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Convert Lambda layer to Reshape layer in global avg pooling #105

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Convert Lambda layer to Reshape layer in global avg pooling #105

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18 changes: 6 additions & 12 deletions onnx2keras/pooling_layers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -142,21 +142,15 @@ def convert_global_avg_pool(node, params, layers, lambda_func, node_name, keras_
:param keras_name: resulting layer name
:return: None
"""
logger = logging.getLogger('onnx2keras.global_avg_pool')

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

global_pool = keras.layers.GlobalAveragePooling2D(data_format='channels_first', name=keras_name)
input_0 = global_pool(input_0)
new_shape = input_0.shape.as_list()
new_shape = new_shape[1:]
new_shape.extend([1, 1])
reshape_layer = keras.layers.Reshape(new_shape)
input_0 = reshape_layer(input_0)

def target_layer(x):
from tensorflow import keras
return keras.backend.expand_dims(x)

logger.debug('Now expand dimensions twice.')
lambda_layer1 = keras.layers.Lambda(target_layer, name=keras_name + '_EXPAND1')
lambda_layer2 = keras.layers.Lambda(target_layer, name=keras_name + '_EXPAND2')
input_0 = lambda_layer1(input_0) # double expand dims
layers[node_name] = lambda_layer2(input_0)
lambda_func[keras_name + '_EXPAND1'] = target_layer
lambda_func[keras_name + '_EXPAND2'] = target_layer
layers[node_name] = input_0