# morph-net

**Repository Path**: mirrors_google-research/morph-net

## Basic Information

- **Project Name**: morph-net
- **Description**: Fast & Simple Resource-Constrained Learning of Deep Network Structure
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-24
- **Last Updated**: 2026-03-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# MorphNet: Fast & Simple Resource-Constrained Learning of Deep Network Structure

[TOC]

## New: FiGS: Fine-Grained Stochastic Architecture Search
FiGS, is a probabilistic approach to channel regularization that we introduced
in [Fine-Grained Stochastic Architecture Search](https://arxiv.org/pdf/2006.09581.pdf).
It outperforms our previous regularizers and can be used as either a pruning algorithm or
a full fledged Differentiable Architecture Search method. This is the recommended
way to apply MorphNet. In the below documentation it is
referred to as the `LogisticSigmoid` regularizer.


## What is MorphNet?
MorphNet is a method for learning deep network structure during training. The
key principle is continuous relaxation of the network-structure learning
problem. In short, the MorphNet regularizer pushes the influence of filters down,
and once they are small enough, the corresponding output channels are marked
for removal from the network.


Specifically, activation sparsity is induced by adding regularizers
that target the consumption of specific resources such as FLOPs or model size.
When the regularizer loss is added to the training loss and their sum is
minimized via stochastic gradient descent or a similar optimizer, the learning
problem becomes a constrained optimization of the structure of the network,
under the constraint represented by the regularizer. The method was first
introduced in our [CVPR 2018](http://cvpr2018.thecvf.com/), paper "[MorphNet: Fast & Simple Resource-Constrained Learning of
Deep Network Structure](https://arxiv.org/abs/1711.06798)". A overview of the
approach as well as device-specific latency regularizers were prestend in
[GTC 2019](https://gputechconf2019.smarteventscloud.com/connect/sessionDetail.ww?SESSION_ID=272314).  [[slides](g3doc//MorphNet_GTC2019.pdf "GTC Slides"), recording: [YouTube](https://youtu.be/UvTXhTvJ_wM), [GTC on-demand](https://on-demand.gputechconf.com/gtc/2019/video/_/S9645/)].
Our new, probabilistic, approach to pruning is called FiGS, and is detailed in
[Fine-Grained Stochastic Architecture Search](https://arxiv.org/pdf/2006.09581.pdf).


## Usage

Suppose you have a working convolutional neural network for image classification
but want to shrink the model to satisfy some constraints (e.g., memory,
latency). Given an existing model (the “seed network”) and a target criterion,
MorphNet will propose a new model by adjusting the number of output channels in
each convolution layer.

Note that MorphNet does not change the topology of the network -- the proposed
model will have the same number of layers and connectivity pattern as the seed
network.

To use MorphNet, you must:

1.  Choose a regularizer from `morphnet.network_regularizers`. The choice is
    based on

    *   your target cost (e.g., FLOPs, latency)
    *   Your ability to add new layers to your model:
        * Add
          our probabilistic gating operation after any layer you wish to prune, and
          use the `LogisticSigmoid` regularizers. **\[recommended\]**
        * If you are unable to add new layers, select regularizer type based on
          your network architecture: use `Gamma` regularizer if the seed network
          has BatchNorm; use `GroupLasso` otherwise \[deprecated\].

    Note: If you use BatchNorm, you must enable the scale parameters (“gamma
    variables”), i.e., by setting `scale=True` if you are using
    `tf.keras.layers.BatchNormalization`.

    Note: If you are using `LogisticSigmoid` don't forget to add the
    probabilistic gating op! See below for example.

2.  Initialize the regularizer with a threshold and the output boundary ops and
    (optionally) the input boundary ops of your model.

    MorphNet regularizer crawls your graph starting from the output boundary,
    and applies regularization to some of the ops it encounters. When it
    encounters any of the input boundary ops, it does not crawl past them (the
    ops in the input boundary are not regularized). The threshold determines
    which output channels can be eliminated.

3.  Add the regularization term to your loss.

    As always, regularization loss must be scaled. We recommend to search for
    the scaling hyperparameter (*regularization strength*) along a logarithmic
    scale spanning a few orders of magnitude around `1/(initial cost)`. For
    example, if the seed network starts with 1e9 FLOPs, explore regularization
    strength around 1e-9.

    Note: MorphNet does not currently add the regularization loss to the
    tf.GraphKeys.REGULARIZATION_LOSSES collection; this choice is subject to
    revision.

    Note: Do not confuse `get_regularization_term()` (the loss you should add to
    your training) with `get_cost()` (the estimated cost of the network if the
    proposed structure is applied).

4.  Train the model.

    Note: We recommend using a fixed learning rate (no decay) for this step,
    though this is not strictly necessary.

5.  Save the proposed model structure with the `StructureExporter`.

    The exported files are in JSON format. Note that as the training progresses,
    the proposed model structure will change. There are no specific guidelines
    on the stopping time, although you would likely want to wait for the
    regularization loss (reported via summaries) to stabilize.

6.  (Optional) Create summary ops to monitor the training progress through
    TensorBoard.

7.  Modify your model using the `StructureExporter` output.

8.  Retrain the model from scratch without the MorphNet regularizer.

    Note: Use the standard values for all hyperparameters (such as the learning
    rate schedule).

9.  (Optional) Uniformly expand the network to adjust the accuracy vs. cost
    trade-off as desired. Alternatively, this step can be performed *before*
    the structure learning step.

We refer to the first round of training as *structure learning* and the second
round as *retraining*.

To summarize, the key hyperparameters for MorphNet are:

*   Regularization strength
*   Alive threshold

Note that the regularizer type is not a hyperparameter because it's uniquely
determined by the metric of interest (FLOPs, latency) and the presence of
BatchNorm.

## Regularizer Types

Regularizer classes can be found under `network_regularizers/` directory. They
are named by the algorithm they use and the target cost they attempt to
minimize. For example, `LogisticSigmoidFlopsRegularizer` uses a
Logistic-Sigmoid probabilistic method to to regularize the FLOP cost
and `GammaModelSizeRegularizer` uses the batch norm gamma in
order to regularize the model size cost.

### Regularizer Algorithms

* **[NEW] LogisticSigmoid** is designed to control any model type, but requires
  adding simple `gating layers` to your model.
* **GroupLasso** is designed for models without batch norm.
* **Gamma** is designed for models with batch norm; it requires that batch
   norm scale is enabled.

### Regularizer Target Costs

* *Flops* targets the FLOP count of the inference network.
* *Model Size* targets the number of weights of the network.
* *Latency* optimizes for the estimated inference latency of the network, based
on the specific hardware characteristics.

## Examples

### Adding a FLOPs Regularizer

The example below demonstrates how to use MorphNet to reduce the number of FLOPs
in your model. In this example, the regularizer will traverse the graph
starting with `logits`, and will not go past any op that is earlier in the graph
than the `inputs` or `labels`; this allows to specify the subgraph for MorphNet to optimize.

<!-- TODO Add Keras example. -->
```python
from morph_net.network_regularizers import flop_regularizer
from morph_net.tools import structure_exporter

def build_model(inputs, labels, is_training, ...):
  gated_relu = activation_gating.gated_relu_activation()

  net = tf.layers.conv2d(inputs, kernel=[5, 5], num_outputs=256)
  net = gated_relu(net, is_training=is_training)

  ...
  ...

  net = tf.layers.conv2d(net, kernel=[3, 3], num_outputs=1024)
  net = gated_relu(net, is_training=is_training)

  logits = tf.reduce_mean(net, [1, 2])
  logits = tf.layers.dense(logits, units=1024)
  return logits

inputs, labels = preprocessor()
logits = build_model(inputs, labels, is_training=True, ...)

network_regularizer = flop_regularizer.LogisticSigmoidFlopsRegularizer(
    output_boundary=[logits.op],
    input_boundary=[inputs.op, labels.op],
    alive_threshold=0.1  # Value in [0, 1]. This default works well for most cases.
)
regularization_strength = 1e-10
regularizer_loss = (network_regularizer.get_regularization_term() * regularization_strength)

model_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels, logits)

optimizer = tf.train.MomentumOptimizer(learning_rate=0.01, momentum=0.9)

train_op = optimizer.minimize(model_loss + regularizer_loss)
```

You should monitor the progress of structure learning training via Tensorboard.
In particular, you should consider adding a summary that computes the current
MorphNet regularization loss and the cost if the currently proposed structure is
adopted.

```python
tf.summary.scalar('RegularizationLoss', regularizer_loss)
tf.summary.scalar(network_regularizer.cost_name, network_regularizer.get_cost())
```

![TensorBoardDisplayOfFlops](g3doc/tensorboard.png "Example of the TensorBoard display of the resource regularized by MorphNet.")

Larger values of `regularization_strength` will converge to smaller effective
FLOP count. If `regularization_strength` is large enough, the FLOP count will
collapse to zero. Conversely, if it is small enough, the FLOP count will remain
at its initial value and the network structure will not vary. The
`regularization_strength` parameter is your knob to control where you want to be
on the price-performance curve. The `alive_threshold` parameter is used for
determining when an activation is alive.

### Extracting the Architecture Learned by MorphNet

During training, you should save a JSON file that contains the learned structure
of the network, that is the count of activations in a given layer kept alive (as
opposed to removed) by MorphNet.

```python
exporter = structure_exporter.StructureExporter(
    network_regularizer.op_regularizer_manager)

with tf.Session() as sess:
  tf.global_variables_initializer().run()
  for step in range(max_steps):
    _, structure_exporter_tensors = sess.run([train_op, exporter.tensors])
    if (step % 1000 == 0):
      exporter.populate_tensor_values(structure_exporter_tensors)
      exporter.create_file_and_save_alive_counts(train_dir, step)
```

## Misc

Contact: morphnet@google.com

### Maintainers

*   Elad Eban, github: [eladeban](https://github.com/eladeban)
*   Andrew Poon, github: [ayp-google](https://github.com/ayp-google)
*   Yair Movshovitz-Attias, github: [yairmov](https://github.com/yairmov)
*   Max Moroz, github: [pkch](https://github.com/pkch)

### Contributors

*   Ariel Gordon, github: [gariel-google](https://github.com/gariel-google).