Source code for tensorpack.train.interface

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

from ..compat import tfv1
from ..input_source import FeedInput, InputSource, QueueInput, StagingInput
from ..utils import logger
from ..compat import is_tfv2
from .config import TrainConfig
from .tower import SingleCostTrainer
from .trainers import SimpleTrainer

__all__ = ['launch_train_with_config']

def apply_default_prefetch(input_source_or_dataflow, trainer):
    Apply a set of default rules to make a fast :class:`InputSource`.

        input_source_or_dataflow(InputSource | DataFlow):
        trainer (Trainer):

    if not isinstance(input_source_or_dataflow, InputSource):
        # to mimic same behavior of the old trainer interface
        if type(trainer) == SimpleTrainer:
            input = FeedInput(input_source_or_dataflow)
  "Automatically applying QueueInput on the DataFlow.")
            input = QueueInput(input_source_or_dataflow)
        input = input_source_or_dataflow
    if hasattr(trainer, 'devices'):
        towers = trainer.devices
        if len(towers) > 1:  # seem to only help on >1 GPUs
            assert not isinstance(trainer, SimpleTrainer)

            if isinstance(input, QueueInput):
      "Automatically applying StagingInput on the DataFlow.")
                input = StagingInput(input)
    return input

[docs]def launch_train_with_config(config, trainer): """ Train with a :class:`TrainConfig` and a :class:`Trainer`, to present the simple and old training interface. It basically does the following 3 things (and you can easily do them by yourself if you need more control): 1. Setup the input with automatic prefetching heuristics, from `` or `config.dataflow`. 2. Call `trainer.setup_graph` with the input as well as `config.model`. 3. Call `trainer.train` with rest of the attributes of config. See the `related tutorial <>`_ to learn more. Args: config (TrainConfig): trainer (Trainer): an instance of :class:`SingleCostTrainer`. Example: .. code-block:: python launch_train_with_config( config, SyncMultiGPUTrainerParameterServer(8, ps_device='gpu')) """ if is_tfv2(): tfv1.disable_eager_execution() assert isinstance(trainer, SingleCostTrainer), trainer assert isinstance(config, TrainConfig), config assert config.model is not None assert config.dataflow is not None or is not None model = config.model input = or config.dataflow input = apply_default_prefetch(input, trainer) # This is the only place where the `ModelDesc` abstraction is useful. # We should gradually stay away from this unuseful abstraction. # TowerFunc is a better abstraction (similar to tf.function in the future) trainer.setup_graph( model.get_input_signature(), input, model.build_graph, model.get_optimizer) _check_unused_regularization() trainer.train_with_defaults( callbacks=config.callbacks, monitors=config.monitors, session_creator=config.session_creator, session_init=config.session_init, steps_per_epoch=config.steps_per_epoch, starting_epoch=config.starting_epoch, max_epoch=config.max_epoch, extra_callbacks=config.extra_callbacks)
def _check_unused_regularization(): coll = tfv1.get_collection(tfv1.GraphKeys.REGULARIZATION_LOSSES) unconsumed_reg = [] for c in coll: if len(c.consumers()) == 0: unconsumed_reg.append(c) if unconsumed_reg: logger.warn("The following tensors appear in REGULARIZATION_LOSSES collection but have no " "consumers! You may have forgotten to add regularization to total cost.") logger.warn("Unconsumed regularization: {}".format(', '.join([ for x in unconsumed_reg])))