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Runtime error
Martijn van Beers
commited on
Commit
•
f992b4c
1
Parent(s):
17ad4ef
Make run_demo work with partial examples
Browse filesBecause of the caching for gradio.Examples that gradio does
automatically, run_demo gets called without the default value
for model_name. So we rearrange stuff so it still works then too.
app.py
CHANGED
@@ -53,7 +53,14 @@ import en_core_web_sm
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nlp = en_core_web_sm.load()
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# Gradio Section:
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-
def run_demo(
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model, preprocess = clip.load(model_name, device=device, jit=False)
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orig_image = pad_to_square(image)
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@@ -77,9 +84,9 @@ def run_demo(model_name, image, text):
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# Default demo:
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default_inputs = [
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gr.Dropdown(label="CLIP Model", choices=['ViT-B/16', 'ViT-B/32', 'ViT-L/14'], value="ViT-L/14"),
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gr.components.Image(type='pil', label="Original Image"),
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gr.components.Textbox(label="Image description"),
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]
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default_outputs = [
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@@ -102,17 +109,17 @@ iface = gr.Interface(fn=run_demo,
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outputs=default_outputs,
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title="CLIP Grounding Explainability",
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description=description,
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examples=[[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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# NER demo:
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def add_label_to_img(img, label, add_entity_label=True):
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@@ -162,9 +169,9 @@ def NER_demo(image, text):
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return labeled_text, gallery_images
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inputs_NER = [
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gr.Dropdown(label="CLIP Model", choices=['ViT-B/16', 'ViT-B/32', 'ViT-L/14'], value="ViT-L/14"),
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gr.Image(type='pil', label="Original Image"),
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gr.components.Textbox(label="Descriptive text"),
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]
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#colours = highlighter._style["color_map"]
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@@ -184,8 +191,8 @@ iface_NER = gr.Interface(fn=NER_demo,
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title="Named Entity Grounding explainability using CLIP",
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description=description_NER,
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examples=[
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[
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[
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],
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cache_examples=False)
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nlp = en_core_web_sm.load()
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# Gradio Section:
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def run_demo(*args):
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if len(args) == 3:
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image, text, model_name = args
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elif len(args) == 2:
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image, text = args
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model_name = "ViT-L/14"
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else:
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raise ValueError("Unexpected number of parameters")
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model, preprocess = clip.load(model_name, device=device, jit=False)
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orig_image = pad_to_square(image)
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# Default demo:
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default_inputs = [
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gr.components.Image(type='pil', label="Original Image"),
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gr.components.Textbox(label="Image description"),
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gr.Dropdown(label="CLIP Model", choices=['ViT-B/16', 'ViT-B/32', 'ViT-L/14'], value="ViT-L/14"),
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]
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default_outputs = [
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outputs=default_outputs,
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title="CLIP Grounding Explainability",
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description=description,
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examples=[["example_images/London.png", "London Eye"],
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["example_images/London.png", "Big Ben"],
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["example_images/harrypotter.png", "Harry"],
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["example_images/harrypotter.png", "Hermione"],
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["example_images/harrypotter.png", "Ron"],
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["example_images/Amsterdam.png", "Amsterdam canal"],
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["example_images/Amsterdam.png", "Old buildings"],
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["example_images/Amsterdam.png", "Pink flowers"],
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["example_images/dogs_on_bed.png", "Two dogs"],
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["example_images/dogs_on_bed.png", "Book"],
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["example_images/dogs_on_bed.png", "Cat"]])
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# NER demo:
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def add_label_to_img(img, label, add_entity_label=True):
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return labeled_text, gallery_images
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inputs_NER = [
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gr.Image(type='pil', label="Original Image"),
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gr.components.Textbox(label="Descriptive text"),
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gr.Dropdown(label="CLIP Model", choices=['ViT-B/16', 'ViT-B/32', 'ViT-L/14'], value="ViT-L/14"),
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]
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#colours = highlighter._style["color_map"]
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title="Named Entity Grounding explainability using CLIP",
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description=description_NER,
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examples=[
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["example_images/London.png", "In this image we see Big Ben and the London Eye, on both sides of the river Thames."],
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["example_images/harrypotter.png", "Hermione, Harry and Ron in their school uniform"],
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],
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cache_examples=False)
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