在win7 64位，Anaconda安装的Python3.6.1下安装的TensorFlow与Keras，Keras的backend为TensorFlow。在运行Mask R-CNN时，在进行调试时想知道PyCharm (Python IDE)底部窗口输出的Loss格式是在哪里定义的，如下图红框中所示：
图1 训练过程的Loss格式化输出
在上图红框中，Loss的输出格式是在哪里定义的呢？有一点是明确的，即上图红框中的内容是在训练的时候输出的。那么先来看一下Mask R-CNN的训练过程。Keras以Numpy数组作为输入数据和标签的数据类型。训练模型一般使用 fit 函数。然而由于Mask R-CNN训练数据巨大，不能一次性全部载入，否则太消耗内存。于是采用生成器的方式一次载入一个batch的数据，而且是在用到这个batch的数据才开始载入的，那么它的训练函数如下：
self.keras_model.fit_generator( train_generator, initial_epoch=self.epoch, epochs=epochs, steps_per_epoch=self.config.STEPS_PER_EPOCH, callbacks=callbacks, validation_data=val_generator, validation_steps=self.config.VALIDATION_STEPS, max_queue_size=100, workers=workers, use_multiprocessing=False, )
这里训练模型的函数相应的为 fit_generator 函数。注意其中的参数callbacks=callbacks，这个参数在输出红框中的内容起到了关键性的作用。下面看一下callbacks的值：
# Callbacks
callbacks = [
keras.callbacks.TensorBoard(log_dir=self.log_dir,
histogram_freq=0, write_graph=True, write_images=False),
keras.callbacks.ModelCheckpoint(self.checkpoint_path,
verbose=0, save_weights_only=True),
]
在输出红框中的内容所需的数据均保存在self.log_dir下。然后调试进入self.keras_model.fit_generator函数，进入keras,legacy.interfaces的legacy_support(func)函数，如下所示：
def legacy_support(func):
@six.wraps(func)
def wrapper(*args, **kwargs):
if object_type == 'class':
object_name = args[0].__class__.__name__
else:
object_name = func.__name__
if preprocessor:
args, kwargs, converted = preprocessor(args, kwargs)
else:
converted = []
if check_positional_args:
if len(args) > len(allowed_positional_args) + 1:
raise TypeError('`' + object_name +
'` can accept only ' +
str(len(allowed_positional_args)) +
' positional arguments ' +
str(tuple(allowed_positional_args)) +
', but you passed the following '
'positional arguments: ' +
str(list(args[1:])))
for key in value_conversions:
if key in kwargs:
old_value = kwargs[key]
if old_value in value_conversions[key]:
kwargs[key] = value_conversions[key][old_value]
for old_name, new_name in conversions:
if old_name in kwargs:
value = kwargs.pop(old_name)
if new_name in kwargs:
raise_duplicate_arg_error(old_name, new_name)
kwargs[new_name] = value
converted.append((new_name, old_name))
if converted:
signature = '`' + object_name + '('
for i, value in enumerate(args[1:]):
if isinstance(value, six.string_types):
signature += '"' + value + '"'
else:
if isinstance(value, np.ndarray):
str_val = 'array'
else:
str_val = str(value)
if len(str_val) > 10:
str_val = str_val[:10] + '...'
signature += str_val
if i < len(args[1:]) - 1 or kwargs:
signature += ', '
for i, (name, value) in enumerate(kwargs.items()):
signature += name + '='
if isinstance(value, six.string_types):
signature += '"' + value + '"'
else:
if isinstance(value, np.ndarray):
str_val = 'array'
else:
str_val = str(value)
if len(str_val) > 10:
str_val = str_val[:10] + '...'
signature += str_val
if i < len(kwargs) - 1:
signature += ', '
signature += ')`'
warnings.warn('Update your `' + object_name +
'` call to the Keras 2 API: ' + signature, stacklevel=2)
return func(*args, **kwargs)
wrapper._original_function = func
return wrapper
return legacy_support
在上述代码的倒数第4行的return func(*args, **kwargs)处返回func，func为fit_generator函数，现调试进入fit_generator函数，该函数定义在keras.engine.training模块内的fit_generator函数，调试进入函数callbacks.on_epoch_begin(epoch)，如下所示：
# Construct epoch logs.
epoch_logs = {}
while epoch < epochs:
for m in self.stateful_metric_functions:
m.reset_states()
callbacks.on_epoch_begin(epoch)
调试进入到callbacks.on_epoch_begin(epoch)函数，进入on_epoch_begin函数，如下所示：
def on_epoch_begin(self, epoch, logs=None):
"""Called at the start of an epoch.
# Arguments
epoch: integer, index of epoch.
logs: dictionary of logs.
"""
logs = logs or {}
for callback in self.callbacks:
callback.on_epoch_begin(epoch, logs)
self._delta_t_batch = 0.
self._delta_ts_batch_begin = deque([], maxlen=self.queue_length)
self._delta_ts_batch_end = deque([], maxlen=self.queue_length)
在上述函数on_epoch_begin中调试进入callback.on_epoch_begin(epoch, logs)函数，转到类ProgbarLogger(Callback)中定义的on_epoch_begin函数，如下所示：
class ProgbarLogger(Callback):
"""Callback that prints metrics to stdout.
# Arguments
count_mode: One of "steps" or "samples".
Whether the progress bar should
count samples seen or steps (batches) seen.
stateful_metrics: Iterable of string names of metrics that
should *not* be averaged over an epoch.
Metrics in this list will be logged as-is.
All others will be averaged over time (e.g. loss, etc).
# Raises
ValueError: In case of invalid `count_mode`.
"""
def __init__(self, count_mode='samples',
stateful_metrics=None):
super(ProgbarLogger, self).__init__()
if count_mode == 'samples':
self.use_steps = False
elif count_mode == 'steps':
self.use_steps = True
else:
raise ValueError('Unknown `count_mode`: ' + str(count_mode))
if stateful_metrics:
self.stateful_metrics = set(stateful_metrics)
else:
self.stateful_metrics = set()
def on_train_begin(self, logs=None):
self.verbose = self.params['verbose']
self.epochs = self.params['epochs']
def on_epoch_begin(self, epoch, logs=None):
if self.verbose:
print('Epoch %d/%d' % (epoch + 1, self.epochs))
if self.use_steps:
target = self.params['steps']
else:
target = self.params['samples']
self.target = target
self.progbar = Progbar(target=self.target,
verbose=self.verbose,
stateful_metrics=self.stateful_metrics)
self.seen = 0
在上述代码的
print('Epoch %d/%d' % (epoch + 1, self.epochs))
输出
Epoch 1/40（如红框中所示内容的第一行）。
然后返回到keras.engine.training模块内的fit_generator函数，执行到self.train_on_batch函数，如下所示：
outs = self.train_on_batch(x, y,
sample_weight=sample_weight,
class_weight=class_weight)
if not isinstance(outs, list):
outs = [outs]
for l, o in zip(out_labels, outs):
batch_logs[l] = o
callbacks.on_batch_end(batch_index, batch_logs)
batch_index += 1
steps_done += 1
调试进入上述代码中的callbacks.on_batch_end(batch_index, batch_logs)函数，进入到on_batch_end函数后，该函数的定义如下所示：
def on_batch_end(self, batch, logs=None):
"""Called at the end of a batch.
# Arguments
batch: integer, index of batch within the current epoch.
logs: dictionary of logs.
"""
logs = logs or {}
if not hasattr(self, '_t_enter_batch'):
self._t_enter_batch = time.time()
self._delta_t_batch = time.time() - self._t_enter_batch
t_before_callbacks = time.time()
for callback in self.callbacks:
callback.on_batch_end(batch, logs)
self._delta_ts_batch_end.append(time.time() - t_before_callbacks)
delta_t_median = np.median(self._delta_ts_batch_end)
if (self._delta_t_batch > 0. and
(delta_t_median > 0.95 * self._delta_t_batch and delta_t_median > 0.1)):
warnings.warn('Method on_batch_end() is slow compared '
'to the batch update (%f). Check your callbacks.'
% delta_t_median)
接着继续调试进入上述代码中的callback.on_batch_end(batch, logs)函数，进入到在类中ProgbarLogger(Callback)定义的on_batch_end函数，如下所示：
def on_batch_end(self, batch, logs=None):
logs = logs or {}
batch_size = logs.get('size', 0)
if self.use_steps:
self.seen += 1
else:
self.seen += batch_size
for k in self.params['metrics']:
if k in logs:
self.log_values.append((k, logs[k]))
# Skip progbar update for the last batch;
# will be handled by on_epoch_end.
if self.verbose and self.seen < self.target:
self.progbar.update(self.seen, self.log_values)
然后执行到上述代码的最后一行self.progbar.update(self.seen, self.log_values)，调试进入update函数，该函数定义在模块keras.utils.generic_utils中的类Progbar(object)定义的函数。类的定义及方法如下所示：
class Progbar(object):
"""Displays a progress bar.
# Arguments
target: Total number of steps expected, None if unknown.
width: Progress bar width on screen.
verbose: Verbosity mode, 0 (silent), 1 (verbose), 2 (semi-verbose)
stateful_metrics: Iterable of string names of metrics that
should *not* be averaged over time. Metrics in this list
will be displayed as-is. All others will be averaged
by the progbar before display.
interval: Minimum visual progress update interval (in seconds).
"""
def __init__(self, target, width=30, verbose=1, interval=0.05,
stateful_metrics=None):
self.target = target
self.width = width
self.verbose = verbose
self.interval = interval
if stateful_metrics:
self.stateful_metrics = set(stateful_metrics)
else:
self.stateful_metrics = set()
self._dynamic_display = ((hasattr(sys.stdout, 'isatty') and
sys.stdout.isatty()) or
'ipykernel' in sys.modules)
self._total_width = 0
self._seen_so_far = 0
self._values = collections.OrderedDict()
self._start = time.time()
self._last_update = 0
def update(self, current, values=None):
"""Updates the progress bar.
# Arguments
current: Index of current step.
values: List of tuples:
`(name, value_for_last_step)`.
If `name` is in `stateful_metrics`,
`value_for_last_step` will be displayed as-is.
Else, an average of the metric over time will be displayed.
"""
values = values or []
for k, v in values:
if k not in self.stateful_metrics:
if k not in self._values:
self._values[k] = [v * (current - self._seen_so_far),
current - self._seen_so_far]
else:
self._values[k][0] += v * (current - self._seen_so_far)
self._values[k][1] += (current - self._seen_so_far)
else:
# Stateful metrics output a numeric value. This representation
# means "take an average from a single value" but keeps the
# numeric formatting.
self._values[k] = [v, 1]
self._seen_so_far = current
now = time.time()
info = ' - %.0fs' % (now - self._start)
if self.verbose == 1:
if (now - self._last_update < self.interval and
self.target is not None and current < self.target):
return
prev_total_width = self._total_width
if self._dynamic_display:
sys.stdout.write('\b' * prev_total_width)
sys.stdout.write('\r')
else:
sys.stdout.write('\n')
if self.target is not None:
numdigits = int(np.floor(np.log10(self.target))) + 1
barstr = '%%%dd/%d [' % (numdigits, self.target)
bar = barstr % current
prog = float(current) / self.target
prog_width = int(self.width * prog)
if prog_width > 0:
bar += ('=' * (prog_width - 1))
if current < self.target:
bar += '>'
else:
bar += '='
bar += ('.' * (self.width - prog_width))
bar += ']'
else:
bar = '%7d/Unknown' % current
self._total_width = len(bar)
sys.stdout.write(bar)
if current:
time_per_unit = (now - self._start) / current
else:
time_per_unit = 0
if self.target is not None and current < self.target:
eta = time_per_unit * (self.target - current)
if eta > 3600:
eta_format = '%d:%02d:%02d' % (eta // 3600, (eta % 3600) // 60, eta % 60)
elif eta > 60:
eta_format = '%d:%02d' % (eta // 60, eta % 60)
else:
eta_format = '%ds' % eta
info = ' - ETA: %s' % eta_format
else:
if time_per_unit >= 1:
info += ' %.0fs/step' % time_per_unit
elif time_per_unit >= 1e-3:
info += ' %.0fms/step' % (time_per_unit * 1e3)
else:
info += ' %.0fus/step' % (time_per_unit * 1e6)
for k in self._values:
info += ' - %s:' % k
if isinstance(self._values[k], list):
avg = np.mean(
self._values[k][0] / max(1, self._values[k][1]))
if abs(avg) > 1e-3:
info += ' %.4f' % avg
else:
info += ' %.4e' % avg
else:
info += ' %s' % self._values[k]
self._total_width += len(info)
if prev_total_width > self._total_width:
info += (' ' * (prev_total_width - self._total_width))
if self.target is not None and current >= self.target:
info += '\n'
sys.stdout.write(info)
sys.stdout.flush()
elif self.verbose == 2:
if self.target is None or current >= self.target:
for k in self._values:
info += ' - %s:' % k
avg = np.mean(
self._values[k][0] / max(1, self._values[k][1]))
if avg > 1e-3:
info += ' %.4f' % avg
else:
info += ' %.4e' % avg
info += '\n'
sys.stdout.write(info)
sys.stdout.flush()
self._last_update = now
def add(self, n, values=None):
self.update(self._seen_so_far + n, values)
重点是上述代码中的update(self, current, values=None)函数，在该函数内设置断点，即可调入该函数。下面重点分析上述代码中的几个输出条目：
1. sys.stdout.write('\n') #换行
2. sys.stdout.write('bar') #输出 [..................]，其中bar= [..................]；
3. sys.stdout.write(info) #输出loss格式，其中info='- ETA:...'；
4. sys.stdout.flush() #刷新缓存，立即得到输出。
通过对Mask R-CNN代码的调试分析可知，图1中的红框中的训练过程中的Loss格式化输出是由built-in模块实现的。若想得到类似的格式化输出，关键在self.keras_model.fit_generator函数中传入callbacks参数和callbacks中内容的定义。
以上这篇基于Keras的格式化输出Loss实现方式就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。