def main(_):
demo_layout = lit_dtypes.LitComponentLayout(
components={
'Main': [
'data-table-module',
'datapoint-editor-module',
'lit-slice-module',
'color-module',
],
'Predictions': ['classification-module', 'scalar-module'],
'Explanations': ['classification-module', 'salience-map-module'],
},
description='Basic layout for image demo',
)
datasets = {'imagenette': imagenette.ImagenetteDataset()}
models = {'mobilenet': mobilenet.MobileNet()}
interpreters = {
'Grad': image_gradient_maps.VanillaGradients(),
'Integrated Gradients': image_gradient_maps.IntegratedGradients(),
'Blur IG': image_gradient_maps.BlurIG(),
'Guided IG': image_gradient_maps.GuidedIG(),
'XRAI': image_gradient_maps.XRAI(),
'XRAI GIG': image_gradient_maps.XRAIGIG(),
}
lit_demo = dev_server.Server(models,
datasets,
interpreters=interpreters,
generators={},
layouts={'demo_layout': demo_layout},
**server_flags.get_flags())
return lit_demo.serve()
"ontonotes_edgeprobe_path", None,
"Path to OntoNotes coreference data in edge probing JSON format. "
"This is needed for training, and optional for running LIT.")
# Custom frontend layout; see client/lib/types.ts
WINOGENDER_LAYOUT = lit_dtypes.LitComponentLayout(
components={
"Main": [
"data-table-module",
"datapoint-editor-module",
"lit-slice-module",
"color-module",
],
"Predictions": [
"span-graph-gold-module",
"span-graph-module",
"classification-module",
],
"Performance": [
"metrics-module",
"scalar-module",
"confusion-matrix-module",
],
},
description="Custom layout for the Winogender coreference demo.",
)
CUSTOM_LAYOUTS = {"winogender": WINOGENDER_LAYOUT}
FLAGS.set_default("default_layout", "winogender")
flags.DEFINE_bool(
"load_bwb", False,
"If true, will load examples from the Billion Word Benchmark dataset. This may download a lot of data the first time you run it, so disable by default for the quick-start example."
)
# Custom frontend layout; see client/lib/types.ts
LM_LAYOUT = lit_dtypes.LitComponentLayout(
components={
"Main": [
"embeddings-module",
"data-table-module",
"datapoint-editor-module",
"lit-slice-module",
"color-module",
],
"Predictions": [
"lm-prediction-module",
"confusion-matrix-module",
],
"Counterfactuals": ["generator-module"],
},
description="Custom layout for language models.",
)
CUSTOM_LAYOUTS = {"lm": LM_LAYOUT}
# You can also change this via URL param e.g. localhost:5432/?layout=default
FLAGS.set_default("default_layout", "lm")
def get_wsgi_app():