def read_rdd(esConfig, esResource=None, filter=None, esQuery=None):
"""
Read the data from elastic search into Spark RDD.
:param esConfig: Dictionary which represents configuration for
elastic search(eg. ip, port, es query etc).
:param esResource: Optional. resource file in elastic search.
It also can be set in esConfig
:param filter: Optional. Request only those fields from Elasticsearch
:param esQuery: Optional. es query
:return: Spark RDD
"""
sc = init_nncontext()
if "es.resource" not in esConfig:
esConfig["es.resource"] = esResource
if filter is not None:
esConfig["es.read.source.filter"] = filter
if esQuery is not None:
esConfig["es.query"] = esQuery
rdd = sc.newAPIHadoopRDD(
"org.elasticsearch.hadoop.mr.EsInputFormat",
"org.apache.hadoop.io.NullWritable",
"org.elasticsearch.hadoop.mr.LinkedMapWritable",
conf=esConfig)
return 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 load_pickle(cls, path, minPartitions=None):
"""
Load XShards from pickle files.
:param path: The pickle file path/directory
:param minPartitions: The minimum partitions for the XShards
:return: SparkXShards object
"""
sc = init_nncontext()
return SparkXShards(sc.pickleFile(path, minPartitions))
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 predict(model_path, img_path):
model = InferenceModel()
model.load_openvino(model_path,
weight_path=model_path[:model_path.rindex(".")] +
".bin",
batch_size=BATCH_SIZE)
sc = init_nncontext("OpenVINO Python resnet_v1_50 Inference Example")
# pre-processing
infer_transformer = ChainedPreprocessing([
ImageBytesToMat(),
ImageResize(256, 256),
ImageCenterCrop(224, 224),
ImageMatToTensor(format="NHWC", to_RGB=True)
])
image_set = ImageSet.read(img_path, sc).\
transform(infer_transformer).get_image().collect()
image_set = np.expand_dims(image_set, axis=1)
for i in range(len(image_set) // BATCH_SIZE + 1):
index = i * BATCH_SIZE
# check whether out of index
if index >= len(image_set):
break
batch = image_set[index]
# put 4 images in one batch
for j in range(index + 1, min(index + BATCH_SIZE, len(image_set))):
batch = np.vstack((batch, image_set[j]))
batch = np.expand_dims(batch, axis=0)
# predict batch
predictions = model.predict(batch)
result = predictions[0]
# post-processing for Top-1
print("batch_" + str(i))
for r in result:
output = {}
max_index = np.argmax(r)
output["Top-1"] = str(max_index)
print("* Predict result " + str(output))
print("finished...")
sc.stop()
def read_df(esConfig, esResource, schema=None):
"""
Read the data from elastic search into DataFrame.
:param esConfig Dictionary which represents configuration for
elastic search(eg. ip, port etc).
:param esResource resource file in elastic search.
:param schema Optional. Defines the schema of Spark dataframe.
If each column in Es is single value, don't need set schema.
:return Spark DataFrame. Each row represents a document in ES.
"""
sc = init_nncontext()
sqlContext = SQLContext.getOrCreate(sc)
spark = sqlContext.sparkSession
reader = spark.read_df.format("org.elasticsearch.spark.sql")
for key in esConfig:
reader.option(key, esConfig[key])
if schema:
reader.schema(schema)
df = reader.load(esResource)
return df
def predict(model_path, image_path, top_n):
sc = init_nncontext(
"Image classification inference example using int8 quantized model")
images = ImageSet.read(image_path, sc, image_codec=1)
model = ImageClassifier.load_model(model_path)
output = model.predict_image_set(images)
label_map = model.get_config().label_map()
# list of images composing uri and results in tuple format
predicts = output.get_predict().collect()
sequential = Sequential()
sequential.add(Activation("softmax", input_shape=predicts[0][1][0].shape))
for pre in predicts:
(uri, probs) = pre
out = sequential.forward(probs[0])
sortedProbs = [(prob, index) for index, prob in enumerate(out)]
sortedProbs.sort()
print("Image : %s, top %d prediction result" % (uri, top_n))
for i in range(top_n):
print(
"\t%s, %f" %
(label_map[sortedProbs[999 - i][1]], sortedProbs[999 - i][0]))
# limitations under the License.
#
from bigdl.optim.optimizer import Adam
from keras.datasets import imdb
from keras.preprocessing import sequence
from zoo.pipeline.api.keras.models import Model
from zoo.pipeline.api.keras.layers import *
from zoo.pipeline.api.autograd import *
from zoo.common.nncontext import init_spark_conf
from zoo.common.nncontext import init_nncontext
conf = init_spark_conf()
conf.set("spark.executor.extraJavaOptions", "-Xss512m")
conf.set("spark.driver.extraJavaOptions", "-Xss512m")
sc = init_nncontext(conf)
max_features = 20000
max_len = 200
print('Loading data...')
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')
print('Pad sequences (samples x time)')
x_train = sequence.pad_sequences(x_train, maxlen=max_len)
x_test = sequence.pad_sequences(x_test, maxlen=max_len)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)
xmb = np.zeros((len(x_train), max_len, 2), dtype=np.int32)
from zoo.pipeline.api.keras.layers import *
from zoo.models.recommendation import UserItemFeature
from zoo.models.recommendation import NeuralCF
from zoo.common.nncontext import init_nncontext
import matplotlib
from sklearn import metrics
from operator import itemgetter
from bigdl.dataset import movielens
from bigdl.util.common import *
sc = init_nncontext("NCF Example")
movielens_data = movielens.get_id_ratings("/tmp/movielens/")
min_user_id = np.min(movielens_data[:,0])
max_user_id = np.max(movielens_data[:,0])
min_movie_id = np.min(movielens_data[:,1])
max_movie_id = np.max(movielens_data[:,1])
rating_labels= np.unique(movielens_data[:,2])
print(movielens_data.shape)
print(min_user_id, max_user_id, min_movie_id, max_movie_id, rating_labels)
def build_sample(user_id, item_id, rating):
sample = Sample.from_ndarray(np.array([user_id, item_id]), np.array([rating]))
return UserItemFeature(user_id, item_id, sample)
pairFeatureRdds = sc.parallelize(movielens_data)\
.map(lambda x: build_sample(x[0], x[1], x[2]-1))
pairFeatureRdds.take(3)
trainPairFeatureRdds, valPairFeatureRdds = pairFeatureRdds.randomSplit([0.8, 0.2], seed= 1)
valPairFeatureRdds.cache()
train_rdd= trainPairFeatureRdds.map(lambda pair_feature: pair_feature.sample)
val_rdd= valPairFeatureRdds.map(lambda pair_feature: pair_feature.sample)
sortedProbs.sort()
print("Image : %s, top %d prediction result" % (uri, topN))
for i in range(topN):
print("\t%s, %f" %
(labelMap[sortedProbs[999 - i][1]], sortedProbs[999 - i][0]))
if __name__ == "__main__":
parser = OptionParser()
parser.add_option("-f",
"--folder",
type=str,
dest="img_path",
default=".",
help="Path where the images are stored")
parser.add_option("--model",
type=str,
dest="model_path",
default="",
help="Path where the model is stored")
parser.add_option("--topN",
type=int,
dest="topN",
default=1,
help="top N number")
(options, args) = parser.parse_args(sys.argv)
sc = init_nncontext("Image Classification Example")
predict(options.model_path, options.img_path, options.topN)
def predict(img_path):
val_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(img_path, transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])),
batch_size=8, shuffle=False,
num_workers=1, pin_memory=True)
model = models.resnet18(pretrained=True).eval()
net = TorchNet.from_pytorch(model, [1, 3, 224, 224])
for inputs, labels in val_loader:
output = net.predict(inputs.numpy(), distributed=True).collect()
index = [o.argmax() for o in output]
print(index)
if __name__ == "__main__":
parser = OptionParser()
parser.add_option("--image", type=str, dest="img_path",
help="The path where the images are stored, "
"can be either a folder or an image path")
(options, args) = parser.parse_args(sys.argv)
sc = init_nncontext("Torch ResNet Prediction Example")
predict(options.img_path)
def __init__(self, data):
sc = init_nncontext()
self.broadcast_data = sc.broadcast(data)
self._value = None
parser.add_option("-l",
"--learning_rate",
dest="learning_rate",
default="0.01")
parser.add_option("--log_dir", dest="log_dir", default="/tmp/.bigdl")
parser.add_option("--model", dest="model")
(options, args) = parser.parse_args(sys.argv)
data_path = options.data_path
token_length = int(options.token_length)
sequence_len = int(options.sequence_length)
max_words_num = int(options.max_words_num)
training_split = float(options.training_split)
batch_size = int(options.batch_size)
sc = init_nncontext(
create_spark_conf().setAppName("Text Classification Example"))
print('Processing text dataset...')
texts = get_news20(base_dir=data_path)
text_data_rdd = sc.parallelize(texts, options.partition_num)
word_meta = analyze_texts(text_data_rdd)
# Remove the top 10 words roughly. You might want to fine tune this.
word_meta = dict(word_meta[10:max_words_num])
word_mata_broadcast = sc.broadcast(word_meta)
word2vec = get_glove(base_dir=data_path, dim=token_length)
# Ignore those unknown words.
filtered_word2vec = dict(
(w, v) for w, v in word2vec.items() if w in word_meta)
filtered_word2vec_broadcast = sc.broadcast(filtered_word2vec)
# 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 argparse
import cv2
from zoo.common.nncontext import init_nncontext
from zoo.models.image.objectdetection import *
sc = init_nncontext("Object Detection Example")
parser = argparse.ArgumentParser()
parser.add_argument('model_path', help="Path where the model is stored")
parser.add_argument('img_path', help="Path where the images are stored")
parser.add_argument('output_path', help="Path to store the detection results")
parser.add_argument("--partition_num",
type=int,
default=1,
help="The number of partitions")
def predict(model_path, img_path, output_path, partition_num):
model = ObjectDetector.load_model(model_path)
image_set = ImageSet.read(img_path,
sc,
if __name__ == "__main__":
parser = OptionParser()
parser.add_option("--data_path", dest="data_path")
parser.add_option("--embedding_file", dest="embedding_file")
parser.add_option("--question_length", dest="question_length", default="10")
parser.add_option("--answer_length", dest="answer_length", default="40")
parser.add_option("--partition_num", dest="partition_num", default="4")
parser.add_option("-b", "--batch_size", dest="batch_size", default="200")
parser.add_option("-e", "--nb_epoch", dest="nb_epoch", default="30")
parser.add_option("-l", "--learning_rate", dest="learning_rate", default="0.001")
parser.add_option("-m", "--model", dest="model")
parser.add_option("--output_path", dest="output_path")
(options, args) = parser.parse_args(sys.argv)
sc = init_nncontext("QARanker Example")
q_set = TextSet.read_csv(options.data_path + "/question_corpus.csv",
sc, int(options.partition_num)).tokenize().normalize()\
.word2idx(min_freq=2).shape_sequence(int(options.question_length))
a_set = TextSet.read_csv(options.data_path+"/answer_corpus.csv",
sc, int(options.partition_num)).tokenize().normalize()\
.word2idx(min_freq=2, existing_map=q_set.get_word_index())\
.shape_sequence(int(options.answer_length))
train_relations = Relations.read(options.data_path + "/relation_train.csv",
sc, int(options.partition_num))
train_set = TextSet.from_relation_pairs(train_relations, q_set, a_set)
validate_relations = Relations.read(options.data_path + "/relation_valid.csv",
sc, int(options.partition_num))
validate_set = TextSet.from_relation_lists(validate_relations, q_set, a_set)
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__)
# 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 argparse
import cv2
from zoo.common.nncontext import init_nncontext
from zoo.models.image.objectdetection import *
sc = init_nncontext(create_spark_conf().setAppName("Object Detection Example"))
parser = argparse.ArgumentParser()
parser.add_argument('model_path', help="Path where the model is stored")
parser.add_argument('img_path', help="Path where the images are stored")
parser.add_argument('output_path', help="Path to store the detection results")
def predict(model_path, img_path, output_path):
model = ObjectDetector.load_model(model_path)
image_set = ImageSet.read(img_path, sc)
output = model.predict_image_set(image_set)
config = model.get_config()
visualizer = Visualizer(config.label_map(), encoding="jpg")
visualized = visualizer(output).get_image(to_chw=False).collect()
df['hours'] = df['datetime'].dt.hour
df['awake'] = (((df['hours'] >= awake_begin) & (df['hours'] <= awake_end))
| (df['hours'] == 0)).astype(int)
return df
if __name__ == "__main__":
parser = OptionParser()
parser.add_option("-f",
type=str,
dest="file_path",
help="The file path to be read")
(options, args) = parser.parse_args(sys.argv)
sc = init_nncontext()
# read data
file_path = options.file_path
data_shard = zoo.orca.data.pandas.read_csv(file_path, sc)
data = data_shard.collect()
# repartition
data_shard = data_shard.repartition(2)
# apply function on each element
trans_data_shard = data_shard.transform_shard(process_feature)
data2 = trans_data_shard.collect()
sc.stop()
def load_pickle(cls, path, minPartitions=None):
sc = init_nncontext()
return SparkXShards(sc.pickleFile(path, minPartitions))
from zoo.models.recommendation import NeuralCF
from zoo.common.nncontext import init_nncontext
import matplotlib
from sklearn import metrics
from operator import itemgetter
from bigdl.nn.criterion import *
from bigdl.optim.optimizer import *
from bigdl.dataset import movielens
from bigdl.util.common import *
matplotlib.use('agg')
import matplotlib.pyplot as plt
%pylab inline
# Initilaize NN context, it will get a SparkContext with optimized configuration for BigDL performance.
sc = init_nncontext("NCF Example")
# Data Preparation
# Download and read movielens 1M data
movielens_data = movielens.get_id_ratings("hdfs:///user/leelau/zoo/recommendation-ncf/*")
# Understand the data. Each record is in format of (userid, movieid, rating_score). UserIDs range between 1 and 6040. MovieIDs range between 1 and 3952. Ratings are made on a 5-star scale (whole-star ratings only). Counts of users and movies are recorded for later use.
min_user_id = np.min(movielens_data[:,0])
max_user_id = np.max(movielens_data[:,0])
min_movie_id = np.min(movielens_data[:,1])
max_movie_id = np.max(movielens_data[:,1])
rating_labels= np.unique(movielens_data[:,2])
print(movielens_data.shape)
print(min_user_id, max_user_id, min_movie_id, max_movie_id, rating_labels)
from zoo.models.anomalydetection import AnomalyDetector
import pandas as pd
from pyspark.sql import SQLContext
from pyspark import sql
from optparse import OptionParser
import sys
if __name__ == "__main__":
parser = OptionParser()
parser.add_option("--input_dir", dest="input_dir")
parser.add_option("-b", "--batch_size", dest="batch_size", default="1024")
parser.add_option("--nb_epoch", dest="nb_epoch", default="20")
parser.add_option("--unroll_len", dest="unroll_len", default="24")
(options, args) = parser.parse_args(sys.argv)
sc = init_nncontext("Anomaly Detection Example")
sqlContext = sql.SQLContext(sc)
def load_and_scale(input_path):
df = pd.read_csv(input_path)
df['datetime'] = pd.to_datetime(df['timestamp'])
df['hours'] = df['datetime'].dt.hour
df['awake'] = (((df['hours'] >= 6) & (df['hours'] <= 23)) |
(df['hours'] == 0)).astype(int)
print(df.info())
sqlContext = SQLContext(sc)
dfspark = sqlContext.createDataFrame(df[["value", "hours", "awake"]])
feature_size = len(["value", "hours", "awake"])
return AnomalyDetector.standardScale(dfspark), feature_size
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__)
dest="encoder_output_dim",
default="256")
parser.add_option("--training_split", dest="training_split", default="0.8")
parser.add_option("-b", "--batch_size", dest="batch_size", default="128")
parser.add_option("-e", "--nb_epoch", dest="nb_epoch", default="20")
parser.add_option("-l",
"--learning_rate",
dest="learning_rate",
default="0.01")
parser.add_option("--log_dir",
dest="log_dir",
default="/tmp/.analytics-zoo")
parser.add_option("-m", "--model", dest="model")
(options, args) = parser.parse_args(sys.argv)
sc = init_nncontext("Text Classification Example")
text_set = TextSet.read(path=options.data_path).to_distributed(
sc, int(options.partition_num))
print("Processing text dataset...")
transformed = text_set.tokenize().normalize()\
.word2idx(remove_topN=10, max_words_num=int(options.max_words_num))\
.shape_sequence(len=int(options.sequence_length)).generate_sample()
train_set, val_set = transformed.random_split(
[float(options.training_split), 1 - float(options.training_split)])
if options.model:
model = TextClassifier.load_model(options.model)
else:
token_length = int(options.token_length)
if not (token_length == 50 or token_length == 100
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__)
parser.add_option("--training_split", dest="training_split", default="0.8")
parser.add_option("-b", "--batch_size", dest="batch_size", default="128")
parser.add_option("--nb_epoch", dest="nb_epoch", default="20")
parser.add_option("-l", "--learning_rate", dest="learning_rate", default="0.01")
parser.add_option("--log_dir", dest="log_dir", default="/tmp/.bigdl")
parser.add_option("--model", dest="model")
(options, args) = parser.parse_args(sys.argv)
data_path = options.data_path
token_length = int(options.token_length)
sequence_len = int(options.sequence_length)
max_words_num = int(options.max_words_num)
training_split = float(options.training_split)
batch_size = int(options.batch_size)
sc = init_nncontext(create_spark_conf().setAppName("Text Classification Example"))
print('Processing text dataset...')
texts = get_news20(base_dir=data_path)
text_data_rdd = sc.parallelize(texts, options.partition_num)
word_meta = analyze_texts(text_data_rdd)
# Remove the top 10 words roughly. You might want to fine tune this.
word_meta = dict(word_meta[10: max_words_num])
word_mata_broadcast = sc.broadcast(word_meta)
word2vec = get_glove(base_dir=data_path, dim=token_length)
# Ignore those unknown words.
filtered_word2vec = dict((w, v) for w, v in word2vec.items() if w in word_meta)
filtered_word2vec_broadcast = sc.broadcast(filtered_word2vec)
if __name__ == "__main__":
parser = OptionParser()
parser.add_option("--image",
type=str,
dest="img_path",
help="The path where the images are stored, "
"can be either a folder or an image path")
parser.add_option("--model",
type=str,
dest="model_path",
help="Path to the TensorFlow model file")
(options, args) = parser.parse_args(sys.argv)
sc = init_nncontext("OpenVINO Object Detection Inference Example")
images = ImageSet.read(options.img_path,
sc,
resize_height=600,
resize_width=600).get_image().collect()
input_data = np.concatenate(
[image.reshape((1, 1) + image.shape) for image in images], axis=0)
model_path = options.model_path
model = InferenceModel()
model.load_openvino(model_path,
weight_path=model_path[:model_path.rindex(".")] +
".bin")
predictions = model.predict(input_data)
# Print the detection result of the first image.
print(predictions[0])
# Transpose TensorFlow NHWC format to Analytics Zoo NCHW format.
model.add(Transpose([(2, 4), (2, 3)]))
model.add(Contiguous())
model.add(detector)
# Select the detection_boxes from the output.
model.add(SelectTable(2))
image_set = ImageSet.read(img_path, sc, partition_num)
transformer = ChainedPreprocessing([ImageResize(256, 256), ImageMatToTensor(),
ImageSetToSample()])
transformed_image_set = image_set.transform(transformer)
output = model.predict_image(transformed_image_set.to_image_frame(), batch_per_partition=1)
# Print the detection box with the highest score of the first prediction result.
result = output.get_predict().first()
print(result[1][0])
if __name__ == "__main__":
parser = OptionParser()
parser.add_option("--image", type=str, dest="img_path",
help="The path where the images are stored, "
"can be either a folder or an image path")
parser.add_option("--model", type=str, dest="model_path",
help="The path of the TensorFlow object detection model")
parser.add_option("--partition_num", type=int, dest="partition_num", default=4,
help="The number of partitions")
(options, args) = parser.parse_args(sys.argv)
sc = init_nncontext("TFNet Object Detection Example")
predict(options.model_path, options.img_path, options.partition_num)
from zoo.common.nncontext import init_nncontext
from zoo.feature.image import *
import cv2
import numpy as np
from IPython.display import Image, display
sc = init_nncontext("Image Augmentation Example")
# create LocalImageSet from an image
local_image_set = ImageSet.read("/home/cdsw/image-augmentation/image/test.jpg")
# create LocalImageSet from an image folder
local_image_set = ImageSet.read("/home/cdsw/image-augmentation/image")
# create LocalImageSet from list of images
image = cv2.imread("/home/cdsw/image-augmentation/image/test.jpg")
local_image_set = LocalImageSet([image])
print(local_image_set.get_image())
print('isDistributed: ', local_image_set.is_distributed(), ', isLocal: ', local_image_set.is_local())
# create DistributedImageSet from an image
distributed_image_set = ImageSet.read("/home/cdsw/image-augmentation/image/test.jpg", sc, 2)
# create DistributedImageSet from an image folder
distributed_image_set = ImageSet.read("/home/cdsw/image-augmentation/image/", sc, 2)
# create LocalImageSet from image rdd
image = cv2.imread("/home/cdsw/image-augmentation/image/test.jpg")
image_rdd = sc.parallelize([image], 2)
label_rdd = sc.parallelize([np.array([1.0])], 2)
distributed_image_set = DistributedImageSet(image_rdd, label_rdd)
