Python tensorpack.utils.logger.error() Examples

The following are 3 code examples of tensorpack.utils.logger.error(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorpack.utils.logger , or try the search function .
Example #1
Source File: load-resnet.py    From webvision-2.0-benchmarks with Apache License 2.0 5 votes vote down vote up 
def convert_param_name(param):
    resnet_param = {}
    for k, v in six.iteritems(param):
        try:
            newname = name_conversion(k)
        except Exception:
            logger.error("Exception when processing caffe layer {}".format(k))
            raise
        logger.info("Name Transform: " + k + ' --> ' + newname)
        resnet_param[newname] = v
    return resnet_param
Example #2
Source File: load-resnet.py    From tensorpack with Apache License 2.0 5 votes vote down vote up 
def convert_param_name(param):
    resnet_param = {}
    for k, v in six.iteritems(param):
        try:
            newname = name_conversion(k)
        except Exception:
            logger.error("Exception when processing caffe layer {}".format(k))
            raise
        logger.info("Name Transform: " + k + ' --> ' + newname)
        resnet_param[newname] = v
    return resnet_param
Example #3
Source File: shufflenet.py    From tensorpack with Apache License 2.0 5 votes vote down vote up 
def get_config(model, nr_tower):
    batch = TOTAL_BATCH_SIZE // nr_tower

    logger.info("Running on {} towers. Batch size per tower: {}".format(nr_tower, batch))
    dataset_train = get_data('train', batch)
    dataset_val = get_data('val', batch)

    step_size = 1280000 // TOTAL_BATCH_SIZE
    max_iter = 3 * 10**5
    max_epoch = (max_iter // step_size) + 1
    callbacks = [
        ModelSaver(),
        ScheduledHyperParamSetter('learning_rate',
                                  [(0, 0.5), (max_iter, 0)],
                                  interp='linear', step_based=True),
        EstimatedTimeLeft()
    ]
    infs = [ClassificationError('wrong-top1', 'val-error-top1'),
            ClassificationError('wrong-top5', 'val-error-top5')]
    if nr_tower == 1:
        # single-GPU inference with queue prefetch
        callbacks.append(InferenceRunner(QueueInput(dataset_val), infs))
    else:
        # multi-GPU inference (with mandatory queue prefetch)
        callbacks.append(DataParallelInferenceRunner(
            dataset_val, infs, list(range(nr_tower))))

    return TrainConfig(
        model=model,
        dataflow=dataset_train,
        callbacks=callbacks,
        steps_per_epoch=step_size,
        max_epoch=max_epoch,
    )