def generate_conv_model(model):
model = model.get_layer(index=1)
inp = (model.inputs[0].shape.dims[1].value,
model.inputs[0].shape.dims[2].value,
model.inputs[0].shape.dims[3].value)
H = Input(inp)
inp = H
for layer_idx in range(1, len(model.layers)):
layer = model.get_layer(index=layer_idx)
config = layer.get_config()
if isinstance(layer, MaxPooling2D):
H = MaxPooling2D.from_config(config)(H)
if isinstance(layer, Dropout):
H = Dropout.from_config(config)(H)
if isinstance(layer, Activation):
H = Activation.from_config(config)(H)
if isinstance(layer, BatchNormalization):
weights = layer.get_weights()
H = BatchNormalization(weights=weights)(H)
elif isinstance(layer, Conv2D):
weights = layer.get_weights()
config['filters'] = weights[1].shape[0]
H = Conv2D(activation=config['activation'],
activity_regularizer=config['activity_regularizer'],
bias_constraint=config['bias_constraint'],
bias_regularizer=config['bias_regularizer'],
data_format=config['data_format'],
dilation_rate=config['dilation_rate'],
filters=config['filters'],
kernel_constraint=config['kernel_constraint'],
kernel_regularizer=config['kernel_regularizer'],
kernel_size=config['kernel_size'],
name=config['name'],
padding=config['padding'],
strides=config['strides'],
trainable=config['trainable'],
use_bias=config['use_bias'],
weights=weights)(H)
return Model(inp, H)
def rebuild_model(self, kernel_list=None):
## args
## list of kernels to remove.
inp = (self.model.inputs[0].shape.dims[1].value,
self.model.inputs[0].shape.dims[2].value,
self.model.inputs[0].shape.dims[3].value)
H = Input(inp)
inp = H
for i in range(len(self.model.layers)):
layer = self.model.get_layer(index=i)
config = layer.get_config()
if isinstance(layer, MaxPooling2D):
H = MaxPooling2D.from_config(config)(H)
if isinstance(layer, Dropout):
H = Dropout.from_config(config)(H)
if isinstance(layer, Activation):
H = Activation.from_config(config)(H)
elif isinstance(layer, Conv2D):
weights = layer.get_weights()
if i == self.target_layer:
weights[0] = np.delete(weights[0], kernel_list, axis=3)
if (len(weights) == 2):
weights[1] = np.delete(weights[1], kernel_list, 0)
else:
if i == self.changed_layer:
weights[0] = np.delete(weights[0], kernel_list, axis=2)
config['filters'] = weights[0].shape[3]
H = Conv2D(
activation=config['activation'],
activity_regularizer=config['activity_regularizer'],
bias_constraint=config['bias_constraint'],
bias_regularizer=config['bias_regularizer'],
data_format=config['data_format'],
dilation_rate=config['dilation_rate'],
filters=config['filters'],
kernel_constraint=config['kernel_constraint'],
# config=config['config'],
# scale=config['scale'],
kernel_regularizer=config['kernel_regularizer'],
kernel_size=config['kernel_size'],
name=config['name'],
padding=config['padding'],
strides=config['strides'],
trainable=config['trainable'],
use_bias=config['use_bias'],
weights=weights)(H)
elif isinstance(layer, Flatten):
H = Flatten()(H)
elif isinstance(layer, Dense):
weights = layer.get_weights()
if i == self.changed_layer:
shape = self.model.layers[i - 1].input_shape
new_weights = np.zeros(
(shape[1] * shape[2] * (shape[3] - len(kernel_list)),
weights[0].shape[1]))
for j in range(weights[0].shape[1]):
new_weights[:, j] = np.delete(
weights[0][:, j].reshape(
(shape[1], shape[2], shape[3])), kernel_list,
2).reshape(-1)
weights[0] = new_weights
config['units'] = weights[0].shape[1]
H = Dense(units=config['units'],
activation=config['activation'],
activity_regularizer=config['activity_regularizer'],
bias_constraint=config['bias_constraint'],
bias_regularizer=config['bias_regularizer'],
kernel_constraint=config['kernel_constraint'],
kernel_regularizer=config['kernel_regularizer'],
name=config['name'],
trainable=config['trainable'],
use_bias=config['use_bias'],
weights=weights)(H)
## it returns the model changed
return Model(inp, H)
def rebuild_net(model=None, layer_filters=[]):
n_discarded_filters = 0
total_filters = 0
model = model
inp = (model.inputs[0].shape.dims[1].value,
model.inputs[0].shape.dims[2].value,
model.inputs[0].shape.dims[3].value)
H = Input(inp)
inp = H
idx_previous = []
for i in range(1, len(model.layers)):
layer = model.get_layer(index=i)
config = layer.get_config()
if isinstance(layer, MaxPooling2D):
H = MaxPooling2D.from_config(config)(H)
if isinstance(layer, Dropout):
H = Dropout.from_config(config)(H)
if isinstance(layer, Activation):
H = Activation.from_config(config)(H)
if isinstance(layer, BatchNormalization):
weights = layer.get_weights()
weights[0] = np.delete(weights[0], idx_previous)
weights[1] = np.delete(weights[1], idx_previous)
weights[2] = np.delete(weights[2], idx_previous)
weights[3] = np.delete(weights[3], idx_previous)
H = BatchNormalization(weights=weights)(H)
elif isinstance(layer, Conv2D):
weights = layer.get_weights()
n_filters = weights[0].shape[3]
total_filters = total_filters + n_filters
idxs = [item for item in layer_filters if item[0] == i][0][1]
weights[0] = np.delete(weights[0], idxs, axis=3)
weights[1] = np.delete(weights[1], idxs)
n_discarded_filters += len(idxs)
if len(idx_previous) != 0:
weights[0] = np.delete(weights[0], idx_previous, axis=2)
config['filters'] = weights[1].shape[0]
H = Conv2D(
activation=config['activation'],
activity_regularizer=config['activity_regularizer'],
bias_constraint=config['bias_constraint'],
bias_regularizer=config['bias_regularizer'],
data_format=config['data_format'],
dilation_rate=config['dilation_rate'],
filters=config['filters'],
kernel_constraint=config['kernel_constraint'],
# config=config['config'],
# scale=config['scale'],
kernel_regularizer=config['kernel_regularizer'],
kernel_size=config['kernel_size'],
name=config['name'],
padding=config['padding'],
strides=config['strides'],
trainable=config['trainable'],
use_bias=config['use_bias'],
weights=weights)(H)
idx_previous = idxs
print('Percentage of discarded filters {}'.format(n_discarded_filters /
float(total_filters)))
return Model(inp, H)
