infer_transunet

Run TransUNet model from a given config file and a weight file trained with the Ikomia algorithm train_transunet.

🚀 Use with Ikomia API

1. Install Ikomia API

We strongly recommend using a virtual environment. If you're not sure where to start, we offer a tutorial here.

pip install ikomia

2. Create your workflow

from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_transunet", auto_connect=True)
algo.set_parameters({
    "config_file": "path/to/config.yaml",
    "model_weight_file": "path/to/best_model.pth",
})

# Run on your image  
wf.run_on(path="path/to/image")

# Inpect your result
display(algo.get_image_with_mask())

☀️ Use with Ikomia Studio

Ikomia Studio offers a friendly UI with the same features as the API.

• If you haven't started using Ikomia Studio yet, download and install it from this page.

• For additional guidance on getting started with Ikomia Studio, check out this blog post.

🔍 Explore algorithm outputs

Every algorithm produces specific outputs, yet they can be explored them the same way using the Ikomia API. For a more in-depth understanding of managing algorithm outputs, please refer to the documentation.

import ikomia
from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_transunet", auto_connect=True)
algo.set_parameters({
    "config_file": "path/to/config.yaml",
    "model_weight_file": "path/to/best_model.pth",
})

# Run on your image  
wf.run_on(path="path/to/image")

# Iterate over outputs
for output in algo.get_outputs():
    # Print information
    print(output)
    # Export it to JSON
    output.to_json()