示例#1
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 def get_loggers(self):
     loggers = {}
     labels = ['input', 'output', 'attn']
     for key in labels:
         loggers[key] = LazyRegisterer(
             os.path.join(self.logs_folder,
                          '{}_{}.png'.format(key, self.split)), 'image',
             'Samples {} {}'.format(key, self.split))
     return loggers
示例#2
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 def get_loggers(self, add_orientation, split):
     loggers = {}
     labels = ['input', 'gt_segmentation', 'output_segmentation']
     if add_orientation:
         labels.extend(['gt_orientation', 'output_orientation'])
     for name in labels:
         key = '{}_{}'.format(name, split)
         loggers[name] = LazyRegisterer(
             os.path.join(self.logs_folder, '{}.png'.format(key)), 'image',
             'Samples {} {}'.format(name, split))
     return loggers
示例#3
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def _get_plot_loggers(model_opt, train_opt):
    samples = {}
    _ssets = ['train', 'valid']
    for _set in _ssets:
        labels = ['input', 'output']
        for name in labels:
            key = '{}_{}'.format(name, _set)
            samples[key] = LazyRegisterer(
                os.path.join(logs_folder, '{}.png'.format(key)), 'image',
                'Samples {} {}'.format(name, _set))

    return samples
示例#4
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 def get_loggers(self):
   loggers = {}
   labels = ['input', 'output', 'total', 'box', 'patch', 'attn']
   if self.model_opt['add_d_out']:
     labels.append('d_in')
   if self.model_opt['add_y_out']:
     labels.append('y_in')
   for key in labels:
     loggers[key] = LazyRegisterer(
         os.path.join(self.logs_folder, '{}_{}.png'.format(key, self.split)),
         'image', 'Samples {} {}'.format(key, self.split))
   return loggers
示例#5
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def get_plot_loggers(model_opt, train_opt):
    samples = {}
    _ssets = ['train', 'valid']
    for _set in _ssets:
        labels = ['input', 'output']
        if model_opt['ctrl_rnn_inp_struct'] == 'attn':
            labels.append('attn')
        for name in labels:
            key = '{}_{}'.format(name, _set)
            samples[key] = LazyRegisterer(
                os.path.join(logs_folder, '{}.png'.format(key)), 'image',
                'Samples {} {}'.format(name, _set))

    return samples
示例#6
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def get_plot_loggers(model_opt, train_opt):
    samples = {}
    _ssets = ['train']
    if train_opt['has_valid']:
        _ssets.append('valid')
    for _set in _ssets:
        labels = ['input', 'output', 'total']
        if model_opt['type'] == 'attention':
            num_ctrl_cnn = len(model_opt['ctrl_cnn_filter_size'])
            num_attn_cnn = len(model_opt['attn_cnn_filter_size'])
            num_attn_dcnn = len(model_opt['attn_dcnn_filter_size'])
            labels.extend(['box', 'patch'])
            if model_opt['ctrl_rnn_inp_struct'] == 'attn':
                labels.append('attn')

        for name in labels:
            key = '{}_{}'.format(name, _set)
            samples[key] = LazyRegisterer(
                os.path.join(logs_folder, '{}.png'.format(key)), 'image',
                'Samples {} {}'.format(name, _set))

    return samples
示例#7
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                    'train batch mean', 'valid batch mean',
                    'train batch variance', 'valid batch variance', 'ema mean',
                    'ema variance'
                ],
                name='D-CNN {} batch norm stats'.format(ii),
                buffer_size=1)
            dcnn_bn_loggers.append(_dcnn_bn_logger)

        log_manager.register(log.filename, 'plain', 'Raw logs')

        model_opt_fname = os.path.join(logs_folder, 'model_opt.yaml')
        saver.save_opt(model_opt_fname, model_opt)
        log_manager.register(model_opt_fname, 'plain', 'Model hyperparameters')

        valid_sample_img = LazyRegisterer(
            os.path.join(logs_folder, 'valid_sample_img.png'), 'image',
            'Validation samples')
        train_sample_img = LazyRegisterer(
            os.path.join(logs_folder, 'train_sample_img.png'), 'image',
            'Training samples')
        log.info(
            ('Visualization can be viewed at: '
             'http://{}/deep-dashboard?id={}').format(args.localhost,
                                                      model_id))

    num_ex_train = dataset['train']['input'].shape[0]
    num_ex_valid = dataset['valid']['input'].shape[0]
    get_batch_train = _get_batch_fn(dataset['train'])
    get_batch_valid = _get_batch_fn(dataset['valid'])
    batch_size = args.batch_size
    log.info('Number of validation examples: {}'.format(num_ex_valid))