Conv2D(filters=32,
kernel_size=(1, 1),
padding='same',
activation='relu',
name='conv2dweight_' + str(ii),
weights=[W, bias]))
else:
if layer_type == "Conv2D":
layer_config = Conv2D.get_config(layer_lst[ii])
layer_temp = Conv2D.from_config(layer_config)
elif layer_type == "MaxPooling2D":
layer_config = MaxPooling2D.get_config(layer_lst[ii])
layer_temp = MaxPooling2D.from_config(layer_config)
elif layer_type == "Dense":
layer_config = Dense.get_config(layer_lst[ii])
layer_temp = Dense.from_config(layer_config)
elif layer_type == "Flatten":
layer_temp = Flatten()
else:
layer_config = tf.keras.layers.Layer.get_config(layer_lst[ii])
layer_temp = tf.keras.layers.Layer.from_config(layer_config)
layer_weight = layer_lst[ii].get_weights()
layer_temp.build(layer_lst[ii].input_shape)
if layer_weight:
layer_temp.set_weights(layer_weight)
model_new.add(layer_temp)
model_new.compile(optimizer='adam',
def conv_swap(filepath, model=settings.options.predictmodel):
old_model = load_model(model,
custom_objects={
'dsc_l2': dsc_l2,
'l1': l1,
'dsc': dsc,
'dsc_int': dsc,
'ISTA': ISTA
})
layer_lst = [l for l in old_model.layers]
with tf.name_scope('my_scope'):
layer_in = layer_lst[0].input
print(layer_lst[0].name)
print(layer_lst[1].name)
layer_config = Activation.get_config(layer_lst[1])
layer_temp = Activation.from_config(layer_config)
layer_temp.build(layer_lst[1].input_shape)
layer_mid = layer_temp(layer_in)
for ii in range(2, len(layer_lst) - 1):
layer_type = type(layer_lst[ii]).__name__
layer_next = type(layer_lst[ii + 1]).__name__
print(layer_lst[ii].name)
if layer_type == "Conv2D" and layer_type != "MaxPooling2D" and layer_lst[
ii].input_shape == layer_lst[ii].output_shape:
conv, bias = layer_lst[ii].get_weights()
D, W, _, err, idx = depthwise_factorization(np.array(conv))
print(err[idx - 1])
D = D[..., np.newaxis]
W = W.T
W = W[np.newaxis, np.newaxis, ...]
if layer_next == "Add":
layer_temp = DepthwiseConv2D(
kernel_size=layer_lst[ii].kernel_size,
padding='same',
activation='linear',
use_bias=False,
weights=[D])(layer_mid)
layer_temp = Conv2D(filters=settings.options.filters,
kernel_size=(1, 1),
padding='same',
activation=settings.options.activation,
name='conv2Dweight_' + str(ii),
weights=[W, bias])(layer_temp)
layer_mid = Add()([layer_mid, layer_temp])
else:
layer_mid = DepthwiseConv2D(
kernel_size=layer_lst[ii].kernel_size,
padding='same',
activation='linear',
use_bias=False,
weights=[D])(layer_mid)
layer_mid = Conv2D(filters=settings.options.filters,
kernel_size=(1, 1),
padding='same',
activation=settings.options.activation,
name='conv2dweight_' + str(ii),
weights=[W, bias])(layer_mid)
elif layer_type == "Add":
continue
else:
if layer_type == "Conv2D":
layer_config = Conv2D.get_config(layer_lst[ii])
layer_temp = Conv2D.from_config(layer_config)
elif layer_type == "MaxPooling2D":
layer_config = MaxPooling2D.get_config(layer_lst[ii])
layer_temp = MaxPooling2D.from_config(layer_config)
elif layer_type == "AveragePooling2D":
layer_config = AveragePooling2D.get_config(layer_lst[ii])
layer_temp = AveragePooling2D.from_config(layer_config)
elif layer_type == "UpSampling2D":
layer_config = UpSampling2D.get_config(layer_lst[ii])
layer_temp = UpSampling2D.from_config(layer_config)
elif layer_type == "SpatialDropout2D":
layer_config = SpatialDropout2D.get_config(layer_lst[ii])
layer_temp = SpatialDropout2D.from_config(layer_config)
elif layer_type == "Dense":
layer_config = Dense.get_config(layer_lst[ii])
layer_temp = Dense.from_config(layer_config)
else:
layer_config = keras.layers.Layer.get_config(layer_lst[ii])
layer_temp = keras.layers.Layer.from_config(layer_config)
layer_weight = layer_lst[ii].get_weights()
layer_temp.build(layer_lst[ii].input_shape)
if layer_weight:
layer_temp.set_weights(layer_weight)
if layer_next == "Add":
layer_temp = layer_temp(layer_mid)
layer_mid = Add()([layer_mid, layer_temp])
else:
layer_mid = layer_temp(layer_mid)
# final layer
if layer_type == "Conv2D":
layer_config = Conv2D.get_config(layer_lst[-1])
layer_temp = Conv2D.from_config(layer_config)
elif layer_type == "Dense":
layer_config = Dense.get_config(layer_lst[-1])
layer_temp = Dense.from_config(layer_config)
layer_weight = layer_lst[-1].get_weights()
layer_temp.build(layer_lst[-1].input_shape)
if layer_weight:
layer_temp.set_weights(layer_weight)
layer_out = layer_temp(layer_mid)
new_model = Model(inputs=layer_in, outputs=layer_out)
new_model.summary()
tf.keras.models.save_model(new_model, filepath)
return new_model