def create_manifest():
size = 0
sample_list = []
for i in range(19):
size = size + 1
source = prefix_text + "raw data" + str(i)
usage = "TRAIN"
annotations_list = []
for j in range(1):
annotation_type = "modelarts/" + text_entity
if 0 == i % 2:
annotation_name = "name"
else:
annotation_name = "location"
annotation_creation_time = "2019-04-28 08:23:06"
annotation_format = "manifest"
annotation_property = {property_start_index: 0, property_end_index: 5}
annotation_confidence = 0.8
annotated_by = "human"
annotations_list.append(
Annotation(name=annotation_name, type=annotation_type,
confidence=annotation_confidence,
creation_time=annotation_creation_time,
annotated_by=annotated_by, annotation_format=annotation_format, property=annotation_property))
sample_list.append(
Sample(source=source, usage=usage, annotations=annotations_list))
return DataSet(sample=sample_list, size=size)
def create_manifest():
size = 0
sample_list = []
for i in range(19):
size = size + 1
source = prefix_s3 + "audio" + str(i) + ".wav"
usage = "TRAIN"
annotations_list = []
for j in range(1):
annotation_type = "modelarts/" + audio_content
annotation_creation_time = "2019-04-28 08:23:06"
annotation_format = "manifest"
annotation_property = {property_content: "Hello world!"}
annotation_confidence = 0.8
annotated_by = "human"
annotations_list.append(
Annotation(type=annotation_type,
confidence=annotation_confidence,
creation_time=annotation_creation_time,
annotated_by=annotated_by,
annotation_format=annotation_format,
property=annotation_property))
sample_list.append(
Sample(source=source, usage=usage, annotations=annotations_list))
return DataSet(sample=sample_list, size=size)
def create_manifest():
size = 0
sample_list = []
for i in range(19):
size = size + 1
source = "s3://obs-ma/test/classification/datafiles/1_1550650984970_" + str(i) + ".jpg"
inference_loc = "s3://obs-ma/test/classification/datafiles/1_1550650984970_" + str(i) + ".txt"
sample_list.append(
Sample(source=source, inference_loc=inference_loc))
return DataSet(sample=sample_list, size=size)
def create_manifest():
size = 0
sample_list = []
for i in range(10):
size = size + 1
source = "s3://obs-ma/test/classification/datafiles/1_1550650984970_" + str(
i) + ".jpg"
usage = "TRAIN"
inference_loc = "s3://obs-ma/test/classification/datafiles/1_1550650984970_" + str(
i) + ".txt"
id = "XGDVGS" + str(i)
annotations_list = []
for j in range(1):
annotation_type = "modelarts/image_classification"
if 0 == i % 2:
annotation_name = "Cat"
else:
annotation_name = "Dog"
annotation_creation_time = "2019-02-20 08:23:06"
annotation_format = "manifest"
annotation_property = {"color": "black"}
annotation_confidence = 0.8
annotated_by = "human"
annotations_list.append(
Annotation(name=annotation_name,
type=annotation_type,
confidence=annotation_confidence,
creation_time=annotation_creation_time,
annotated_by=annotated_by,
annotation_format=annotation_format,
property=annotation_property))
sample_list.append(
Sample(source=source,
usage=usage,
annotations=annotations_list,
inference_loc=inference_loc,
id=id))
for i in range(9):
id = "XGDVGS" + str(i)
size = size + 1
source = "s3://obs-ma/test/classification/datafiles/1_1550650984970_" + str(
i) + ".jpg"
usage = "TRAIN"
annotations_list = []
inference_loc = "s3://obs-ma/test/classification/datafiles/1_1550650984970_" + str(
i) + ".txt"
sample_list.append(
Sample(source=source,
usage=usage,
annotations=annotations_list,
inference_loc=inference_loc,
id=id))
return DataSet(sample=sample_list, size=size)
def create_manifest(path_base, ak=None, sk=None, endpoint=None):
size = 0
sample_list = []
for i in range(8):
size = size + 1
source = "s3://obs-ma/test/label-0220/datafiles/1 (15)_1550632618199" + str(
i) + ".jpg"
usage = "TRAIN"
inference_loc = "s3://obs-ma/test/label-0220/datafiles/1 (15)_1550632618199" + str(
i) + ".txt"
annotations_list = []
print("sample ", i)
for j in range(2):
annotation_type = "modelarts/object_detection"
annotation_loc = "/000000089955_1556180702627_" + str(i) + str(
j) + ".xml"
annotation_creation_time = "2019-02-20 03:16:58"
annotation_format = "PASCAL VOC"
annotated_by = "human"
annotations_list.append(
Annotation(type=annotation_type,
loc="." + annotation_loc,
creation_time=annotation_creation_time,
annotated_by=annotated_by,
annotation_format=annotation_format))
pascal_voc = test_write_voc_xml.create_pascal_voc()
pascal_voc.save_xml(path_base + annotation_loc, ak, sk, endpoint)
print("test write VOC xml: Success")
sample_list.append(
Sample(source=source,
usage=usage,
annotations=annotations_list,
inference_loc=inference_loc))
return DataSet(sample=sample_list, size=size)