Spaces:
Runtime error
Runtime error
File size: 2,876 Bytes
fa636b5 e3914b4 8e67e6e e3914b4 fa636b5 e3914b4 8e67e6e e3914b4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
import numpy as np
import gradio as gr
"""An example of generating a gif explanation for an image of my dog."""
import argparse
import os
from os.path import exists, dirname
import sys
import flask
parent_dir = dirname(os.path.abspath(os.getcwd()))
sys.path.append(parent_dir)
from bayes.explanations import BayesLocalExplanations, explain_many
from bayes.data_routines import get_dataset_by_name
from bayes.models import *
from image_posterior import create_gif
parser = argparse.ArgumentParser()
parser.add_argument("--cred_width", type=float, default=0.1)
parser.add_argument("--save_loc", type=str, required=True)
parser.add_argument("--n_top_segs", type=int, default=5)
parser.add_argument("--n_gif_images", type=int, default=20)
IMAGE_NAME = "imagenet_diego"
BLENHEIM_SPANIEL_CLASS = 156
def get_image_data():
"""Gets the image data and model."""
puppy_image = get_dataset_by_name(IMAGE_NAME, get_label=False)
model_and_data = process_imagenet_get_model(puppy_image)
return puppy_image, model_and_data
def segmentation_generation():
cred_width = 0.1
n_top_segs = 5
n_gif_images = 20
puppy_image, model_and_data = get_image_data()
# Unpack datax
xtest = model_and_data["xtest"]
ytest = model_and_data["ytest"]
segs = model_and_data["xtest_segs"]
get_model = model_and_data["model"]
label = model_and_data["label"]
# Unpack instance and segments
instance = xtest[0]
segments = segs[0]
# Get wrapped model
cur_model = get_model(instance, segments)
# Get background data
xtrain = get_xtrain(segments)
prediction = np.argmax(cur_model(xtrain[:1]), axis=1)
assert prediction == BLENHEIM_SPANIEL_CLASS, f"Prediction is {prediction} not {BLENHEIM_SPANIEL_CLASS}"
# Compute explanation
exp_init = BayesLocalExplanations(training_data=xtrain,
data="image",
kernel="lime",
categorical_features=np.arange(xtrain.shape[1]),
verbose=True)
rout = exp_init.explain(classifier_f=cur_model,
data=np.ones_like(xtrain[0]),
label=BLENHEIM_SPANIEL_CLASS,
cred_width=cred_width,
focus_sample=False,
l2=False)
# Create the gif of the explanation
return create_gif(rout['blr'], segments, instance, n_gif_images, n_top_segs)
def image_mod(image):
return image.rotate(45)
if __name__ == "__main__":
inp = gr.inputs.Image(label="Input Image", type="pil")
out = gr.outputs.Video(label="Output Image")
iface = gr.Interface(segmentation_generation, inputs=inp, outputs=out, examples=[["./imagenet_diego.png"]])
iface.launch() |