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https://github.com/nod-ai/AMD-SHARK-Studio.git
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fe080eaee6
This commit introduces web application for SHARK using gradio platform. This adds web visualization of `Resnet50` and `Albert_Maskfill` models as a start. Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com> Signed-off-by: Gaurav Shukla <gaurav@nod-labs.com>
55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
from PIL import Image
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import requests
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import torch
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from torchvision import transforms
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from shark.shark_inference import SharkInference
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from shark.shark_downloader import download_torch_model
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################################## Preprocessing inputs and model ############
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def preprocess_image(img):
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image = Image.fromarray(img)
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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(
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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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)
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img_preprocessed = preprocess(image)
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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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labels = load_labels()
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_, indexes = torch.sort(res, descending=True)
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percentage = torch.nn.functional.softmax(res, dim=1)[0]
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top3 = dict([(labels[idx], percentage[idx].item()) for idx in indexes[0][:3]])
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return top3
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##############################################################################
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def resnet_inf(numpy_img):
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img = preprocess_image(numpy_img)
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## Can pass any img or input to the forward module.
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mlir_model, func_name, inputs, golden_out = download_torch_model("resnet50")
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shark_module = SharkInference(mlir_model, func_name, mlir_dialect="linalg")
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shark_module.compile()
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result = shark_module.forward((img.detach().numpy(),))
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# print("The top 3 results obtained via shark_runner is:")
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return top3_possibilities(torch.from_numpy(result))
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