Source code for tensorpack.tfutils.varreplace

# -*- coding: utf-8 -*-
# File:
# Credit: Qinyao He

from contextlib import contextmanager

from ..utils import logger
from ..compat import tfv1 as tf
from .common import get_tf_version_tuple

__all__ = ['custom_getter_scope', 'freeze_variables', 'remap_variables']

[docs]@contextmanager def custom_getter_scope(custom_getter): """ Args: custom_getter: the same as in :func:`tf.get_variable` Returns: The current variable scope with a custom_getter. """ scope = tf.get_variable_scope() if get_tf_version_tuple() >= (1, 5): with tf.variable_scope( scope, custom_getter=custom_getter, auxiliary_name_scope=False): yield else: ns = tf.get_default_graph().get_name_scope() with tf.variable_scope( scope, custom_getter=custom_getter): with tf.name_scope(ns + '/' if ns else ''): yield
[docs]def remap_variables(fn): """ Use fn to map the output of any variable getter. Args: fn (tf.Variable -> tf.Tensor) Returns: The current variable scope with a custom_getter that maps all the variables by fn. Example: .. code-block:: python from tensorpack.tfutils import varreplace with varreplace.remap_variables(lambda var: quantize(var)): x = FullyConnected('fc', x, 1000) # fc/{W,b} will be quantized """ def custom_getter(getter, *args, **kwargs): v = getter(*args, **kwargs) return fn(v) return custom_getter_scope(custom_getter)
[docs]def freeze_variables(stop_gradient=True, skip_collection=False): """ Return a context to freeze variables, by wrapping ``tf.get_variable`` with a custom getter. It works by either applying ``tf.stop_gradient`` on the variables, or keeping them out of the ``TRAINABLE_VARIABLES`` collection, or both. Both options have their own pros and cons. Example: .. code-block:: python from tensorpack.tfutils import varreplace with varreplace.freeze_variable(stop_gradient=False, skip_collection=True): x = FullyConnected('fc', x, 1000) # fc/* will not be trained Args: stop_gradient (bool): if True, variables returned from `get_variable` will be wrapped with `tf.stop_gradient`. Note that the created variables may still have gradient when accessed by other approaches (e.g. by name, or by collection). For example, they may still have a gradient in weight decay. Also note that this makes `tf.get_variable` returns a Tensor instead of a Variable, which may break existing contract. Therefore, it's recommended to use the `skip_collection` option instead. skip_collection (bool): if True, do not add the variable to ``TRAINABLE_VARIABLES`` collection, but to ``MODEL_VARIABLES`` collection. As a result they will not be trained by default. Note: `stop_gradient` only stops variables returned by `get_variable` **within the context** to contribute no gradient in this context. Therefore it may not completely freeze the variables. For example: 1. If a variable is created, or reused outside of the context, it can still contribute to the gradient of other tensors. 2. If a freezed variable is accessed by other approaches (e.g., by names, by collections), it can still contribute to the gradient of other tensors. For example, weight decay cannot be stopped by a `stop_gradient` context. `skip_collection` has to be used the first time the variable is created. Once `skip_collection` is used, the variable is not a trainable variable anymore, and will be completely freezed from gradient update in tensorpack's single-cost trainer. Choose the option carefully depend on what you need. """ def custom_getter(getter, *args, **kwargs): trainable = kwargs.get('trainable', True) name = args[0] if len(args) else kwargs.get('name') if skip_collection: kwargs['trainable'] = False v = getter(*args, **kwargs) # do not perform unnecessary changes if it's not originally trainable # otherwise the variable may get added to MODEL_VARIABLES twice if trainable and skip_collection: if isinstance(v, tf.Variable): tf.add_to_collection(tf.GraphKeys.MODEL_VARIABLES, v) else: logger.warning(""" [freeze_variables] variable getter did not return a Variable, but '{}' instead, likely due to another custom getter. freeze_variables() work only if the other custom getter respects the `trainable` argument and don't put variables with `trainable=False` into TRAINABLE_VARIABLES collection. Please double check if this is true for the custom getter. """.format(str(v)).replace("\n", "")) if trainable and stop_gradient: v = tf.stop_gradient(v, name='freezed_' + name) return v return custom_getter_scope(custom_getter)