def _create_placeholders(self):
import tensorflow as tf
if not self.hard_code_batch_size:
tensors = nest.pack_sequence_as(self.tensor_structure, [
tf.placeholder(
name=t.name, dtype=t.dtype, shape=[None] + list(t.shape))
for t in nest.flatten(self.tensor_structure)
])
else:
if self.batch_per_thread > 0:
tensors = nest.pack_sequence_as(self.tensor_structure, [
tf.placeholder(
name=t.name,
dtype=t.dtype,
shape=[self.batch_per_thread] + list(t.shape))
for t in nest.flatten(self.tensor_structure)
])
else:
tensors = nest.pack_sequence_as(self.tensor_structure, [
tf.placeholder(
name=t.name,
dtype=t.dtype,
shape=[self.batch_size // self.total_core_num] +
list(t.shape))
for t in nest.flatten(self.tensor_structure)
])
for tensor in nest.flatten(tensors):
tf.get_default_graph().clear_collection(tensor.name)
tf.add_to_collection(tensor.name, self)
return tensors
def _tensors_to_rdd(tensors, sc, splits):
import tensorflow as tf
if isinstance(tensors, np.ndarray):
tensors = (tensors,)
if isinstance(tensors, list):
for i in range(len(tensors)):
if tensors[i].dtype == np.dtype("float64"):
tensors[i] = np.float32(tensors[i])
data_list = _splits(tensors)
rdd = sc.parallelize(data_list, splits)
tensor_structure = [TensorMeta(tf.as_dtype(t.dtype),
shape=t.shape[1:],
name="input_%s" % i)
for i, t in enumerate(tensors)]
else:
flattened = nest.flatten(tensors)
for i in range(len(flattened)):
if flattened[i].dtype == np.dtype("float64"):
flattened[i] = np.float32(flattened[i])
data_list = _splits(flattened)
rdd = sc.parallelize(data_list, splits)
rdd = rdd.map(lambda x: nest.pack_sequence_as(tensors, x))
tensor_structure = nest.pack_sequence_as(tensors,
[TensorMeta(tf.as_dtype(t.dtype),
shape=t.shape[1:],
name="input_%s" % i)
for i, t in enumerate(flattened)])
return rdd, tensor_structure
def __init__(self, rdd, tensor_structure, batch_size,
batch_per_thread, hard_code_batch_size=False, val_rdd=None):
'''
TFDatasets represents a distributed collection of elements to be feed into Tensorflow
graph. TFDatasets can be created using a RDD and each of its records is one or more
numpy.ndarray of the same nested structure, representing the tensors to be feed into
TensorFlow graph on each iteration. TFDatasets must be used with TFOptimizer or
TFPredictor.
'''
if batch_size > 0 and batch_per_thread > 0:
raise ValueError("bath_size and batch_per_thread should not be set simultaneously")
self.has_batch = True
node_num, core_num = get_node_and_core_number()
self.total_core_num = node_num * core_num
if batch_size > 0:
if batch_size % self.total_core_num != 0:
raise ValueError("batch_size should be a multiple " +
"of total core number, but got batch_size: " +
"%s where total core number is %s" % (batch_size,
self.total_core_num))
if batch_size <= 0 and batch_per_thread <= 0:
batch_per_thread = 1
batch_size = self.total_core_num
self.has_batch = False
self.batch_size = batch_size
self.batch_per_thread = batch_per_thread
self.hard_code_batch_size = hard_code_batch_size
self.tensor_structure = tensor_structure
self.val_rdd = val_rdd
if not self.hard_code_batch_size:
self.output_shapes = nest.pack_sequence_as(
self.tensor_structure, [[None] + list(t.shape)
if t is not None else None
for t in nest.flatten(self.tensor_structure)])
else:
if self.batch_per_thread > 0:
self.output_shapes = nest.pack_sequence_as(
self.tensor_structure, [[self.batch_per_thread] + t.shape
if t is not None else None
for t in nest.flatten(self.tensor_structure)])
else:
self.output_shapes = nest.pack_sequence_as(
self.tensor_structure, [[self.batch_size // self.total_core_num] + t.shape
if t is not None else None
for t in nest.flatten(self.tensor_structure)])
self.rdd = rdd
self.input_names = nest.pack_sequence_as(
self.tensor_structure, [t.name
if t is not None else None
for t in nest.flatten(self.tensor_structure)])
self._tensors = None
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 partition(data, num_shards=None):
"""
Partition local in memory data and form a SparkXShards
:param data: np.ndarray, a tuple, list, dict of np.ndarray, or a nested structure
made of tuple, list, dict with ndarray as the leaf value
:param num_shards: the number of shards that the data will be partitioned into
:return: a SparkXShards
"""
sc = init_nncontext()
node_num, core_num = get_node_and_core_number()
shard_num = node_num * core_num if num_shards is None else num_shards
import numpy as np
type_err_msg = """
The types supported in zoo.orca.data.XShards.partition are
1. np.ndarray
2. a tuple, list, dict of np.ndarray
3. nested structure made of tuple, list, dict with ndarray as the leaf value
But got data of type {}
""".format(type(data))
supported_types = {list, tuple, dict}
if isinstance(data, np.ndarray):
if data.shape[0] < shard_num:
raise ValueError(
"The length of data {} is smaller than the total number "
"of shards {}. Please adjust the num_shards option to be "
"at most {}.".format(data.shape[0], shard_num,
data.shape[0]))
arrays = np.array_split(data, shard_num)
rdd = sc.parallelize(arrays)
else:
assert type(data) in supported_types, type_err_msg
flattened = nest.flatten(data)
data_length = len(flattened[0])
data_to_be_shard = []
if data_length < shard_num:
raise ValueError(
"The length of data {} is smaller than the total number "
"of shards {}. Please adjust the num_shards option to be "
"at most {}.".format(data_length, shard_num, data_length))
for i in range(shard_num):
data_to_be_shard.append([])
for x in flattened:
assert len(x) == data_length, \
"the ndarrays in data must all have the same size in first dimension, " \
"got first ndarray of size {} and another {}".format(data_length, len(x))
x_parts = np.array_split(x, shard_num)
for idx, x_part in enumerate(x_parts):
data_to_be_shard[idx].append(x_part)
data_to_be_shard = [
nest.pack_sequence_as(data, shard)
for shard in data_to_be_shard
]
rdd = sc.parallelize(data_to_be_shard)
data_shards = SparkXShards(rdd)
return data_shards
def _tensors_to_rdd(tensors, sc, splits):
import tensorflow as tf
if isinstance(tensors, list):
data_list = _splits(tensors)
rdd = sc.parallelize(data_list, splits)
tensor_structure = [TensorMeta(tf.as_dtype(t.dtype),
shape=t.shape[1:],
name="input_%s" % i)
for i, t in enumerate(tensors)]
else:
flattened = nest.flatten(tensors)
data_list = _splits(flattened)
rdd = sc.parallelize(data_list, splits)
rdd = rdd.map(lambda x: nest.pack_sequence_as(tensors, x))
tensor_structure = nest.pack_sequence_as(tensors,
[TensorMeta(tf.as_dtype(t.dtype),
shape=t.shape[1:],
name="input_%s" % i)
for i, t in enumerate(flattened)])
return rdd, tensor_structure
def to_dataset(iter):
data_list = list(iter)
import tensorflow as tf
if not data_list:
return []
datasets = [create_dataset_fn(data) for data in data_list]
from functools import reduce
dataset = reduce(lambda x, y: x.concatenate(y), datasets)
dataset = dataset.batch(batch_per_shard, drop_remainder)
iterator = dataset.make_initializable_iterator()
train_next_ops = nest.flatten(iterator.get_next())
output_types = [
t.as_datatype_enum for t in nest.flatten(dataset.output_types)
]
init_op_name = iterator.initializer.name
table_init_op = tf.tables_initializer().name
output_names = [op.name for op in train_next_ops]
graph = train_next_ops[0].graph
flatten_shapes = nest.flatten(dataset.output_shapes)
flatten_shapes = [shape[1:] for shape in flatten_shapes]
flatten_tensor_structure = [
TensorMeta(dtype=output_types[i],
shape=list(flatten_shapes[i]),
name="zoo_input_{}".format(i))
for i in range(len(flatten_shapes))
]
structure = dataset.output_types
if isinstance(structure, tf.DType):
structure = (structure, )
tensor_structure = nest.pack_sequence_as(structure,
flatten_tensor_structure)
meta_info = {
"init_op_name": init_op_name,
"table_init_op": table_init_op,
"output_names": output_names,
"output_types": output_types,
"tensor_structure": tensor_structure
}
return [(bytearray(graph.as_graph_def().SerializeToString()),
meta_info)]
def partition(data):
"""
Partition local in memory data and form a SparkXShards
:param data: np.ndarray, a tuple, list, dict of np.ndarray, or a nested structure
made of tuple, list, dict with ndarray as the leaf value
:return: a SparkXShards
"""
sc = init_nncontext()
node_num, core_num = get_node_and_core_number()
total_core_num = node_num * core_num
import numpy as np
type_err_msg = """
The types supported in zoo.orca.data.XShards.partition are
1. np.ndarray
2. a tuple, list, dict of np.ndarray
3. nested structure made of tuple, list, dict with ndarray as the leaf value
But got data of type {}
""".format(type(data))
supported_types = {list, tuple, dict}
if isinstance(data, np.ndarray):
arrays = np.array_split(data, total_core_num)
rdd = sc.parallelize(arrays)
else:
assert type(data) in supported_types, type_err_msg
flattened = nest.flatten(data)
data_length = len(flattened[0])
data_to_be_shard = []
for i in range(total_core_num):
data_to_be_shard.append([])
for x in flattened:
assert len(x) == data_length, \
"the ndarrays in data must all have the same size in first dimension, " \
"got first ndarray of size {} and another {}".format(data_length, len(x))
x_parts = np.array_split(x, total_core_num)
for idx, x_part in enumerate(x_parts):
data_to_be_shard[idx].append(x_part)
data_to_be_shard = [
nest.pack_sequence_as(data, shard)
for shard in data_to_be_shard
]
rdd = sc.parallelize(data_to_be_shard)
data_shards = SparkXShards(rdd)
return data_shards
def __init__(self, tensor_structure, batch_size,
batch_per_thread, hard_code_batch_size=False):
'''
TFDataset represents a distributed collection of elements (backed by a RDD)
to be feed into Tensorflow graph.
:param tensor_structure: a nested structure of TensorMeta objects specifying the
name, shape and data type of each element in this TFDataset
:param batch_size: the batch size, used for training, should be a multiple of
total core num
:param batch_per_thread: the batch size for each thread, used for inference or evaluation
:param hard_code_batch_size: whether to hard code the batch_size into tensorflow graph,
if True, the static size of the first dimension of the resulting tensors is
batch_size/total_core_num (training) or batch_per_thread for inference; if False,
it is None.
'''
if batch_size > 0 and batch_per_thread > 0:
raise ValueError("bath_size and batch_per_thread should not be set simultaneously")
self.has_batch = True
node_num, core_num = get_node_and_core_number()
self.total_core_num = node_num * core_num
if batch_size > 0:
if batch_size % self.total_core_num != 0:
raise ValueError("batch_size should be a multiple " +
"of total core number, but got batch_size: " +
"%s where total core number is %s" % (batch_size,
self.total_core_num))
if batch_size <= 0 and batch_per_thread <= 0:
batch_per_thread = 1
batch_size = self.total_core_num
self.has_batch = False
self.batch_size = batch_size
self.batch_per_thread = batch_per_thread
self.hard_code_batch_size = hard_code_batch_size
self.tensor_structure = tensor_structure
if not self.hard_code_batch_size:
self.output_shapes = nest.pack_sequence_as(
self.tensor_structure, [[None] + list(t.shape)
if t is not None else None
for t in nest.flatten(self.tensor_structure)])
else:
if self.batch_per_thread > 0:
self.output_shapes = nest.pack_sequence_as(
self.tensor_structure, [[self.batch_per_thread] + t.shape
if t is not None else None
for t in nest.flatten(self.tensor_structure)])
else:
self.output_shapes = nest.pack_sequence_as(
self.tensor_structure, [[self.batch_size // self.total_core_num] + t.shape
if t is not None else None
for t in nest.flatten(self.tensor_structure)])
self.input_names = nest.pack_sequence_as(
self.tensor_structure, [t.name
if t is not None else None
for t in nest.flatten(self.tensor_structure)])
self._tensors = None
def predict(self, data, **kwargs):
def predict_transform(dict_data, batch_size):
assert isinstance(dict_data, dict), "each shard should be an dict"
assert "x" in dict_data, "key x should in each shard"
feature_data = dict_data["x"]
if isinstance(feature_data, np.ndarray):
assert feature_data.shape[1] <= batch_size, \
"The batch size of input data (the second dim) should be less than the model " \
"batch size, otherwise some inputs will be ignored."
elif isinstance(feature_data, list):
for elem in feature_data:
assert isinstance(elem, np.ndarray), "Each element in the x list should be " \
"a ndarray, but get " + \
elem.__class__.__name__
assert elem.shape[1] <= batch_size, "The batch size of each input data (the " \
"second dim) should be less than the " \
"model batch size, otherwise some inputs " \
"will be ignored."
else:
raise ValueError(
"x in each shard should be a ndarray or a list of ndarray."
)
return dict_data["x"]
sc = init_nncontext()
if isinstance(data, SparkXShards):
assert sc is not None, "You should pass sc(spark context) if data is a XShards."
from zoo.orca.learn.utils import convert_predict_to_xshard
data = data.transform_shard(predict_transform, self.batch_size)
result_rdd = self.model.distributed_predict(data.rdd, sc)
return convert_predict_to_xshard(result_rdd)
elif isinstance(data, (np.ndarray, list)):
total_core_num = self.core_num * self.node_num
if isinstance(data, np.ndarray):
assert data.shape[1] <= self.batch_size, "The batch size of input data (the " \
"second dim) should be less than the " \
"model batch size, otherwise some " \
"inputs will be ignored."
split_num = min(total_core_num, data.shape[0])
arrays = np.array_split(data, split_num)
data_rdd = sc.parallelize(arrays, numSlices=split_num)
elif isinstance(data, list):
flattened = nest.flatten(data)
data_length = len(flattened[0])
data_to_be_rdd = []
split_num = min(total_core_num, flattened[0].shape[0])
for i in range(split_num):
data_to_be_rdd.append([])
for x in flattened:
assert isinstance(x, np.ndarray), "the data in the data list should be " \
"ndarrays, but get " + \
x.__class__.__name__
assert len(x) == data_length, \
"the ndarrays in data must all have the same size in first dimension" \
", got first ndarray of size {} and another {}".format(data_length, len(x))
assert x.shape[1] <= self.batch_size, "The batch size of each input data (" \
"the second dim) should be less than " \
"the model batch size, otherwise some " \
"inputs will be ignored."
x_parts = np.array_split(x, split_num)
for idx, x_part in enumerate(x_parts):
data_to_be_rdd[idx].append(x_part)
data_to_be_rdd = [
nest.pack_sequence_as(data, shard)
for shard in data_to_be_rdd
]
data_rdd = sc.parallelize(data_to_be_rdd, numSlices=split_num)
result_rdd = self.model.distributed_predict(data_rdd, sc)
result_arr_list = result_rdd.collect()
result_arr = np.concatenate(result_arr_list, axis=0)
return result_arr
else:
raise ValueError(
"Only XShards, a numpy array and a list of numpy arrays are supported "
"as input data, but get " + data.__class__.__name__)
def predict(self, data, feature_cols=None):
"""
Predict input data
:param data: data to be predicted. XShards, Spark DataFrame, numpy array and list of numpy
arrays are supported. If data is XShards, each partition is a dictionary of {'x':
feature}, where feature(label) is a numpy array or a list of numpy arrays.
:param feature_cols: Feature column name(s) of data. Only used when data is a Spark
DataFrame. Default: None.
:return: predicted result.
If the input data is XShards, the predict result is a XShards, each partition
of the XShards is a dictionary of {'prediction': result}, where the result is a
numpy array or a list of numpy arrays.
If the input data is numpy arrays or list of numpy arrays, the predict result is
a numpy array or a list of numpy arrays.
"""
from pyspark.sql import DataFrame
def predict_transform(dict_data, batch_size):
assert isinstance(dict_data, dict), "each shard should be an dict"
assert "x" in dict_data, "key x should in each shard"
feature_data = dict_data["x"]
if isinstance(feature_data, np.ndarray):
assert feature_data.shape[0] <= batch_size, \
"The batch size of input data (the second dim) should be less than the model " \
"batch size, otherwise some inputs will be ignored."
elif isinstance(feature_data, list):
for elem in feature_data:
assert isinstance(elem, np.ndarray), "Each element in the x list should be " \
"a ndarray, but get " + \
elem.__class__.__name__
assert elem.shape[0] <= batch_size, "The batch size of each input data (the " \
"second dim) should be less than the " \
"model batch size, otherwise some inputs " \
"will be ignored."
else:
raise ValueError(
"x in each shard should be a ndarray or a list of ndarray."
)
return feature_data
sc = init_nncontext()
if isinstance(data, DataFrame):
from zoo.orca.learn.utils import dataframe_to_xshards, convert_predict_rdd_to_dataframe
xshards, _ = dataframe_to_xshards(data,
validation_data=None,
feature_cols=feature_cols,
label_cols=None,
mode="predict")
transformed_data = xshards.transform_shard(predict_transform,
self.batch_size)
result_rdd = self.model.distributed_predict(
transformed_data.rdd, sc)
def delete_useless_result(data):
shard, y = data
data_length = len(shard["x"])
return y[:data_length]
result_rdd = xshards.rdd.zip(result_rdd).map(delete_useless_result)
return convert_predict_rdd_to_dataframe(
data, result_rdd.flatMap(lambda data: data))
elif isinstance(data, SparkXShards):
transformed_data = data.transform_shard(predict_transform,
self.batch_size)
result_rdd = self.model.distributed_predict(
transformed_data.rdd, sc)
def update_shard(data):
shard, y = data
data_length = len(shard["x"])
shard["prediction"] = y[:data_length]
return shard
return SparkXShards(data.rdd.zip(result_rdd).map(update_shard))
elif isinstance(data, (np.ndarray, list)):
if isinstance(data, np.ndarray):
split_num = math.ceil(len(data) / self.batch_size)
arrays = np.array_split(data, split_num)
data_length_list = list(map(lambda arr: len(arr), arrays))
data_rdd = sc.parallelize(arrays, numSlices=split_num)
elif isinstance(data, list):
flattened = nest.flatten(data)
data_length = len(flattened[0])
data_to_be_rdd = []
split_num = math.ceil(flattened[0].shape[0] / self.batch_size)
for i in range(split_num):
data_to_be_rdd.append([])
for x in flattened:
assert isinstance(x, np.ndarray), "the data in the data list should be " \
"ndarrays, but get " + \
x.__class__.__name__
assert len(x) == data_length, \
"the ndarrays in data must all have the same size in first dimension" \
", got first ndarray of size {} and another {}".format(data_length, len(x))
x_parts = np.array_split(x, split_num)
for idx, x_part in enumerate(x_parts):
data_to_be_rdd[idx].append(x_part)
data_length_list = list(
map(lambda arr: len(arr), x_part))
data_to_be_rdd = [
nest.pack_sequence_as(data, shard)
for shard in data_to_be_rdd
]
data_rdd = sc.parallelize(data_to_be_rdd, numSlices=split_num)
result_rdd = self.model.distributed_predict(data_rdd, sc)
result_arr_list = result_rdd.collect()
for i in range(0, len(result_arr_list)):
result_arr_list[i] = result_arr_list[i][:data_length_list[i]]
result_arr = np.concatenate(result_arr_list, axis=0)
return result_arr
else:
raise ValueError(
"Only XShards, Spark DataFrame, a numpy array and a list of numpy arr"
"ays are supported as input data, but get " +
data.__class__.__name__)
def predict(self, data, feature_cols=None, batch_size=4):
"""
Predict input data
:param batch_size: Int. Set batch Size, default is 4.
:param data: data to be predicted. XShards, Spark DataFrame, numpy array and list of numpy
arrays are supported. If data is XShards, each partition is a dictionary of {'x':
feature}, where feature(label) is a numpy array or a list of numpy arrays.
:param feature_cols: Feature column name(s) of data. Only used when data is a Spark
DataFrame. Default: None.
:return: predicted result.
If the input data is XShards, the predict result is a XShards, each partition
of the XShards is a dictionary of {'prediction': result}, where the result is a
numpy array or a list of numpy arrays.
If the input data is numpy arrays or list of numpy arrays, the predict result is
a numpy array or a list of numpy arrays.
"""
sc = init_nncontext()
model_bytes_broadcast = sc.broadcast(self.model_bytes)
weight_bytes_broadcast = sc.broadcast(self.weight_bytes)
def partition_inference(partition):
model_bytes = model_bytes_broadcast.value
weight_bytes = weight_bytes_broadcast.value
partition = list(partition)
data_num = len(partition)
ie = IECore()
config = {'CPU_THREADS_NUM': str(self.core_num)}
ie.set_config(config, 'CPU')
net = ie.read_network(model=model_bytes,
weights=weight_bytes,
init_from_buffer=True)
net.batch_size = batch_size
local_model = ie.load_network(network=net,
device_name="CPU",
num_requests=data_num)
inputs = list(iter(local_model.requests[0].input_blobs))
outputs = list(iter(local_model.requests[0].output_blobs))
assert len(
outputs) != 0, "The number of model outputs should not be 0."
def add_elem(d):
d_len = len(d)
if d_len < batch_size:
rep_time = [1] * (d_len - 1)
rep_time.append(batch_size - d_len + 1)
return np.repeat(d, rep_time, axis=0), d_len
else:
return d, d_len
results = []
for idx, batch_data in enumerate(partition):
infer_request = local_model.requests[idx]
input_dict = dict()
elem_num = 0
if isinstance(batch_data, list):
for i, input in enumerate(inputs):
input_dict[input], elem_num = add_elem(batch_data[i])
else:
input_dict[inputs[0]], elem_num = add_elem(batch_data)
infer_request.infer(input_dict)
if len(outputs) == 1:
results.append(infer_request.output_blobs[
outputs[0]].buffer[:elem_num])
else:
results.append(
list(
map(
lambda output: infer_request.output_blobs[
output].buffer[:elem_num], outputs)))
return results
def predict_transform(dict_data, batch_size):
assert isinstance(dict_data, dict), "each shard should be an dict"
assert "x" in dict_data, "key x should in each shard"
feature_data = dict_data["x"]
if isinstance(feature_data, np.ndarray):
assert feature_data.shape[0] <= batch_size, \
"The batch size of input data (the second dim) should be less than the model " \
"batch size, otherwise some inputs will be ignored."
elif isinstance(feature_data, list):
for elem in feature_data:
assert isinstance(elem, np.ndarray), "Each element in the x list should be " \
"a ndarray, but get " + \
elem.__class__.__name__
assert elem.shape[0] <= batch_size, "The batch size of each input data (the " \
"second dim) should be less than the " \
"model batch size, otherwise some inputs " \
"will be ignored."
else:
raise ValueError(
"x in each shard should be a ndarray or a list of ndarray."
)
return feature_data
if isinstance(data, DataFrame):
from zoo.orca.learn.utils import dataframe_to_xshards, convert_predict_rdd_to_dataframe
xshards, _ = dataframe_to_xshards(data,
validation_data=None,
feature_cols=feature_cols,
label_cols=None,
mode="predict")
transformed_data = xshards.transform_shard(predict_transform,
batch_size)
result_rdd = transformed_data.rdd.mapPartitions(
lambda iter: partition_inference(iter))
return convert_predict_rdd_to_dataframe(
data, result_rdd.flatMap(lambda data: data))
elif isinstance(data, SparkXShards):
transformed_data = data.transform_shard(predict_transform,
batch_size)
result_rdd = transformed_data.rdd.mapPartitions(
lambda iter: partition_inference(iter))
def update_result_shard(data):
shard, y = data
shard["prediction"] = y
return shard
return SparkXShards(
data.rdd.zip(result_rdd).map(update_result_shard))
elif isinstance(data, (np.ndarray, list)):
if isinstance(data, np.ndarray):
split_num = math.ceil(len(data) / batch_size)
arrays = np.array_split(data, split_num)
num_slices = min(split_num, self.node_num)
data_rdd = sc.parallelize(arrays, numSlices=num_slices)
elif isinstance(data, list):
flattened = nest.flatten(data)
data_length = len(flattened[0])
data_to_be_rdd = []
split_num = math.ceil(flattened[0].shape[0] / batch_size)
num_slices = min(split_num, self.node_num)
for i in range(split_num):
data_to_be_rdd.append([])
for x in flattened:
assert isinstance(x, np.ndarray), "the data in the data list should be " \
"ndarrays, but get " + \
x.__class__.__name__
assert len(x) == data_length, \
"the ndarrays in data must all have the same size in first dimension" \
", got first ndarray of size {} and another {}".format(data_length, len(x))
x_parts = np.array_split(x, split_num)
for idx, x_part in enumerate(x_parts):
data_to_be_rdd[idx].append(x_part)
data_to_be_rdd = [
nest.pack_sequence_as(data, shard)
for shard in data_to_be_rdd
]
data_rdd = sc.parallelize(data_to_be_rdd, numSlices=num_slices)
print("Partition number: ", data_rdd.getNumPartitions())
result_rdd = data_rdd.mapPartitions(
lambda iter: partition_inference(iter))
result_arr_list = result_rdd.collect()
result_arr = None
if isinstance(result_arr_list[0], list):
result_arr = [
np.concatenate([r[i] for r in result_arr_list], axis=0)
for i in range(len(result_arr_list[0]))
]
elif isinstance(result_arr_list[0], np.ndarray):
result_arr = np.concatenate(result_arr_list, axis=0)
return result_arr
else:
raise ValueError(
"Only XShards, Spark DataFrame, a numpy array and a list of numpy arr"
"ays are supported as input data, but get " +
data.__class__.__name__)
