Compile PyTorch Models

Author: Alex Wong

This article is an introductory tutorial to deploy PyTorch models with Relay.

For us to begin, PyTorch should be installed. TorchVision is also required so we can use the model zoo. A quick solution is to install via pip:

pip install torch
pip install torchvision

or please refer to official site

PyTorch versions should be backwards compatible but should be used with the proper TorchVision version.

Currently, TVM supports PyTorch 1.7 and 1.4. Other versions may be unstable.

import tvm
from tvm import relay

import numpy as np

from import download_testdata

# PyTorch imports
import torch
import torchvision

Load a pretrained PyTorch model

model_name = "resnet18"
model = getattr(torchvision.models, model_name)(pretrained=True)
model = model.eval()

# We grab the TorchScripted model via tracing
input_shape = [1, 3, 224, 224]
input_data = torch.randn(input_shape)
scripted_model = torch.jit.trace(model, input_data).eval()
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/ UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/ UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
Downloading: "" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth

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Load a test image

Classic cat example!

from PIL import Image

img_url = ""
img_path = download_testdata(img_url, "cat.png", module="data")
img =, 224))

# Preprocess the image and convert to tensor
from torchvision import transforms

my_preprocess = transforms.Compose(
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
img = my_preprocess(img)
img = np.expand_dims(img, 0)

Import the graph to Relay

Convert PyTorch graph to Relay graph. The input name can be arbitrary.

Relay Build

Compile the graph to llvm target with given input specification.

target ="llvm", host="llvm")
dev = tvm.cpu(0)
with tvm.transform.PassContext(opt_level=3):
    lib =, target=target, params=params)

Execute the portable graph on TVM

Now we can try deploying the compiled model on target.

from tvm.contrib import graph_executor

dtype = "float32"
m = graph_executor.GraphModule(lib["default"](dev))
# Set inputs
m.set_input(input_name, tvm.nd.array(img.astype(dtype)))
# Execute
# Get outputs
tvm_output = m.get_output(0)

Look up synset name

Look up prediction top 1 index in 1000 class synset.

synset_url = "".join(
synset_name = "imagenet_synsets.txt"
synset_path = download_testdata(synset_url, synset_name, module="data")
with open(synset_path) as f:
    synsets = f.readlines()

synsets = [x.strip() for x in synsets]
splits = [line.split(" ") for line in synsets]
key_to_classname = {spl[0]: " ".join(spl[1:]) for spl in splits}

class_url = "".join(
class_name = "imagenet_classes.txt"
class_path = download_testdata(class_url, class_name, module="data")
with open(class_path) as f:
    class_id_to_key = f.readlines()

class_id_to_key = [x.strip() for x in class_id_to_key]

# Get top-1 result for TVM
top1_tvm = np.argmax(tvm_output.numpy()[0])
tvm_class_key = class_id_to_key[top1_tvm]

# Convert input to PyTorch variable and get PyTorch result for comparison
with torch.no_grad():
    torch_img = torch.from_numpy(img)
    output = model(torch_img)

    # Get top-1 result for PyTorch
    top1_torch = np.argmax(output.numpy())
    torch_class_key = class_id_to_key[top1_torch]

print("Relay top-1 id: {}, class name: {}".format(top1_tvm, key_to_classname[tvm_class_key]))
print("Torch top-1 id: {}, class name: {}".format(top1_torch, key_to_classname[torch_class_key]))
Relay top-1 id: 281, class name: tabby, tabby cat
Torch top-1 id: 281, class name: tabby, tabby cat

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