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move interpretation code to separate file

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This commit is contained in:
dawoodkhan82 2020-09-18 09:54:13 -04:00
parent 7e264abaad
commit c8b6dec050
5 changed files with 109 additions and 103 deletions
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2
demo/image_classifier.py
View File

@ -40,7 +40,7 @@ label = gr.outputs.Label(num_top_classes=3)
gr.Interface(image_classifier, imagein, label,
capture_session=True,
explain_by="default",
interpret_by="default",
examples=[
["images/cheetah1.jpg"],
["images/lion.jpg"]

2
demo/sentiment_analysis.py
View File

@ -9,7 +9,7 @@ def sentiment_analysis(text):
del scores["compound"]
return scores
io = gr.Interface(sentiment_analysis, "textbox", "label", explain_by="default")
io = gr.Interface(sentiment_analysis, "textbox", "label", interpret_by="default")
io.test_launch()
io.launch()

106
gradio/interface.py
View File

@ -7,15 +7,9 @@ import tempfile
import webbrowser
from gradio.inputs import InputComponent
from gradio.inputs import Image
from gradio.inputs import Textbox
from gradio.outputs import OutputComponent
from gradio import networking, strings, utils, processing_utils
from gradio import networking, strings, utils
from distutils.version import StrictVersion
from skimage.segmentation import slic
from skimage.util import img_as_float
from gradio import processing_utils
import PIL
import pkg_resources
import requests
import random
@ -26,7 +20,7 @@ import sys
import weakref
import analytics
import os
import numpy as np
PKG_VERSION_URL = "https://gradio.app/api/pkg-version"
analytics.write_key = "uxIFddIEuuUcFLf9VgH2teTEtPlWdkNy"
@ -53,7 +47,8 @@ class Interface:
def __init__(self, fn, inputs, outputs, verbose=False, examples=None,
live=False, show_input=True, show_output=True,
capture_session=False, explain_by=None, title=None, description=None,
capture_session=False, interpret_by=None, title=None,
description=None,
thumbnail=None, server_port=None, server_name=networking.LOCALHOST_NAME,
allow_screenshot=True, allow_flagging=True,
flagging_dir="flagged", analytics_enabled=True):
@ -117,7 +112,7 @@ class Interface:
self.show_output = show_output
self.flag_hash = random.getrandbits(32)
self.capture_session = capture_session
self.explain_by = explain_by
self.interpret_by = interpret_by
self.session = None
self.server_name = server_name
self.title = title
@ -186,7 +181,7 @@ class Interface:
"thumbnail": self.thumbnail,
"allow_screenshot": self.allow_screenshot,
"allow_flagging": self.allow_flagging,
"allow_interpretation": self.explain_by is not None
"allow_interpretation": self.interpret_by is not None
}
try:
param_names = inspect.getfullargspec(self.predict[0])[0]
@ -422,95 +417,6 @@ class Interface:
return httpd, path_to_local_server, share_url
def tokenize_text(self, text):
leave_one_out_tokens = []
tokens = text.split()
for idx, _ in enumerate(tokens):
new_token_array = tokens.copy()
del new_token_array[idx]
leave_one_out_tokens.append(new_token_array)
return tokens, leave_one_out_tokens
def tokenize_image(self, image):
image = np.array(processing_utils.decode_base64_to_image(image))
segments_slic = slic(image, n_segments=20, compactness=10, sigma=1)
leave_one_out_tokens = []
for (i, segVal) in enumerate(np.unique(segments_slic)):
mask = segments_slic == segVal
white_screen = np.copy(image)
white_screen[segments_slic == segVal] = 255
leave_one_out_tokens.append((mask, white_screen))
return leave_one_out_tokens
def score_text(self, tokens, leave_one_out_tokens, text):
original_label = ""
original_confidence = 0
tokens = text.split()
input_text = " ".join(tokens)
original_output = self.process([input_text])
output = {result["label"] : result["confidence"]
for result in original_output[0][0]['confidences']}
original_label = original_output[0][0]["label"]
original_confidence = output[original_label]
scores = []
for idx, input_text in enumerate(leave_one_out_tokens):
input_text = " ".join(input_text)
raw_output = self.process([input_text])
output = {result["label"] : result["confidence"]
for result in raw_output[0][0]['confidences']}
scores.append(original_confidence - output[original_label])
scores_by_char = []
for idx, token in enumerate(tokens):
if idx != 0:
scores_by_char.append((" ", 0))
for char in token:
scores_by_char.append((char, scores[idx]))
return scores_by_char
def score_image(self, leave_one_out_tokens, image):
original_output = self.process([image])
output = {result["label"] : result["confidence"]
for result in original_output[0][0]['confidences']}
original_label = original_output[0][0]["label"]
original_confidence = output[original_label]
image_interface = self.input_interfaces[0]
shape = processing_utils.decode_base64_to_image(image).size
output_scores = np.full((shape[1], shape[0]), 0.0)
for mask, input_image in leave_one_out_tokens:
input_image_base64 = processing_utils.encode_array_to_base64(
input_image)
raw_output = self.process([input_image_base64])
output = {result["label"] : result["confidence"]
for result in raw_output[0][0]['confidences']}
score = original_confidence - output[original_label]
output_scores += score * mask
max_val = np.max(np.abs(output_scores))
if max_val > 0:
output_scores = output_scores / max_val
return output_scores.tolist()
def simple_explanation(self, x):
if isinstance(self.input_interfaces[0], Textbox):
tokens, leave_one_out_tokens = self.tokenize_text(x[0])
return [self.score_text(tokens, leave_one_out_tokens, x[0])]
elif isinstance(self.input_interfaces[0], Image):
leave_one_out_tokens = self.tokenize_image(x[0])
return [self.score_image(leave_one_out_tokens, x[0])]
else:
print("Not valid input type")
def explain(self, x):
if self.explain_by == "default":
return self.simple_explanation(x)
else:
preprocessed_x = [input_interface(x_i) for x_i, input_interface in zip(x, self.input_interfaces)]
return self.explain_by(*preprocessed_x)
def reset_all():
for io in Interface.get_instances():
io.close()

99
gradio/interpretation.py Normal file
View File

@ -0,0 +1,99 @@
from gradio.inputs import Image
from gradio.inputs import Textbox
from gradio import processing_utils
from skimage.segmentation import slic
import numpy as np
def tokenize_text(text):
leave_one_out_tokens = []
tokens = text.split()
for idx, _ in enumerate(tokens):
new_token_array = tokens.copy()
del new_token_array[idx]
leave_one_out_tokens.append(new_token_array)
return tokens, leave_one_out_tokens
def tokenize_image(image):
image = np.array(processing_utils.decode_base64_to_image(image))
segments_slic = slic(image, n_segments=20, compactness=10, sigma=1)
leave_one_out_tokens = []
for (i, segVal) in enumerate(np.unique(segments_slic)):
mask = segments_slic == segVal
white_screen = np.copy(image)
white_screen[segments_slic == segVal] = 255
leave_one_out_tokens.append((mask, white_screen))
return leave_one_out_tokens
def score_text(interface, leave_one_out_tokens, text):
tokens = text.split()
input_text = " ".join(tokens)
original_output = interface.process([input_text])
output = {result["label"]: result["confidence"]
for result in original_output[0][0]['confidences']}
original_label = original_output[0][0]["label"]
original_confidence = output[original_label]
scores = []
for idx, input_text in enumerate(leave_one_out_tokens):
input_text = " ".join(input_text)
raw_output = interface.process([input_text])
output = {result["label"]: result["confidence"]
for result in raw_output[0][0]['confidences']}
scores.append(original_confidence - output[original_label])
scores_by_char = []
for idx, token in enumerate(tokens):
if idx != 0:
scores_by_char.append((" ", 0))
for char in token:
scores_by_char.append((char, scores[idx]))
return scores_by_char
def score_image(interface, leave_one_out_tokens, image):
original_output = interface.process([image])
output = {result["label"]: result["confidence"]
for result in original_output[0][0]['confidences']}
original_label = original_output[0][0]["label"]
original_confidence = output[original_label]
shape = processing_utils.decode_base64_to_image(image).size
output_scores = np.full((shape[1], shape[0]), 0.0)
for mask, input_image in leave_one_out_tokens:
input_image_base64 = processing_utils.encode_array_to_base64(
input_image)
raw_output = interface.process([input_image_base64])
output = {result["label"]: result["confidence"]
for result in raw_output[0][0]['confidences']}
score = original_confidence - output[original_label]
output_scores += score * mask
max_val = np.max(np.abs(output_scores))
if max_val > 0:
output_scores = output_scores / max_val
return output_scores.tolist()
def simple_interpretation(interface, x):
if isinstance(interface.input_interfaces[0], Textbox):
tokens, leave_one_out_tokens = tokenize_text(interface,
x[0])
return [score_text(interface, tokens, leave_one_out_tokens, x[0])]
elif isinstance(interface.input_interfaces[0], Image):
leave_one_out_tokens = tokenize_image(x[0])
return [score_image(interface, leave_one_out_tokens, x[0])]
else:
print("Not valid input type")
def interpret(interface, x):
if interface.interpret_by == "default":
return simple_interpretation(interface, x)
else:
preprocessed_x = [input_interface(x_i) for x_i, input_interface in
zip(x, interface.input_interfaces)]
return interface.interpret_by(*preprocessed_x)

3
gradio/networking.py
View File

@ -12,6 +12,7 @@ from gradio import inputs, outputs
import time
import json
from gradio.tunneling import create_tunnel
from gradio.interpretation import interpret
import urllib.request
from shutil import copyfile
import requests
@ -199,7 +200,7 @@ def serve_files_in_background(interface, port, directory_to_serve=None, server_n
data_string = self.rfile.read(
int(self.headers["Content-Length"]))
msg = json.loads(data_string)
interpretation = interface.explain(msg["data"])
interpretation = interpret(interface, msg["data"])
self.wfile.write(json.dumps(interpretation).encode())
else:
self.send_error(404, 'Path not found: {}'.format(self.path))