mirror of https://github.com/llvm/torch-mlir
107 lines
3.2 KiB
Python
107 lines
3.2 KiB
Python
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# -*- Python -*-
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# This file is licensed under a pytorch-style license
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# See frontends/pytorch/LICENSE for license information.
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from PIL import Image
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import requests
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import torch
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import torchvision.models as models
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from torchvision import transforms
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import typing
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import torch_mlir
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import npcomp
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from npcomp.compiler.pytorch.backend import refjit, frontend_lowering, iree
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from npcomp.compiler.utils import logging
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logging.enable()
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mb = torch_mlir.ModuleBuilder()
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def load_and_preprocess_image(url: str):
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img = Image.open(requests.get(url, stream=True).raw).convert("RGB")
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# preprocessing pipeline
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preprocess = transforms.Compose(
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[
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transforms.Resize(256),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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]
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)
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img_preprocessed = preprocess(img)
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return torch.unsqueeze(img_preprocessed, 0)
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def load_labels():
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classes_text = requests.get(
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"https://raw.githubusercontent.com/cathyzhyi/ml-data/main/imagenet-classes.txt",
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stream=True,
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).text
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labels = [line.strip() for line in classes_text.splitlines()]
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return labels
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def top3_possibilities(res):
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_, indexes = torch.sort(res, descending=True)
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percentage = torch.nn.functional.softmax(res, dim=1)[0] * 100
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top3 = [(labels[idx], percentage[idx].item()) for idx in indexes[0][:3]]
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return top3
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def predictions(torch_func, jit_func, img, labels):
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golden_prediction = top3_possibilities(torch_func(img))
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print("PyTorch prediction")
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print(golden_prediction)
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prediction = top3_possibilities(torch.from_numpy(jit_func(img)))
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print("NPCOMP prediction")
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print(prediction)
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class ResNet18Module(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.resnet = models.resnet18(pretrained=True)
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self.train(False)
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def forward(self, img):
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return self.resnet.forward(img)
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class TestModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.s = ResNet18Module()
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def forward(self, x):
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return self.s.forward(x)
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test_module = TestModule()
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class_annotator = torch_mlir.ClassAnnotator()
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recursivescriptmodule = torch.jit.script(test_module)
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torch.jit.save(recursivescriptmodule, "/tmp/foo.pt")
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class_annotator.exportNone(recursivescriptmodule._c._type())
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class_annotator.exportPath(recursivescriptmodule._c._type(), ["forward"])
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class_annotator.annotateArgs(
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recursivescriptmodule._c._type(),
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["forward"],
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[None, ([-1, -1, -1, -1], torch.float32, True),],
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)
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# TODO: Automatically handle unpacking Python class RecursiveScriptModule into the underlying ScriptModule.
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mb.import_module(recursivescriptmodule._c, class_annotator)
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backend = refjit.RefjitNpcompBackend()
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compiled = backend.compile(frontend_lowering.lower_object_graph(mb.module))
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jit_module = backend.load(compiled)
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image_url = (
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"https://upload.wikimedia.org/wikipedia/commons/2/26/YellowLabradorLooking_new.jpg"
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)
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print("load image from " + image_url)
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img = load_and_preprocess_image(image_url)
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labels = load_labels()
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predictions(test_module.forward, jit_module.forward, img, labels)
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