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