Python parallel_reader.get_data_files方法代码示例

# 需要导入模块: from tensorflow.contrib.slim.python.slim.data import parallel_reader [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.data.parallel_reader import get_data_files [as 别名]
def print_config(flags, dataset, save_dir = None, print_to_file = True):
    def do_print(stream=None):
        print('\n# =========================================================================== #', file=stream)
        print('# Training flags:', file=stream)
        print('# =========================================================================== #', file=stream)
        pprint(flags.__flags, stream=stream)

        print('\n# =========================================================================== #', file=stream)
        print('# seglink net parameters:', file=stream)
        print('# =========================================================================== #', file=stream)
        vars = globals()
        for key in vars:
            var = vars[key]
            if util.dtype.is_number(var) or util.dtype.is_str(var) or util.dtype.is_list(var) or util.dtype.is_tuple(var):
                pprint('%s=%s'%(key, str(var)), stream = stream)
            
        print('\n# =========================================================================== #', file=stream)
        print('# Training | Evaluation dataset files:', file=stream)
        print('# =========================================================================== #', file=stream)
        data_files = parallel_reader.get_data_files(dataset.data_sources)
        pprint(sorted(data_files), stream=stream)
        print('', file=stream)
    do_print(None)
    
    if print_to_file:
        # Save to a text file as well.
        if save_dir is None:
            save_dir = flags.train_dir
            
        util.io.mkdir(save_dir)
        path = util.io.join_path(save_dir, 'training_config.txt')
        with open(path, "a") as out:
            do_print(out) 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.data import parallel_reader [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.data.parallel_reader import get_data_files [as 别名]
def _verify_all_data_sources_read(self, shared_queue):
    with self.test_session():
      tfrecord_paths = test_utils.create_tfrecord_files(
          self.get_temp_dir(), num_files=3)

    num_readers = len(tfrecord_paths)
    p_reader = parallel_reader.ParallelReader(
        io_ops.TFRecordReader, shared_queue, num_readers=num_readers)

    data_files = parallel_reader.get_data_files(tfrecord_paths)
    filename_queue = input_lib.string_input_producer(data_files)
    key, value = p_reader.read(filename_queue)

    count0 = 0
    count1 = 0
    count2 = 0

    num_reads = 50

    sv = supervisor.Supervisor(logdir=self.get_temp_dir())
    with sv.prepare_or_wait_for_session() as sess:
      sv.start_queue_runners(sess)

      for _ in range(num_reads):
        current_key, _ = sess.run([key, value])
        if '0-of-3' in str(current_key):
          count0 += 1
        if '1-of-3' in str(current_key):
          count1 += 1
        if '2-of-3' in str(current_key):
          count2 += 1

    self.assertGreater(count0, 0)
    self.assertGreater(count1, 0)
    self.assertGreater(count2, 0)
    self.assertEquals(count0 + count1 + count2, num_reads) 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.data import parallel_reader [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.data.parallel_reader import get_data_files [as 别名]
def print_configuration(flags, ssd_params, data_sources, save_dir=None):
    """Print the training configuration.
    """
    def print_config(stream=None):
        print('\n# =========================================================================== #', file=stream)
        print('# Training | Evaluation flags:', file=stream)
        print('# =========================================================================== #', file=stream)
        pprint(flags, stream=stream)

        print('\n# =========================================================================== #', file=stream)
        print('# SSD net parameters:', file=stream)
        print('# =========================================================================== #', file=stream)
        pprint(dict(ssd_params._asdict()), stream=stream)

        print('\n# =========================================================================== #', file=stream)
        print('# Training | Evaluation dataset files:', file=stream)
        print('# =========================================================================== #', file=stream)
        data_files = parallel_reader.get_data_files(data_sources)
        pprint(data_files, stream=stream)
        print('', file=stream)

    print_config(None)
    # Save to a text file as well.
    if save_dir is not None:
        if not os.path.exists(save_dir):
            os.makedirs(save_dir)
        path = os.path.join(save_dir, 'training_config.txt')
        with open(path, "w") as out:
            print_config(out) 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.data import parallel_reader [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.data.parallel_reader import get_data_files [as 别名]
def print_config(flags, dataset, save_dir = None, print_to_file = True):
    def do_print(stream=None):
        print(util.log.get_date_str(), file = stream)
        print('\n# =========================================================================== #', file=stream)
        print('# Training flags:', file=stream)
        print('# =========================================================================== #', file=stream)
        
        def print_ckpt(path):
            ckpt = util.tf.get_latest_ckpt(path)
            if ckpt is not None:
                print('Resume Training from : %s'%(ckpt), file = stream)
                return True
            return False
        
        if not print_ckpt(flags.train_dir):
            print_ckpt(flags.checkpoint_path)                
            
        pprint(flags.__flags, stream=stream)

        print('\n# =========================================================================== #', file=stream)
        print('# pixel_link net parameters:', file=stream)
        print('# =========================================================================== #', file=stream)
        vars = globals()
        for key in vars:
            var = vars[key]
            if util.dtype.is_number(var) or util.dtype.is_str(var) or util.dtype.is_list(var) or util.dtype.is_tuple(var):
                pprint('%s=%s'%(key, str(var)), stream = stream)
            
        print('\n# =========================================================================== #', file=stream)
        print('# Training | Evaluation dataset files:', file=stream)
        print('# =========================================================================== #', file=stream)
        data_files = parallel_reader.get_data_files(dataset.data_sources)
        pprint(sorted(data_files), stream=stream)
        print('', file=stream)
    do_print(None)
    
    if print_to_file:
        # Save to a text file as well.
        if save_dir is None:
            save_dir = flags.train_dir
            
        util.io.mkdir(save_dir)
        path = util.io.join_path(save_dir, 'training_config.txt')
        with open(path, "a") as out:
            do_print(out)