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fix bugs for comparing output #26

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merged 1 commit into from
Apr 20, 2020
Merged

fix bugs for comparing output #26

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32 changes: 18 additions & 14 deletions hls4ml/model/profiling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -320,16 +320,18 @@ def numerical(keras_model=None, hls_model=None, X=None, plot='boxplot'):
def _is_ignored_layer(layer):
"""Some layers need to be ingored during inference"""
if isinstance(layer, (keras.layers.InputLayer,
keras.layers.Dropout,
keras.layers.Flatten)):
keras.layers.Dropout)):
return True
return False

def _add_layer_get_ouput(partial_model, layer, X):
copy_model = keras.models.clone_model(partial_model) #Make a copy to avoid modify the original model
copy_model.add(layer)
copy_model.compile(optimizer='adam', loss='mse')
y = copy_model.predict(X)
def _get_output(ymodel, layer, X):

#If there is no layer in the model just take that layer's output
if len(ymodel.keys()) == 0:
y = layer(X)
else:
#If there are already layers then calculate next output based on last layer's output
y = layer(ymodel[list(ymodel.keys())[-1]])
return y

def get_ymodel_keras(keras_model, X):
Expand Down Expand Up @@ -358,20 +360,22 @@ def get_ymodel_keras(keras_model, X):
if layer.activation:

if layer.activation.__name__ == "linear":
ymodel[layer.name] = _add_layer_get_ouput(partial_model, layer, X)
ymodel[layer.name] = _get_output(ymodel, layer, X)

else:
temp_activation = layer.activation
layer.activation = None
#Get output for layer without activation
ymodel[layer.name] = _add_layer_get_ouput(partial_model, layer, X)
ymodel[layer.name] = _get_output(ymodel, layer, X)

#Get ouput for activation
ymodel[layer.name + "_{}".format(temp_activation.__name__)] = temp_activation(ymodel[layer.name])
ymodel[layer.name + "_{}".format(temp_activation.__name__)] = _get_output(ymodel, temp_activation, X)

#Add the activation back
layer.activation = temp_activation

else:
ymodel[layer.name] = _add_layer_get_ouput(partial_model, layer, X)
ymodel[layer.name] = _get_output(ymodel, layer, X)

#Add the layer for later processing
partial_model.add(layer)
Expand All @@ -382,7 +386,7 @@ def _norm_diff(ymodel, ysim):
"""Calculate the square root of the sum of the squares of the differences"""
diff = {}

for key in list(ymodel.keys()):
for key in list(ysim.keys()):
diff[key] = np.linalg.norm(ysim[key]-ymodel[key])

#---Bar Plot---
Expand All @@ -406,7 +410,7 @@ def _dist_diff(ymodel, ysim):

diff = {}

for key in list(ymodel.keys()):
for key in list(ysim.keys()):
flattened_ysim = ysim[key].flatten()
flattened_ymodel = np.array(ymodel[key]).flatten()

Expand Down