def _assign_trained_weights_to_separable_layers(self):
all_ops = []
with tf.variable_scope(self._net_sep.get_scope(), reuse=True):
for layer_name, K in self._net_desc.items():
source_weights = self._base_weights_dict[layer_name]
layer1_name = LayerName(layer_name + '_sep_K%d/weights' % (K))
dest_weights_1 = tf.get_variable(layer1_name.layer_weights())
dest_weights_2 = tf.get_variable(layer_name.layer_weights())
ops = self._get_assign_ops(source_weights, dest_weights_1,
dest_weights_2, K)
all_ops.extend(ops)
self._sess.run(all_ops)
def _assign_trained_weights_to_unchanged_layers(self):
with tf.variable_scope(self._net_sep.get_scope(), reuse=True):
restore_var_dict = {}
for v in self._base_variables:
name_full = v.op.name
layer_name = LayerName(name_full, 'net_layer_weights')
# if layer_name in self._net_desc:
# continue #skip compressed layers and their BatchNorm params
# print(name_full + ' ' + layer_name )
restore_var_dict[name_full] = tf.get_variable(layer_name.layer_weights())
saver = tf.train.Saver(restore_var_dict)
saver.restore(self._sess, self._saved_model_path)
def _assign_trained_weights_to_separable_layers(self):
all_ops = []
with tf.variable_scope(self._net_sep.get_scope(), reuse=True):
for layer_name, Ks in self._net_desc.items():
source_weights = self._base_weights_dict[layer_name]
for K in Ks:
layer1_name = LayerName(layer_name +'_sep_K' + str(K) + '/weights','layer_weights')
layer2_name = LayerName(layer_name +'_K' + str(K) + '/weights','layer_weights')
dest_weights_1 = tf.get_variable(layer1_name.layer_weights())
dest_weights_2 = tf.get_variable(layer2_name.layer_weights())
ops = self._get_assign_ops(source_weights, dest_weights_1, dest_weights_2, K)
all_ops.extend(ops)
for bn_type in ['beta','gamma','moving_mean','moving_variance']:
try:
dest_bn_var_name = layer_name+'_K' + str(K)+'/BatchNorm/'+bn_type
dest_bn_var = tf.get_variable(dest_bn_var_name)
source_bn_var_name = layer_name+'/BatchNorm/'+bn_type
source_bn_var = self._comp_bn_vars_dict[source_bn_var_name]
assign_op = tf.assign(dest_bn_var, source_bn_var)
all_ops.append(assign_op)
except ValueError:
break #means this compressed layer doesn't have BatchNorm
try:
dest_bias_var_name = layer_name+'_K' + str(K)+'/biases'
dest_bias_var = tf.get_variable(dest_bias_var_name)
source_bias_var_name = layer_name+'/biases'
source_bias_var = self._comp_bias_vars_dict[source_bias_var_name]
assign_op = tf.assign(dest_bias_var, source_bias_var)
all_ops.append(assign_op)
except ValueError:
continue #means this compressed layer doesn't have biases
self._sess.run(all_ops)