Source code for tensorpack.callbacks.graph

# -*- coding: utf-8 -*-
# File:

""" Graph related callbacks"""

import tensorflow as tf
import os
import numpy as np
from six.moves import zip

from ..utils import logger
from .base import Callback
from ..tfutils.common import get_op_tensor_name

__all__ = ['RunOp', 'RunUpdateOps', 'ProcessTensors', 'DumpTensors',
           'DumpTensor', 'DumpTensorAsImage', 'DumpParamAsImage']

[docs]class RunOp(Callback): """ Run an Op. """ _chief_only = False
[docs] def __init__(self, op, run_before=True, run_as_trigger=True, run_step=False, verbose=False): """ Args: op (tf.Operation or function): an Op, or a function that returns the Op in the graph. The function will be called after the main graph has been created (in the `setup_graph` callback). run_before (bool): run the Op before training run_as_trigger (bool): run the Op on every :meth:`trigger()` call. run_step (bool): run the Op every step (along with training) verbose (bool): print logs when the op is run. Example: The `DQN Example <>`_ uses this callback to update target network. """ if not callable(op): self.setup_func = lambda: op # noqa else: self.setup_func = op self.run_before = run_before self.run_as_trigger = run_as_trigger self.run_step = run_step self.verbose = verbose
def _setup_graph(self): self._op = self.setup_func() if self.run_step: self._fetch = tf.train.SessionRunArgs(fetches=self._op) def _before_train(self): if self.run_before: self._print() def _trigger(self): if self.run_as_trigger: self._print() def _before_run(self, _): if self.run_step: self._print() return self._fetch def _print(self): if self.verbose:"Running Op {} ...".format(
[docs]class RunUpdateOps(RunOp): """ Run ops from the collection UPDATE_OPS every step """
[docs] def __init__(self, collection=tf.GraphKeys.UPDATE_OPS): """ Args: collection (str): collection of ops to run. Defaults to ``tf.GraphKeys.UPDATE_OPS`` """ name = 'UPDATE_OPS' if collection == tf.GraphKeys.UPDATE_OPS else collection def f(): ops = tf.get_collection(collection) if ops:"Applying collection {} of {} ops.".format(name, len(ops))) return*ops, name='update_ops') else: return tf.no_op(name='empty_update_ops') super(RunUpdateOps, self).__init__( f, run_before=False, run_as_trigger=False, run_step=True)
[docs]class ProcessTensors(Callback): """ Fetch extra tensors **along with** each training step, and call some function over the values. It uses `_{before,after}_run` method to inject `tf.train.SessionRunHooks` to the session. You can use it to print tensors, save tensors to file, etc. Example: .. code-block:: python ProcessTensors(['mycost1', 'mycost2'], lambda c1, c2: print(c1, c2, c1 + c2)) """
[docs] def __init__(self, names, fn): """ Args: names (list[str]): names of tensors fn: a function taking all requested tensors as input """ assert isinstance(names, (list, tuple)), names self._names = names self._fn = fn
def _setup_graph(self): tensors = self.get_tensors_maybe_in_tower(self._names) self._fetch = tf.train.SessionRunArgs(fetches=tensors) def _before_run(self, _): return self._fetch def _after_run(self, _, rv): results = rv.results self._fn(*results)
[docs]class DumpTensors(ProcessTensors): """ Dump some tensors to a file. Every step this callback fetches tensors and write them to a npz file under ``logger.get_logger_dir``. The dump can be loaded by ``dict(np.load(filename).items())``. """
[docs] def __init__(self, names): """ Args: names (list[str]): names of tensors """ assert isinstance(names, (list, tuple)), names self._names = names dir = logger.get_logger_dir() def fn(*args): dic = {} for name, val in zip(self._names, args): dic[name] = val fname = os.path.join( dir, 'DumpTensor-{}.npz'.format(self.global_step)) np.savez(fname, **dic) super(DumpTensors, self).__init__(names, fn)
[docs]class DumpTensorAsImage(Callback): """ Dump a tensor to image(s) to ``logger.get_logger_dir()`` once triggered. Note that it requires the tensor is directly evaluable, i.e. either inputs are not its dependency (e.g. the weights of the model), or the inputs are feedfree (in which case this callback will take an extra datapoint from the input pipeline). """
[docs] def __init__(self, tensor_name, prefix=None, map_func=None, scale=255): """ Args: tensor_name (str): the name of the tensor. prefix (str): the filename prefix for saved images. Defaults to the Op name. map_func: map the value of the tensor to an image or list of images of shape [h, w] or [h, w, c]. If None, will use identity. scale (float): a multiplier on pixel values, applied after map_func. """ op_name, self.tensor_name = get_op_tensor_name(tensor_name) self.func = map_func if prefix is None: self.prefix = op_name else: self.prefix = prefix self.log_dir = logger.get_logger_dir() self.scale = scale
def _before_train(self): self._tensor = self.graph.get_tensor_by_name(self.tensor_name) def _trigger(self): val = if self.func is not None: val = self.func(val) if isinstance(val, list) or val.ndim == 4: for idx, im in enumerate(val): self._dump_image(im, idx) else: self._dump_image(val) self.trainer.monitors.put_image(self.prefix, val) def _dump_image(self, im, idx=None): assert im.ndim in [2, 3], str(im.ndim) fname = os.path.join( self.log_dir, self.prefix + '-ep{:03d}{}.png'.format( self.epoch_num, '-' + str(idx) if idx else '')) res = im * self.scale res = np.clip(res, 0, 255) cv2.imwrite(fname, res.astype('uint8'))
try: import cv2 except ImportError: from ..utils.develop import create_dummy_class DumpTensorAsImage = create_dummy_class('DumpTensorAsImage', 'cv2') # noqa # alias DumpParamAsImage = DumpTensorAsImage DumpTensor = DumpTensors