def explain_image(self, model, data, labels):
with DeepExplain(session=K.get_session()) as de:
model_wo_sm = iutils.keras.graph.model_wo_softmax(model)
input_tensor = model_wo_sm.layers[0].input
output_tensor = model_wo_sm.layers[-1].output
fModel = keras.models.Model(inputs=input_tensor,
outputs=output_tensor)
target_tensor = fModel(input_tensor)
attributions = de.explain('occlusion',
target_tensor * labels,
input_tensor,
data,
window_shape=self.window_shape,
step=self.step)
attributions = np.nan_to_num(attributions)
analysis = attributions
analysis = iutils.postprocess_images(analysis,
color_coding='BGRtoRGB',
channels_first=False)
analysis = ivis.gamma(analysis, minamp=0, gamma=0.95)
analysis = ivis.heatmap(analysis)
return analysis[0]
def explain_image_innvestigate(self, model, data):
try:
# Build the model
model = keras.models.Model(inputs=model.inputs,
outputs=model.outputs)
model.compile(optimizer="adam", loss="categorical_crossentropy")
model_wo_sm = iutils.keras.graph.model_wo_softmax(model)
analyzer = innvestigate.create_analyzer(self.gradient_method,
model_wo_sm)
analysis = analyzer.analyze(data)
analysis = iutils.postprocess_images(analysis,
color_coding='BGRtoRGB',
channels_first=False)
analysis = ivis.gamma(analysis, minamp=0, gamma=0.95)
analysis = ivis.heatmap(analysis)
return analysis[0]
except innvestigate.NotAnalyzeableModelException:
return None
except Exception:
return None
def explain_image_deepexplain(self, model, data, labels):
with DeepExplain(session=K.get_session()) as de:
model_wo_sm = iutils.keras.graph.model_wo_softmax(model)
input_tensor = model_wo_sm.layers[0].input
output_tensor = model_wo_sm.layers[-1].output
fModel = keras.models.Model(inputs=input_tensor,
outputs=output_tensor)
target_tensor = fModel(input_tensor)
attributions = de.explain(self.lrp_method, target_tensor * labels,
input_tensor, data, **self.kwargs)
analysis = attributions
analysis = iutils.postprocess_images(analysis,
color_coding='BGRtoRGB',
channels_first=False)
analysis = ivis.gamma(analysis, minamp=0, gamma=0.95)
analysis = ivis.heatmap(analysis)
return analysis[0]
def postprocess(X, color_conversion, channels_first):
X = X.copy()
X = iutils.postprocess_images(
X, color_coding=color_conversion, channels_first=channels_first)
return X
def image(X):
X = X.copy()
X = iutils.postprocess_images(X)
return ivis.graymap(X, input_is_postive_only=True)
def postprocess(X):
X = X.copy()
X = iutils.postprocess_images(X)
return X
