (apply=False)
      layer3_5_conv2: ConvBNPattern (apply=False)
      layer3_5_bn1: ConvBNPattern (apply=False)
      layer3_5_conv1: ConvBNPattern (apply=False)
      layer3_4_bn3: ConvBNPattern (apply=False)
      layer3_4_conv3: ConvBNPattern (apply=False)
      layer3_4_bn2: ConvBNPattern (apply=False)
      layer3_4_conv2: ConvBNPattern (apply=False)
      layer3_4_bn1: ConvBNPattern (apply=False)
      layer3_4_conv1: ConvBNPattern (apply=False)
      layer3_3_bn3: ConvBNPattern (apply=False)
      layer3_3_conv3: ConvBNPattern (apply=False)
      layer3_3_bn2: ConvBNPattern (apply=False)
      layer3_3_conv2: ConvBNPattern (apply=False)
      layer3_3_bn1: ConvBNPattern (apply=False)
      layer3_3_conv1: ConvBNPattern (apply=False)
      layer3_2_bn3: ConvBNPattern (apply=False)
      layer3_2_conv3: ConvBNPattern (apply=False)
      layer3_2_bn2: ConvBNPattern (apply=False)
      layer3_2_conv2: ConvBNPattern (apply=False)
      layer3_2_bn1: ConvBNPattern (apply=False)
      layer3_2_conv1: ConvBNPattern (apply=False)
      layer3_1_bn3: ConvBNPattern (apply=False)
      layer3_1_conv3: ConvBNPattern (apply=False)
      layer3_1_bn2: ConvBNPattern (apply=False)
      layer3_1_conv2: ConvBNPattern (apply=False)
      layer3_1_bn1: ConvBNPattern (apply=False)
      layer3_1_conv1: ConvBNPattern (apply=False)
      layer3_0_downsample_1: ConvBNPattern (apply=True)
      layer3_0_downsample_0: ConvBNPattern (apply=False)
      layer3_0_bn3: ConvBNPattern (apply=False)
      layer3_0_conv3: ConvBNPattern (apply=False)
      layer3_0_bn2: ConvBNPattern (apply=False)
      layer3_0_conv2: ConvBNPattern (apply=False)
      layer3_0_bn1: ConvBNPattern (apply=False)
      layer3_0_conv1: ConvBNPattern (apply=False)
      layer2_3_bn3: ConvBNPattern (apply=False)
      layer2_3_conv3: ConvBNPattern (apply=False)
      layer2_3_bn2: ConvBNPattern (apply=False)
      layer2_3_conv2: ConvBNPattern (apply=False)
      layer2_3_bn1: ConvBNPattern (apply=False)
      layer2_3_conv1: ConvBNPattern (apply=False)
      layer2_2_bn3: ConvBNPattern (apply=False)
      layer2_2_conv3: ConvBNPattern (apply=False)
      layer2_2_bn2: ConvBNPattern (apply=False)
      layer2_2_conv2: ConvBNPattern (apply=False)
      layer2_2_bn1: ConvBNPattern (apply=False)
      layer2_2_conv1: ConvBNPattern (apply=False)
      layer2_1_bn3: ConvBNPattern (apply=False)
      layer2_1_conv3: ConvBNPattern (apply=False)
      layer2_1_bn2: ConvBNPattern (apply=False)
      layer2_1_conv2: ConvBNPattern (apply=False)
      layer2_1_bn1: ConvBNPattern (apply=False)
      layer2_1_conv1: ConvBNPattern (apply=False)
      layer2_0_downsample_1: ConvBNPattern (apply=True)
      layer2_0_downsample_0: ConvBNPattern (apply=False)
      layer2_0_bn3: ConvBNPattern (apply=False)
      layer2_0_conv3: ConvBNPattern (apply=False)
      layer2_0_bn2: ConvBNPattern (apply=False)
      layer2_0_conv2: ConvBNPattern (apply=False)
      layer2_0_bn1: ConvBNPattern (apply=False)
      layer2_0_conv1: ConvBNPattern (apply=False)
      layer1_2_bn3: ConvBNPattern (apply=False)
      layer1_2_conv3: ConvBNPattern (apply=False)
      layer1_2_bn2: ConvBNPattern (apply=False)
      layer1_2_conv2: ConvBNPattern (apply=False)
      layer1_2_bn1: ConvBNPattern (apply=False)
      layer1_2_conv1: ConvBNPattern (apply=False)
      layer1_1_bn3: ConvBNPattern (apply=False)
      layer1_1_conv3: ConvBNPattern (apply=False)
      layer1_1_bn2: ConvBNPattern (apply=False)
      layer1_1_conv2: ConvBNPattern (apply=False)
      layer1_1_bn1: ConvBNPattern (apply=False)
      layer1_1_conv1: ConvBNPattern (apply=False)
      layer1_0_downsample_1: ConvBNPattern (apply=True)
      layer1_0_downsample_0: ConvBNPattern (apply=False)
      layer1_0_bn3: ConvBNPattern (apply=False)
      layer1_0_conv3: ConvBNPattern (apply=False)
      layer1_0_bn2: ConvBNPattern (apply=False)
      layer1_0_conv2: ConvBNPattern (apply=False)
      layer1_0_bn1: ConvBNPattern (apply=False)
      layer1_0_conv1: ConvBNPattern (apply=False)
      bn1: ConvBNPattern (apply=False)
      conv1: ConvBNPattern (apply=False)
      fc: LinearPattern (apply=True)
      avgpool: AdaptiveAvgPoolPattern (apply=True)




.. GENERATED FROM PYTHON SOURCE LINES 163-164

Apply the plan without changes:

.. GENERATED FROM PYTHON SOURCE LINES 164-170

.. code-block:: Python


    result = apply_transformation_plan(plan)

    print(f"Applied: {result.report['applied_count']}")
    print(f"Skipped: {result.report['skipped_count']}")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Applied: 55
    Skipped: 103




.. GENERATED FROM PYTHON SOURCE LINES 171-191

.. note::

   A non-zero ``skipped_count`` is expected and intentional.

   All patterns are matched against the graph independently first.
   The plan then iterates through the results **in pattern-list order**,
   building a set of *consumed* nodes.  When a match's nodes overlap with
   nodes already claimed by an earlier match, it is marked
   ``apply=False`` and counted as skipped.

   For ResNet50 with ``TENSORRT_PATTERNS`` this means:
   ``StemConvBNActMaxPoolPattern`` (listed first) claims the
   ``Conv→BN→ReLU→MaxPool`` stem nodes.  The shorter
   ``ConvBNActPattern``, ``ConvBNPattern``, etc. also match sub-chains
   within that same stem, so they are skipped because their nodes were
   already consumed.

   This is how pattern priority works: supply the **longest / most
   specific** patterns first so they take precedence over shorter,
   more general ones when sub-graphs overlap.

.. GENERATED FROM PYTHON SOURCE LINES 193-197

Edit the plan before applying
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Disable a specific match by setting ``apply = False``:

.. GENERATED FROM PYTHON SOURCE LINES 197-216

.. code-block:: Python


    plan2 = get_transformation_plan(graph_module, patterns=TENSORRT_PATTERNS)

    # Disable fusion of the stem
    plan2.matches["maxpool"]["StemConvBNActMaxPoolPattern"].apply = False

    result2 = apply_transformation_plan(plan2)

    fused2_counts = Counter(
        type(m).__name__
        for m in result2.model.modules()
        if isinstance(m, FUSED_MODULE_TYPES)
    )
    print(
        f"Fused modules (stem skipped): {sum(fused2_counts.values())} "
        f"(was {sum(fused_counts.values())})"
    )
    print(f"Skipped: {result2.report['skipped_count']}")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Skipping <built-in method flatten of type object at 0x78884ebd6c40>: missing shape metadata on input node
    Skipping avgpool: missing shape metadata on input node
    Fused modules (stem skipped): 53 (was 54)
    Skipped: 104




.. GENERATED FROM PYTHON SOURCE LINES 217-221

Toggle only conversion or fusion patterns
------------------------------------------

You can scope the transformation by choosing pattern groups directly.

.. GENERATED FROM PYTHON SOURCE LINES 221-238

.. code-block:: Python


    only_fusions = transform(model, patterns=TENSORRT_FUSION_PATTERNS).model
    only_conversions = transform(
        model, patterns=TENSORRT_CONVERSION_PATTERNS
    ).model

    only_fusions_count = sum(
        1 for m in only_fusions.modules() if isinstance(m, FUSED_MODULE_TYPES)
    )
    only_conversions_count = sum(
        1 for m in only_conversions.modules() if isinstance(m, FUSED_MODULE_TYPES)
    )

    print("\nPattern group experiments:")
    print(f"  only fusions -> fused module count: {only_fusions_count}")
    print(f"  only conversions -> fused module count: {only_conversions_count}")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Skipping <built-in method flatten of type object at 0x78884ebd6c40>: missing shape metadata on input node
    Skipping avgpool: missing shape metadata on input node

    Pattern group experiments:
      only fusions -> fused module count: 54
      only conversions -> fused module count: 0




.. GENERATED FROM PYTHON SOURCE LINES 239-262

Selective transformation
-------------------------

Because every transformation is a ``Pattern`` object, you have full
control.  For example, to fuse only Conv→BN→ReLU chains:

.. code-block:: python

   from embedl_deploy.tensorrt import ConvBNActPattern

   selective = transform(model, patterns=[ConvBNActPattern()])

Or use the plan to cherry-pick:

.. code-block:: python

   plan = get_transformation_plan(model, patterns=TENSORRT_PATTERNS)
   # Disable everything except ConvBNAct
   for pats in plan.matches.values():
       for pat_name, match in pats.items():
           if pat_name != "ConvBNActPattern":
               match.apply = False
   result = apply_transformation_plan(plan)

.. GENERATED FROM PYTHON SOURCE LINES 264-268

Quantization patterns: inspect and toggle
------------------------------------------

Quantization rewrites follow the exact same plan/edit/apply workflow.

.. GENERATED FROM PYTHON SOURCE LINES 268-292

.. code-block:: Python


    quant_plan = get_transformation_plan(
        result.model, patterns=TENSORRT_QUANTIZED_PATTERNS
    )
    quant_match_count = sum(len(v) for v in quant_plan.matches.values())
    print(f"\nQuantization-plan matches: {quant_match_count}")

    if quant_match_count:
        # Example: disable all quantization matches before applying.
        for pats in quant_plan.matches.values():
            for match in pats.values():
                match.apply = False

        quant_result = apply_transformation_plan(quant_plan)
        print(
            "Applied quantized patterns after disabling all matches: "
            f"{quant_result.report['applied_count']}"
        )
    else:
        print(
            "No quantization matches in this release. "
            "When quantized patterns are added, you can toggle them the same way."
        )





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    Quantization-plan matches: 0
    No quantization matches in this release. When quantized patterns are added, you can toggle them the same way.




.. GENERATED FROM PYTHON SOURCE LINES 293-321

Visualising transforms
----------------------

The images below show the layer mapping before and after TensorRT
compilation. `Embedl Visualizer <https://hub.embedl.com/visualizer>`_ renders
PyTorch graphs, ONNX models, and hardware-compiled artifacts (e.g., TensorRT
engines) side-by-side for comparison and debugging. It is available online
for public use on `Embedl Hub <https://hub.embedl.com>`_ and locally for
enterprise solutions.

.. raw:: html

   <div style="display: flex; gap: 1rem; align-items: flex-start;">
     <figure style="flex: 1; text-align: center; margin: 0;">
       <figcaption><strong>Design in PyTorch</strong></figcaption>
       <img src="https://6631582.fs1.hubspotusercontent-na1.net/hubfs/6631582/deploy_mapping.png"
            alt="Layer mapping — PyTorch to ONNX"
            style="width: 100%;">
     </figure>
     <figure style="flex: 1; text-align: center; margin: 0;">
       <figcaption><strong>Deploy on edge</strong></figcaption>
       <img
         src="https://6631582.fs1.hubspotusercontent-na1.net/hubfs/6631582/deploy_mapping_trt.png"
         alt="Layer mapping — ONNX to TensorRT"
         style="width: 100%;">
     </figure>
   </div>


.. GENERATED FROM PYTHON SOURCE LINES 323-338

Next steps
----------

After these graph rewrites, the model is ready for:

- **ONNX export** — ``torch.onnx.export(deployed, example_input, "resnet50.onnx")``
- **Quantization** — enable quantized patterns as they become available
- **TensorRT compilation** — compile the ONNX model to a TensorRT engine

.. code-block:: bash

   /usr/src/tensorrt/bin/trtexec --onnx=resnet50_fused.onnx \
       --exportLayerInfo=layer_info.json \
       --profilingVerbosity=detailed \
       --exportProfile=profile.json


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 2.439 seconds)


.. _sphx_glr_download_auto_tutorials_resnet50_fusion.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: resnet50_fusion.ipynb <resnet50_fusion.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: resnet50_fusion.py <resnet50_fusion.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: resnet50_fusion.zip <resnet50_fusion.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_