torch-mlir/examples/eager_mode_resnet18.py

90 lines
2.6 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
import requests
import torch
import torchvision.models as models
from PIL import Image
from torchvision import transforms
from torch_mlir.eager_mode.torch_mlir_tensor import TorchMLIRTensor
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, img, labels):
golden_prediction = top3_possibilities(torch_func(img))
print("PyTorch prediction")
print(golden_prediction)
prediction = top3_possibilities(torch_func(TorchMLIRTensor(img)))
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"
)
print("load image from " + image_url, file=sys.stderr)
img = load_and_preprocess_image(image_url)
labels = load_labels()
test_module = TestModule()
predictions(test_module.forward, img, labels)