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modified saliency function to accept more parameters

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This commit is contained in:
Abubakar Abid 2019-07-25 10:59:54 -07:00
parent 37c67f0f5d
commit faf3b738f4
3 changed files with 6 additions and 34 deletions
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3
gradio/inputs.py
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@ -174,8 +174,9 @@ class Webcam(AbstractInput):
class Textbox(AbstractInput):
def __init__(self, sample_inputs=None):
def __init__(self, sample_inputs=None, preprocessing_fn=None):
self.sample_inputs = sample_inputs
super().__init__(preprocessing_fn=preprocessing_fn)
def get_validation_inputs(self):
return validation_data.ENGLISH_TEXTS

6
gradio/networking.py
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@ -126,6 +126,7 @@ def set_sample_data_in_config_file(temp_dir, sample_inputs):
},
)
def set_disabled_in_config_file(temp_dir, disabled):
config_file = os.path.join(temp_dir, CONFIG_FILE)
render_template_with_tags(
@ -184,13 +185,14 @@ def serve_files_in_background(interface, port, directory_to_serve=None):
self._set_headers()
data_string = self.rfile.read(int(self.headers["Content-Length"]))
msg = json.loads(data_string)
processed_input = interface.input_interface.preprocess(msg["data"])
raw_input = msg["data"]
processed_input = interface.input_interface.preprocess(raw_input)
prediction = interface.predict(processed_input)
processed_output = interface.output_interface.postprocess(prediction)
output = {"action": "output", "data": processed_output}
if interface.saliency is not None:
import numpy as np
saliency = interface.saliency(interface.model_obj, processed_input, prediction)
saliency = interface.saliency(interface.model_obj, raw_input, processed_input, prediction, processed_output)
output['saliency'] = saliency.tolist()
# Prepare return json dictionary.

31
gradio/test.py
View File

@ -1,31 +0,0 @@
import tensorflow as tf
import gradio
#CREATE INTERFACE USING 'GOOD' MNIST MODEL
(x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data()
model = tf.keras.models.load_model('MNIST_9344.h5')
input = gradio.inputs.Sketchpad(sample_inputs=x_train[:10])
iface = gradio.Interface(inputs=input, outputs="label", model=model, model_type='keras')
iface.launch(inline=False, share=False, inbrowser=True);
#CREATE INTERFACE BY TRAINING MSNIST MODEL
# (x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data()
# x_train, x_test = x_train / 255.0, x_test / 255.0
#
# model = tf.keras.models.Sequential([
# tf.keras.layers.Flatten(),
# tf.keras.layers.Dense(512, activation=tf.nn.relu),
# tf.keras.layers.Dropout(0.2),
# tf.keras.layers.Dense(10, activation=tf.nn.softmax)
# ])
#
# model.compile(optimizer='adam',
# loss='sparse_categorical_crossentropy',
# metrics=['accuracy'])
# model.fit(x_train, y_train, epochs=1)
# inp = gradio.inputs.Sketchpad(sample_inputs=x_train[:10])
# iface = gradio.Interface(inputs=inp, outputs="label", model=model, model_type='keras')
# iface.launch(inline=False, share=False, inbrowser=True);