torch-mlir/examples/torchdynamo_resnet18.py

95 lines
3.3 KiB
Python

# 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.
import sys
from typing import List
from PIL import Image
import requests
import torch
import torch._dynamo as dynamo
import torchvision.models as models
from torchvision import transforms
import torch_mlir
from torch_mlir.dynamo import make_simple_dynamo_backend
from torch_mlir_e2e_test.linalg_on_tensors_backends import refbackend
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)
image_url = "https://upload.wikimedia.org/wikipedia/commons/2/26/YellowLabradorLooking_new.jpg"
print("load image from " + image_url, file=sys.stderr)
img = load_and_preprocess_image(image_url)
labels = load_labels()
@make_simple_dynamo_backend
def refbackend_torchdynamo_backend(fx_graph: torch.fx.GraphModule,
example_inputs: List[torch.Tensor]):
mlir_module = torch_mlir.compile(
fx_graph, example_inputs, output_type="linalg-on-tensors")
backend = refbackend.RefBackendLinalgOnTensorsBackend()
compiled = backend.compile(mlir_module)
loaded = backend.load(compiled)
def compiled_callable(*inputs):
inputs = [x.numpy() for x in inputs]
result = loaded.forward(*inputs)
if not isinstance(result, tuple):
result = torch.from_numpy(result)
else:
result = tuple(torch.from_numpy(x) for x in result)
return result
return compiled_callable
resnet18 = models.resnet18(pretrained=True)
resnet18.train(False)
dynamo_callable = dynamo.optimize(refbackend_torchdynamo_backend)(resnet18)
predictions(resnet18.forward, lambda x: dynamo_callable(torch.from_numpy(x)).detach().numpy(), img, labels)