def predict(self, x, batch_per_thread=None, distributed=False):
if isinstance(x, TFDataset):
# todo check arguments
x = _standarize_feature_dataset(x, self.model)
return self._predict_distributed(x)
else:
if distributed:
sc = getOrCreateSparkContext()
rdd, types, shapes = _create_rdd_x(
x, self.model._feed_input_names, sc)
dataset = TFDataset.from_rdd(
rdd,
names=self.model._feed_input_names,
types=types,
shapes=shapes,
batch_per_thread=-1
if batch_per_thread is None else batch_per_thread)
results = self._predict_distributed(dataset).collect()
output_num = len(self.model.outputs)
if output_num == 1:
return np.stack(results)
else:
predictions = []
for i in range(0, output_num):
predictions.append(
np.stack([res[i] for res in results]))
return predictions
else:
return self.model.predict(x=x, batch_size=batch_per_thread)
def predict(self, x, batch_per_thread=1, distributed=True):
"""
Use a model to do prediction.
"""
if isinstance(x, ImageSet):
results = callBigDlFunc(self.bigdl_type, "zooPredict",
self.value,
x,
batch_per_thread)
return ImageSet(results)
if distributed:
if isinstance(x, np.ndarray):
data_rdd = to_sample_rdd(x, np.zeros([x.shape[0]]), getOrCreateSparkContext())
elif isinstance(x, RDD):
data_rdd = x
else:
raise TypeError("Unsupported prediction data type: %s" % type(x))
results = callBigDlFunc(self.bigdl_type, "zooPredict",
self.value,
data_rdd,
batch_per_thread)
return results.map(lambda result: Layer.convert_output(result))
else:
if isinstance(x, np.ndarray) or isinstance(x, list):
results = callBigDlFunc(self.bigdl_type, "zooPredict",
self.value,
self._to_jtensors(x),
batch_per_thread)
return [Layer.convert_output(result) for result in results]
else:
raise TypeError("Unsupported prediction data type: %s" % type(x))
def from_ndarrays(tensors,
batch_size=-1,
batch_per_thread=-1,
hard_code_batch_size=False,
val_tensors=None,
sequential_order=False,
shuffle=True):
sc = getOrCreateSparkContext()
node_num, core_num = get_node_and_core_number()
total_core_num = node_num * core_num
rdd, tensor_structure = _tensors_to_rdd(tensors, sc, total_core_num)
val_rdd = None
if val_tensors is not None:
val_rdd, _ = _tensors_to_rdd(val_tensors, sc, total_core_num)
return TFNdarrayDataset(rdd,
tensor_structure,
batch_size,
batch_per_thread,
hard_code_batch_size,
val_rdd,
sequential_order=sequential_order,
shuffle=shuffle)
def evaluate(self,
x=None,
y=None,
batch_per_thread=None,
distributed=False):
if isinstance(x, TFDataset):
x = _standarize_feature_label_dataset(x, self.model)
# todo check arguments
return self._evaluate_distributed(x)
else:
if distributed:
sc = getOrCreateSparkContext()
rdd, types, shapes = _create_rdd_x_y(
x, y, self.model._feed_input_names,
self.model._feed_output_names, sc)
names = self.model._feed_input_names + self.model._feed_output_names
dataset = TFDataset.from_rdd(
rdd,
names=names,
types=types,
shapes=shapes,
batch_per_thread=-1
if batch_per_thread is None else batch_per_thread)
return self._evaluate_distributed(dataset)
else:
return self.model.evaluate(x=x,
y=y,
batch_size=batch_per_thread)
def __init__(self, file_path, parse_fn, batch_size,
batch_per_thread, hard_code_batch_size=False, validation_file_path=None):
import tensorflow as tf
g = tf.Graph()
with g.as_default():
serialized_example = tf.placeholder(dtype=tf.string, shape=[])
results = parse_fn(serialized_example)
flattened = nest.flatten(results)
output_names = [tf.cast(t, dtype=tf.float32).name for t in flattened]
serialized_graph = bytearray(g.as_graph_def().SerializeToString())
sc = getOrCreateSparkContext()
train_rdd = callBigDlFunc("float", "createRDDFromTFRecords",
file_path, sc, serialized_graph,
serialized_example.name, output_names)
validation_rdd = None
if validation_file_path is not None:
validation_rdd = callBigDlFunc("float", "createRDDFromTFRecords",
validation_file_path, sc, serialized_graph,
serialized_example.name, output_names)
tensor_structure = nest.pack_sequence_as(results,
[TensorMeta(tf.as_dtype(t.dtype),
shape=t.shape,
name="data_%s" % i)
for i, t in enumerate(nest.flatten(results))])
super(TFRecordDataset, self).__init__(tensor_structure, batch_size,
batch_per_thread, hard_code_batch_size)
self.train_rdd = train_rdd
self.validation_rdd = validation_rdd
def evaluate(self,
x=None,
y=None,
batch_per_thread=None,
distributed=False):
if isinstance(x, TFDataset):
if not x.has_batch:
raise ValueError("The batch_per_thread of TFDataset must be " +
"specified when used in KerasModel evaluate.")
x = _standarize_feature_label_dataset(x, self.model)
# todo check arguments
return self._evaluate_distributed(x)
else:
if distributed:
sc = getOrCreateSparkContext()
rdd, types, shapes = _create_rdd_x_y(
x, y, self.model._feed_input_names,
self.model._feed_output_names, sc)
names = self.model._feed_input_names + self.model._feed_output_names
dataset = TFDataset.from_rdd(
rdd,
names=names,
types=types,
shapes=shapes,
batch_per_thread=-1
if batch_per_thread is None else batch_per_thread)
return self._evaluate_distributed(dataset)
else:
return self.model.evaluate(x=x,
y=y,
batch_size=batch_per_thread)
def predict(self, x, batch_per_thread=1, distributed=True):
"""
Use a model to do prediction.
"""
if isinstance(x, ImageSet):
results = callZooFunc(self.bigdl_type, "zooPredict",
self.value,
x,
batch_per_thread)
return ImageSet(results)
if distributed:
if isinstance(x, np.ndarray):
data_rdd = to_sample_rdd(x, np.zeros([x.shape[0]]), getOrCreateSparkContext())
elif isinstance(x, RDD):
data_rdd = x
else:
raise TypeError("Unsupported prediction data type: %s" % type(x))
results = callZooFunc(self.bigdl_type, "zooPredict",
self.value,
data_rdd,
batch_per_thread)
return results.map(lambda result: Layer.convert_output(result))
else:
start_idx = 0
results = []
while start_idx < len(x):
end_idx = min(start_idx + batch_per_thread, len(x))
results.append(self.forward(x[start_idx:end_idx]))
start_idx += batch_per_thread
return np.concatenate(results, axis=0)
def predict(self,
x,
batch_per_thread=None,
distributed=False):
"""
Use a model to do prediction.
:param x: Input data. It could be:
- a TFDataset object
- A Numpy array (or array-like), or a list of arrays
(in case the model has multiple inputs).
- A dict mapping input names to the corresponding array/tensors,
if the model has named inputs.
:param batch_per_thread:
The default value is 1.
When distributed is True,the total batch size is batch_per_thread * rdd.getNumPartitions.
When distributed is False the total batch size is batch_per_thread * numOfCores.
:param distributed: Boolean. Whether to do prediction in distributed mode or local mode.
Default is True. In local mode, x must be a Numpy array.
"""
if isinstance(x, TFDataset):
# todo check arguments
if not x.has_batch:
raise ValueError("The batch_per_thread of TFDataset" +
" must be specified when used in KerasModel predict.")
if isinstance(x, TFNdarrayDataset):
x = _standarize_feature_dataset(x, self.model)
return self._predict_distributed(x)
else:
if distributed:
sc = getOrCreateSparkContext()
rdd, types, shapes = _create_rdd_x(x, self.model._feed_input_names, sc)
dataset = TFDataset.from_rdd(rdd,
names=self.model._feed_input_names,
types=types,
shapes=shapes,
batch_per_thread=-1 if batch_per_thread is None
else batch_per_thread)
results = self._predict_distributed(dataset).collect()
output_num = len(self.model.outputs)
if output_num == 1:
return np.stack(results)
else:
predictions = []
for i in range(0, output_num):
predictions.append(np.stack([res[i] for res in results]))
return predictions
else:
return self.model.predict(x=x,
batch_size=batch_per_thread)
def fit(self,
x=None,
y=None,
batch_size=None,
epochs=1,
validation_split=0.,
validation_data=None,
distributed=False,
**kwargs):
if isinstance(x, TFDataset):
# todo check arguments
if not x.has_batch:
raise ValueError("The batch_size of TFDataset must be " +
"specified when used in KerasModel fit.")
x = _standarize_feature_label_dataset(x, self.model)
self._fit_distributed(x, validation_split, epochs, **kwargs)
elif distributed:
sc = getOrCreateSparkContext()
train_rdd, types, shapes = _create_rdd_x_y(
x, y, self.model._feed_input_names,
self.model._feed_output_names, sc)
val_rdd = None
if validation_data is not None:
val_rdd, _, _ = _create_rdd_x_y(validation_data[0],
validation_data[1],
self.model._feed_input_names,
self.model._feed_output_names,
sc)
names = self.model._feed_input_names + self.model._feed_output_names
dataset = TFDataset.from_rdd(
train_rdd,
names=names,
shapes=shapes,
types=types,
batch_size=batch_size if batch_size is not None else 32,
val_rdd=val_rdd)
self._fit_distributed(dataset, validation_split, epochs, **kwargs)
else:
self.model.fit(x=x,
y=y,
batch_size=batch_size,
epochs=epochs,
validation_split=validation_split,
validation_data=validation_data,
**kwargs)
def from_ndarrays(tensors, batch_size=-1, batch_per_thread=-1,
hard_code_batch_size=False, val_tensors=None):
'''
Create a TFDataset from a nested structure of numpy ndarrays. Each element
in the resulting TFDataset has the same structure of the argument tensors and
is created by indexing on the first dimension of each ndarray in the tensors
argument.
This method is equivalent to sc.parallize the tensors and call TFDataset.from_rdd
:return:
'''
sc = getOrCreateSparkContext()
node_num, core_num = get_node_and_core_number()
total_core_num = node_num * core_num
rdd, tensor_structure = _tensors_to_rdd(tensors, sc, total_core_num)
val_rdd = None
if val_tensors is not None:
val_rdd, _ = _tensors_to_rdd(val_tensors, sc, total_core_num)
return TFNdarrayDataset(rdd, tensor_structure, batch_size,
batch_per_thread, hard_code_batch_size, val_rdd)
