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Fixed to work with keras2onnx-made models #76

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Fixed to work with keras2onnx-made models #76

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26 changes: 25 additions & 1 deletion onnx2keras/converter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -76,7 +76,14 @@ def onnx_to_keras(onnx_model, input_names,

logger.debug('Gathering weights to dictionary.')
weights = {}

for onnx_w in onnx_weights:
# Simpler solution. Seems to work with all keras2onnx-made models (July 28 2020)
# Unsure if it works with others. See below alternate solution
onnx_extracted_weights_name = onnx_w.ListFields()[-1][1]
weights[onnx_extracted_weights_name] = numpy_helper.to_array(onnx_w)
# Previous code, does not work for models from keras2onnx as of July 28 2020, and likely earlier
'''
try:
if len(onnx_w.ListFields()) < 4:
onnx_extracted_weights_name = onnx_w.ListFields()[1][1]
Expand All @@ -86,7 +93,24 @@ def onnx_to_keras(onnx_model, input_names,
except:
onnx_extracted_weights_name = onnx_w.ListFields()[3][1]
weights[onnx_extracted_weights_name] = numpy_helper.to_array(onnx_w)

'''
# Alternate (ugly) solution, based on previous code
'''
try:
if len(onnx_w.ListFields()) < 4:
onnx_extracted_weights_name = onnx_w.ListFields()[1][1]
else:
onnx_extracted_weights_name = onnx_w.ListFields()[2][1]
weights[onnx_extracted_weights_name] = numpy_helper.to_array(onnx_w)
except:
# Ugly fix
try:
onnx_extracted_weights_name = onnx_w.ListFields()[3][1]
weights[onnx_extracted_weights_name] = numpy_helper.to_array(onnx_w)
except:
onnx_extracted_weights_name = onnx_w.ListFields()[-1][1]
weights[onnx_extracted_weights_name] = numpy_helper.to_array(onnx_w)
'''
logger.debug('Found weight {0} with shape {1}.'.format(
onnx_extracted_weights_name,
weights[onnx_extracted_weights_name].shape))
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18 changes: 13 additions & 5 deletions onnx2keras/operation_layers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,12 +27,20 @@ def convert_clip(node, params, layers, lambda_func, node_name, keras_name):
assert AttributeError('More than 1 input for clip layer.')

input_0 = ensure_tf_type(layers[node.input[0]], name="%s_const" % keras_name)

if params['min'] == 0:
logger.debug("Using ReLU({0}) instead of clip".format(params['max']))
layer = keras.layers.ReLU(max_value=params['max'], name=keras_name)
clip_min = None
clip_max = None
for key in layers.keys():
if 'clip_min' in key:
clip_min = layers[key]
elif 'clip_max' in key:
clip_max = layers[key]
if clip_min == 0:
logger.debug("Using ReLU({0}) instead of clip".format(clip_max))
layer = keras.layers.ReLU(max_value=clip_max)
# previous code, caused scope name errors for keras2onnx-made models
#layer = keras.layers.ReLU(max_value=clip_max, name=keras_name)
else:
def target_layer(x, vmin=params['min'], vmax=params['max']):
def target_layer(x, vmin=clip_min, vmax=clip_max):
import tensorflow as tf
return tf.clip_by_value(x, vmin, vmax)
layer = keras.layers.Lambda(target_layer, name=keras_name)
Expand Down