#
# Copyright 2016 The BigDL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import math
import os.path
from pyspark.sql import DataFrame
from bigdl.orca.data import SparkXShards
from bigdl.orca.learn.spark_estimator import Estimator as SparkEstimator
from bigdl.dllib.utils.common import get_node_and_core_number
from bigdl.dllib.utils import nest
from bigdl.dllib.nncontext import init_nncontext
from openvino.inference_engine import IECore
import numpy as np
from bigdl.dllib.utils.log4Error import *
[docs]class Estimator(object):
[docs] @staticmethod
def from_openvino(*, model_path):
"""
Load an openVINO Estimator.
:param model_path: String. The file path to the OpenVINO IR xml file.
"""
return OpenvinoEstimator(model_path=model_path)
[docs]class OpenvinoEstimator(SparkEstimator):
def __init__(self,
*,
model_path):
self.load(model_path)
[docs] def fit(self, data, epochs, batch_size=32, feature_cols=None, label_cols=None,
validation_data=None, checkpoint_trigger=None):
"""
Fit is not supported in OpenVINOEstimator
"""
invalidInputError(False, "not implemented")
[docs] 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))
invalidInputError(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):
invalidInputError(isinstance(dict_data, dict), "each shard should be an dict")
invalidInputError("x" in dict_data, "key x should in each shard")
feature_data = dict_data["x"]
if isinstance(feature_data, np.ndarray):
invalidInputError(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:
invalidInputError(isinstance(elem, np.ndarray),
"Each element in the x list should be a ndarray,"
" but get " + elem.__class__.__name__)
invalidInputError(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:
invalidInputError(False,
"x in each shard should be a ndarray or a list of ndarray.")
return feature_data
if isinstance(data, DataFrame):
from bigdl.orca.learn.utils import dataframe_to_xshards
from bigdl.orca.learn.utils import 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:
invalidInputError(isinstance(x, np.ndarray),
"the data in the data list should be ndarrays,"
" but get " + x.__class__.__name__)
invalidInputError(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:
invalidInputError(False,
"Only XShards, Spark DataFrame, a numpy array and a list of numpy"
" arrays are supported as input data, but"
" get " + data.__class__.__name__)
[docs] def evaluate(self, data, batch_size=32, feature_cols=None, label_cols=None):
"""
Evaluate is not supported in OpenVINOEstimator
"""
invalidInputError(False, "not implemented")
[docs] def get_model(self):
"""
Get_model is not supported in OpenVINOEstimator
"""
invalidInputError(False, "not implemented")
[docs] def save(self, model_path):
"""
Save is not supported in OpenVINOEstimator
"""
invalidInputError(False, "not implemented")
[docs] def load(self, model_path):
"""
Load an openVINO model.
:param model_path: String. The file path to the OpenVINO IR xml file.
:return:
"""
self.node_num, self.core_num = get_node_and_core_number()
invalidInputError(isinstance(model_path, str), "The model_path should be string.")
invalidInputError(os.path.exists(model_path), "The model_path should be exist.")
with open(model_path, 'rb') as file:
self.model_bytes = file.read()
with open(model_path[:model_path.rindex(".")] + ".bin", 'rb') as file:
self.weight_bytes = file.read()
[docs] def set_tensorboard(self, log_dir, app_name):
"""
Set_tensorboard is not supported in OpenVINOEstimator
"""
invalidInputError(False, "not implemented")
[docs] def clear_gradient_clipping(self):
"""
Clear_gradient_clipping is not supported in OpenVINOEstimator
"""
invalidInputError(False, "not implemented")
[docs] def set_constant_gradient_clipping(self, min, max):
"""
Set_constant_gradient_clipping is not supported in OpenVINOEstimator
"""
invalidInputError(False, "not implemented")
[docs] def set_l2_norm_gradient_clipping(self, clip_norm):
"""
Set_l2_norm_gradient_clipping is not supported in OpenVINOEstimator
"""
invalidInputError(False, "not implemented")
[docs] def get_train_summary(self, tag=None):
"""
Get_train_summary is not supported in OpenVINOEstimator
"""
invalidInputError(False, "not implemented")
[docs] def get_validation_summary(self, tag=None):
"""
Get_validation_summary is not supported in OpenVINOEstimator
"""
invalidInputError(False, "not implemented")
[docs] def load_orca_checkpoint(self, path, version):
"""
Load_orca_checkpoint is not supported in OpenVINOEstimator
"""
invalidInputError(False, "not implemented")