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69 lines
2.0 KiB
69 lines
2.0 KiB
from eden.block import Block
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from eden.datatypes import Image
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from eden.hosting import host_block
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## eden <3 pytorch
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from torchvision import models, transforms
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import torch
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model = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
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model = model.eval() ## no dont move it to the gpu just yet :)
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my_transforms = transforms.Compose(
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[
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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]), # this normalizes the image to the same format as the pretrained model
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]
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)
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eden_block = Block()
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my_args = {
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"width": 224, ## width
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"height": 224, ## height
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"input_image": Image(), ## images require eden.datatypes.Image()
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}
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import requests
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labels = requests.get(
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"https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt"
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).text.split("\n")
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@eden_block.run(args=my_args, progress=False)
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def do_something(config):
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global model, labels
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pil_image = config["input_image"]
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pil_image = pil_image.resize((config["width"], config["height"]))
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device = config.gpu
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input_tensor = my_transforms(pil_image).to(device).unsqueeze(0)
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model = model.to(device)
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with torch.no_grad():
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pred = model(input_tensor)[0].cpu()
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index = torch.argmax(pred).item()
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value = pred[index].item()
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# the index is the classification label for the pretrained resnet18 model.
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# the human-readable labels associated with this index are pulled and returned as "label"
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# we need to get them from imagenet labels, which we need to get online.
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label = labels[index]
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# serialize the image
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pil_image = Image(pil_image)
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return {"value": value, "index": index, "label": label, 'image': pil_image}
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if __name__ == "__main__":
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host_block(
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block=eden_block,
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port=5656,
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host="0.0.0.0",
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redis_host="redis",
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# logfile="log.log",
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logfile=None,
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log_level="debug",
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max_num_workers=1,
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requires_gpu=True,
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)
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