torch-mlir/examples/torchscript_resnet18_e2e.py

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# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
# Also available under a BSD-style license. See LICENSE.
from PIL import Image
import requests
import torch
import torchvision.models as models
from torchvision import transforms
from torch_mlir.dialects.torch.importer.jit_ir import ClassAnnotator, ModuleBuilder
from torch_mlir.passmanager import PassManager
from torch_mlir_e2e_test.linalg_on_tensors_backends import refbackend
mb = ModuleBuilder()
def load_and_preprocess_image(url: str):
headers = {
'User-Agent':
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'
}
img = Image.open(requests.get(url, headers=headers,
stream=True).raw).convert("RGB")
# preprocessing pipeline
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_preprocessed = preprocess(img)
return torch.unsqueeze(img_preprocessed, 0)
def load_labels():
classes_text = requests.get(
"https://raw.githubusercontent.com/cathyzhyi/ml-data/main/imagenet-classes.txt",
stream=True,
).text
labels = [line.strip() for line in classes_text.splitlines()]
return labels
def top3_possibilities(res):
_, indexes = torch.sort(res, descending=True)
percentage = torch.nn.functional.softmax(res, dim=1)[0] * 100
top3 = [(labels[idx], percentage[idx].item()) for idx in indexes[0][:3]]
return top3
def predictions(torch_func, jit_func, img, labels):
golden_prediction = top3_possibilities(torch_func(img))
print("PyTorch prediction")
print(golden_prediction)
prediction = top3_possibilities(torch.from_numpy(jit_func(img.numpy())))
print("torch-mlir prediction")
print(prediction)
class ResNet18Module(torch.nn.Module):
def __init__(self):
super().__init__()
self.resnet = models.resnet18(pretrained=True)
self.train(False)
def forward(self, img):
return self.resnet.forward(img)
class TestModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.s = ResNet18Module()
def forward(self, x):
return self.s.forward(x)
image_url = (
"https://upload.wikimedia.org/wikipedia/commons/2/26/YellowLabradorLooking_new.jpg"
)
import sys
print("load image from " + image_url, file=sys.stderr)
img = load_and_preprocess_image(image_url)
labels = load_labels()
test_module = TestModule()
class_annotator = ClassAnnotator()
recursivescriptmodule = torch.jit.script(test_module)
torch.jit.save(recursivescriptmodule, "/tmp/foo.pt")
class_annotator.exportNone(recursivescriptmodule._c._type())
class_annotator.exportPath(recursivescriptmodule._c._type(), ["forward"])
class_annotator.annotateArgs(
recursivescriptmodule._c._type(),
["forward"],
[
None,
([-1, -1, -1, -1], torch.float32, True),
],
)
# TODO: Automatically handle unpacking Python class RecursiveScriptModule into the underlying ScriptModule.
mb.import_module(recursivescriptmodule._c, class_annotator)
backend = refbackend.RefBackendLinalgOnTensorsBackend()
with mb.module.context:
pm = PassManager.parse('torchscript-module-to-torch-backend-pipeline,torch-backend-to-linalg-on-tensors-backend-pipeline')
pm.run(mb.module)
compiled = backend.compile(mb.module)
jit_module = backend.load(compiled)
predictions(test_module.forward, jit_module.forward, img, labels)