flops regularizer with multiple output logits

[itabhiyanta]

Hi
Thanks a lot for making morphnet open source. I wish to try it out but it seems I am having some trouble with getting it to work.
I have multiple outputs from my network. I am using the GroupLassoFlopsRegularizer and it seems to give me an error when i pass it a list of logits which come out of my network.
My network

`
def build_model(self):
self.g = tf.Graph()
with self.g.as_default():
tf.set_random_seed(42)
self.inputs = tf.placeholder(tf.float32, [None, *(self.state_size)], name="inputs_")
print(self.inputs)
self.out_a = tf.placeholder(tf.int32, [None, ], name="out_a")
self.out_b = tf.placeholder(tf.int32, [None, ], name="out_b")
self.out_c = tf.placeholder(tf.int32, [None, ], name="out_c")
self.out_d = tf.placeholder(tf.int32, [None, ], name="out_d")
self.discounted_episode_rewards_norm = tf.placeholder(tf.float32, [None, ],
name="discounted_episode_rewards_")
self.avg_reward_ = tf.placeholder(tf.float32, name="avg_reward")
print(self.discounted_episode_rewards_norm)
# Add this placeholder for having this variable in tensorboard
self.mean_reward_ = tf.placeholder(tf.float32, name="mean_reward")
#model=k.Sequential()
#inp = Input(shape=self.state_size, name='stack_of_4_images')(self.inputs)
c1 = Conv2D(32,8,strides=2,activation='relu',name='Conv1')(self.inputs) #kernel_initializer=glorot_uniform(seed=42),
c2 = Conv2D(64, (6,8), strides=2, activation='relu', name='Conv2')(c1) # kernel_initializer=glorot_uniform(seed=42),
c3 = Conv2D(128, (6,8), strides=2, activation='relu', name='Conv3')(c2) # kernel_initializer=glorot_uniform(seed=42),
flat = Flatten()(c3)
d1 = Dense(1024, activation='selu', name='Dense1')(flat)
d2 = Dense(1024, activation='selu', name='Dense2')(d1)
logits_a = Dense(self.output_size, name='logits_a')(d2)
logits_b = Dense(self.output_size, name='logits_b')(d2)
logits_c = Dense(self.output_size, name='logits_c')(d2)
logits_d = Dense(self.output_size, name='logits_d')(d2)

        self.outputs_softmax_a= Softmax()(logits_a)
        self.outputs_softmax_b = Softmax()(logits_b)
        self.outputs_softmax_c = Softmax()(logits_c)
        self.outputs_softmax_d = Softmax()(logits_d)
        self.network_regularizer = flop_regularizer.GroupLassoFlopsRegularizer(
            [logits_a.op, logits_b.op, logits_c.op, logits_d.op], threshold=1e-3)
        self.exporter = structure_exporter.StructureExporter(
            self.network_regularizer.op_regularizer_manager)
        self.regularization_strength = 1e-10

        with tf.name_scope("loss"):
            neg_log_prob_a = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits_a, labels=self.out_a)
            neg_log_prob_b = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits_b, labels=self.out_b)
            neg_log_prob_c = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits_c, labels=self.out_c)
            neg_log_prob_d = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits_d, labels=self.out_d)

            loss_a = tf.reduce_mean(neg_log_prob_a * self.discounted_episode_rewards_norm)  # reward guided loss
            loss_b = tf.reduce_mean(neg_log_prob_b * self.discounted_episode_rewards_norm)  # reward guided loss
            loss_c = tf.reduce_mean(neg_log_prob_c * self.discounted_episode_rewards_norm)  # reward guided loss
            loss_d = tf.reduce_mean(neg_log_prob_d * self.discounted_episode_rewards_norm)  # reward guided loss
            self.regularizer_loss = (self.network_regularizer.get_regularization_term() *
                                     self.regularization_strength)

            self.loss = loss_a + loss_b + loss_c + loss_d + self.regularizer_loss
        with tf.name_scope("train"):
            #update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
            train_ops = tf.train.AdamOptimizer(self.lr).minimize(self.loss)
            self.train_op = tf.group([train_ops])#, update_ops])

        self.setup_writer()  # has to be before global variables initializer inside the graph for write_op to be
        # populated
        self.init = tf.global_variables_initializer()`

The error i get when i run my script that instantiates the model

File "/home/ubuntu/morph_basic/basic_bline.py", line 78, in build_model [logits_a.op, logits_b.op, logits_c.op, logits_d.op], threshold=1e-3) File "/home/ubuntu/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/morph_net/network_regularizers/flop_regularizer.py", line 153, in __init__ regularizer_blacklist=regularizer_blacklist) File "/home/ubuntu/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/morph_net/framework/op_regularizer_manager.py", line 126, in __init__ self._op_handler_dict[op.type].assign_grouping(op, self) File "/home/ubuntu/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/morph_net/framework/grouping_op_handler.py", line 40, in assign_grouping output_ops, op_reg_manager) File "/home/ubuntu/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/morph_net/framework/op_handler_util.py", line 73, in get_ops_without_groups op_slices = op_reg_manager.get_op_slices(op) File "/home/ubuntu/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/morph_net/framework/op_regularizer_manager.py", line 438, in get_op_slices if size > 0: TypeError: '>' not supported between instances of 'NoneType' and 'int'

[itabhiyanta]

i fixed this by using the suggestion in issue #26