Compile PyTorch Models

Author: Alex Wong

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

For us to begin with, PyTorch should be installed. TorchVision is also required since we will be using it as our model zoo.

A quick solution is to install via pip

pip install torch==1.4.0
pip install torchvision==0.5.0

or please refer to official site https://pytorch.org/get-started/locally/

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

Currently, TVM supports PyTorch 1.4 and 1.3. Other versions may be unstable.

import tvm
from tvm import relay

import numpy as np

from tvm.contrib.download 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()

Load a test image

Classic cat example!

from PIL import Image
img_url = 'https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true'
img_path = download_testdata(img_url, 'cat.png', module='data')
img = Image.open(img_path).resize((224, 224))

# Preprocess the image and convert to tensor
from torchvision import transforms
my_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])
])
img = my_preprocess(img)
img = np.expand_dims(img, 0)

Out:

File /workspace/.tvm_test_data/data/cat.png exists, skip.

Import the graph to Relay

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

input_name = 'input0'
shape_list = [(input_name, img.shape)]
mod, params = relay.frontend.from_pytorch(scripted_model,
                                          shape_list)

Out:

ANTLR runtime and generated code versions disagree: 4.8!=4.7.2
ANTLR runtime and generated code versions disagree: 4.8!=4.7.2

Relay Build

Compile the graph to llvm target with given input specification.

target = 'llvm'
target_host = 'llvm'
ctx = tvm.cpu(0)
with tvm.transform.PassContext(opt_level=3):
    graph, lib, params = relay.build(mod,
                                     target=target,
                                     target_host=target_host,
                                     params=params)

Execute the portable graph on TVM

Now we can try deploying the compiled model on target.

from tvm.contrib import graph_runtime
dtype = 'float32'
m = graph_runtime.create(graph, lib, ctx)
# Set inputs
m.set_input(input_name, tvm.nd.array(img.astype(dtype)))
m.set_input(**params)
# Execute
m.run()
# 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(['https://raw.githubusercontent.com/Cadene/',
                      'pretrained-models.pytorch/master/data/',
                      'imagenet_synsets.txt'])
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(['https://raw.githubusercontent.com/Cadene/',
                      'pretrained-models.pytorch/master/data/',
                      'imagenet_classes.txt'])
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.asnumpy()[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]))

Out:

File /workspace/.tvm_test_data/data/imagenet_synsets.txt exists, skip.
File /workspace/.tvm_test_data/data/imagenet_classes.txt exists, skip.
Relay top-1 id: 281, class name: tabby, tabby cat
Torch top-1 id: 281, class name: tabby, tabby cat

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