def test_case_00(add_remove_file): # only bin data
data = [{
"image1": bytes("image1 bytes abc", encoding='UTF-8'),
"image2": bytes("image1 bytes def", encoding='UTF-8'),
"image3": bytes("image1 bytes ghi", encoding='UTF-8'),
"image4": bytes("image1 bytes jkl", encoding='UTF-8'),
"image5": bytes("image1 bytes mno", encoding='UTF-8')
}, {
"image1": bytes("image2 bytes abc", encoding='UTF-8'),
"image2": bytes("image2 bytes def", encoding='UTF-8'),
"image3": bytes("image2 bytes ghi", encoding='UTF-8'),
"image4": bytes("image2 bytes jkl", encoding='UTF-8'),
"image5": bytes("image2 bytes mno", encoding='UTF-8')
}, {
"image1": bytes("image3 bytes abc", encoding='UTF-8'),
"image2": bytes("image3 bytes def", encoding='UTF-8'),
"image3": bytes("image3 bytes ghi", encoding='UTF-8'),
"image4": bytes("image3 bytes jkl", encoding='UTF-8'),
"image5": bytes("image3 bytes mno", encoding='UTF-8')
}, {
"image1": bytes("image5 bytes abc", encoding='UTF-8'),
"image2": bytes("image5 bytes def", encoding='UTF-8'),
"image3": bytes("image5 bytes ghi", encoding='UTF-8'),
"image4": bytes("image5 bytes jkl", encoding='UTF-8'),
"image5": bytes("image5 bytes mno", encoding='UTF-8')
}, {
"image1": bytes("image6 bytes abc", encoding='UTF-8'),
"image2": bytes("image6 bytes def", encoding='UTF-8'),
"image3": bytes("image6 bytes ghi", encoding='UTF-8'),
"image4": bytes("image6 bytes jkl", encoding='UTF-8'),
"image5": bytes("image6 bytes mno", encoding='UTF-8')
}]
schema = {
"image1": {
"type": "bytes"
},
"image2": {
"type": "bytes"
},
"image3": {
"type": "bytes"
},
"image4": {
"type": "bytes"
},
"image5": {
"type": "bytes"
}
}
writer = FileWriter(TEMP_FILE, FILES_NUM)
writer.add_schema(schema, "schema")
writer.write_raw_data(data)
writer.commit()
d1 = ds.MindDataset(TEMP_FILE, None, num_readers, shuffle=False)
d1.save(AUTO_FILE, FILES_NUM)
data_value_to_list = []
for item in data:
new_data = {}
new_data['image1'] = np.asarray(list(item["image1"]), dtype=np.uint8)
new_data['image2'] = np.asarray(list(item["image2"]), dtype=np.uint8)
new_data['image3'] = np.asarray(list(item["image3"]), dtype=np.uint8)
new_data['image4'] = np.asarray(list(item["image4"]), dtype=np.uint8)
new_data['image5'] = np.asarray(list(item["image5"]), dtype=np.uint8)
data_value_to_list.append(new_data)
d2 = ds.MindDataset(dataset_file=AUTO_FILE,
num_parallel_workers=num_readers,
shuffle=False)
assert d2.get_dataset_size() == 5
num_iter = 0
for item in d2.create_dict_iterator(num_epochs=1, output_numpy=True):
assert len(item) == 5
for field in item:
if isinstance(item[field], np.ndarray):
assert (
item[field] == data_value_to_list[num_iter][field]).all()
else:
assert item[field] == data_value_to_list[num_iter][field]
num_iter += 1
assert num_iter == 5
def random_split_trans2mindrecord(input_file_path,
output_file_path,
criteo_stats_dict,
part_rows=2000000,
line_per_sample=1000,
test_size=0.1,
seed=2020):
"""Random split data and save mindrecord"""
test_size = int(TRAIN_LINE_COUNT * test_size)
all_indices = [i for i in range(TRAIN_LINE_COUNT)]
np.random.seed(seed)
np.random.shuffle(all_indices)
print("all_indices.size:{}".format(len(all_indices)))
test_indices_set = set(all_indices[:test_size])
print("test_indices_set.size:{}".format(len(test_indices_set)))
print("-----------------------" * 10 + "\n" * 2)
train_data_list = []
test_data_list = []
ids_list = []
wts_list = []
label_list = []
writer_train = FileWriter(
os.path.join(output_file_path, "train_input_part.mindrecord"), 21)
writer_test = FileWriter(
os.path.join(output_file_path, "test_input_part.mindrecord"), 3)
schema = {
"label": {
"type": "float32",
"shape": [-1]
},
"feat_vals": {
"type": "float32",
"shape": [-1]
},
"feat_ids": {
"type": "int32",
"shape": [-1]
}
}
writer_train.add_schema(schema, "CRITEO_TRAIN")
writer_test.add_schema(schema, "CRITEO_TEST")
with open(input_file_path, encoding="utf-8") as file_in:
items_error_size_lineCount = []
count = 0
train_part_number = 0
test_part_number = 0
for i, line in enumerate(file_in):
count += 1
if count % 1000000 == 0:
print("Have handle {}w lines.".format(count // 10000))
line = line.strip("\n")
items = line.split("\t")
if len(items) != 40:
items_error_size_lineCount.append(i)
continue
label = float(items[0])
values = items[1:14]
cats = items[14:]
assert len(values) == 13, "values.size: {}".format(len(values))
assert len(cats) == 26, "cats.size: {}".format(len(cats))
ids, wts = criteo_stats_dict.map_cat2id(values, cats)
ids_list.extend(ids)
wts_list.extend(wts)
label_list.append(label)
if count % line_per_sample == 0:
if i not in test_indices_set:
train_data_list.append({
"feat_ids":
np.array(ids_list, dtype=np.int32),
"feat_vals":
np.array(wts_list, dtype=np.float32),
"label":
np.array(label_list, dtype=np.float32)
})
else:
test_data_list.append({
"feat_ids":
np.array(ids_list, dtype=np.int32),
"feat_vals":
np.array(wts_list, dtype=np.float32),
"label":
np.array(label_list, dtype=np.float32)
})
if train_data_list and len(train_data_list) % part_rows == 0:
writer_train.write_raw_data(train_data_list)
train_data_list.clear()
train_part_number += 1
if test_data_list and len(test_data_list) % part_rows == 0:
writer_test.write_raw_data(test_data_list)
test_data_list.clear()
test_part_number += 1
ids_list.clear()
wts_list.clear()
label_list.clear()
if train_data_list:
writer_train.write_raw_data(train_data_list)
if test_data_list:
writer_test.write_raw_data(test_data_list)
writer_train.commit()
writer_test.commit()
print("-------------" * 10)
print("items_error_size_lineCount.size(): {}.".format(
len(items_error_size_lineCount)))
print("-------------" * 10)
np.save("items_error_size_lineCount.npy", items_error_size_lineCount)
schema = {
"input_ids": {
"type": "int32",
"shape": [-1]
},
}
writer = FileWriter(file_name=args.output_file,
shard_num=args.file_partition)
writer.add_schema(schema, args.dataset_type)
writer.open_and_set_header()
###
transforms_count = 0
if args.dataset_type == 'wiki':
for x in tokenize_wiki(args.input_glob):
transforms_count += 1
writer.write_raw_data([x])
print("Transformed {} records.".format(transforms_count))
elif args.dataset_type == 'lambada':
for x in tokenize_lambada(args.input_glob):
transforms_count += 1
writer.write_raw_data([x])
print("Transformed {} records.".format(transforms_count))
elif args.dataset_type == 'openwebtext':
file_iter = glob.iglob(args.input_glob)
with Pool(processes=args.num_process) as pool:
pool.map(task_unit, package_file(file_iter, args.file_batch_size))
else:
raise ValueError("Not support dataset type: {}".format(
args.dataset_type))
writer.commit()
def test_case_02(add_remove_file): # muti-bytes
data = [{
"file_name":
"001.jpg",
"label":
43,
"float32_array":
np.array([1.2, 2.78, 3.1234, 4.9871, 5.12341], dtype=np.float32),
"float64_array":
np.array([
48.1234556789, 49.3251241431, 50.13514312414, 51.8971298471,
123414314.2141243, 87.1212122
],
dtype=np.float64),
"float32":
3456.12345,
"float64":
1987654321.123456785,
"source_sos_ids":
np.array([1, 2, 3, 4, 5], dtype=np.int32),
"source_sos_mask":
np.array([6, 7, 8, 9, 10, 11, 12], dtype=np.int64),
"image1":
bytes("image1 bytes abc", encoding='UTF-8'),
"image2":
bytes("image1 bytes def", encoding='UTF-8'),
"image3":
bytes("image1 bytes ghi", encoding='UTF-8'),
"image4":
bytes("image1 bytes jkl", encoding='UTF-8'),
"image5":
bytes("image1 bytes mno", encoding='UTF-8')
}, {
"file_name":
"002.jpg",
"label":
91,
"float32_array":
np.array([1.2, 2.78, 4.1234, 4.9871, 5.12341], dtype=np.float32),
"float64_array":
np.array([
48.1234556789, 49.3251241431, 60.13514312414, 51.8971298471,
123414314.2141243, 87.1212122
],
dtype=np.float64),
"float32":
3456.12445,
"float64":
1987654321.123456786,
"source_sos_ids":
np.array([11, 2, 3, 4, 5], dtype=np.int32),
"source_sos_mask":
np.array([16, 7, 8, 9, 10, 11, 12], dtype=np.int64),
"image1":
bytes("image2 bytes abc", encoding='UTF-8'),
"image2":
bytes("image2 bytes def", encoding='UTF-8'),
"image3":
bytes("image2 bytes ghi", encoding='UTF-8'),
"image4":
bytes("image2 bytes jkl", encoding='UTF-8'),
"image5":
bytes("image2 bytes mno", encoding='UTF-8')
}, {
"file_name":
"003.jpg",
"label":
61,
"float32_array":
np.array([1.2, 2.78, 5.1234, 4.9871, 5.12341], dtype=np.float32),
"float64_array":
np.array([
48.1234556789, 49.3251241431, 70.13514312414, 51.8971298471,
123414314.2141243, 87.1212122
],
dtype=np.float64),
"float32":
3456.12545,
"float64":
1987654321.123456787,
"source_sos_ids":
np.array([21, 2, 3, 4, 5], dtype=np.int32),
"source_sos_mask":
np.array([26, 7, 8, 9, 10, 11, 12], dtype=np.int64),
"image1":
bytes("image3 bytes abc", encoding='UTF-8'),
"image2":
bytes("image3 bytes def", encoding='UTF-8'),
"image3":
bytes("image3 bytes ghi", encoding='UTF-8'),
"image4":
bytes("image3 bytes jkl", encoding='UTF-8'),
"image5":
bytes("image3 bytes mno", encoding='UTF-8')
}, {
"file_name":
"004.jpg",
"label":
29,
"float32_array":
np.array([1.2, 2.78, 6.1234, 4.9871, 5.12341], dtype=np.float32),
"float64_array":
np.array([
48.1234556789, 49.3251241431, 80.13514312414, 51.8971298471,
123414314.2141243, 87.1212122
],
dtype=np.float64),
"float32":
3456.12645,
"float64":
1987654321.123456788,
"source_sos_ids":
np.array([31, 2, 3, 4, 5], dtype=np.int32),
"source_sos_mask":
np.array([36, 7, 8, 9, 10, 11, 12], dtype=np.int64),
"image1":
bytes("image4 bytes abc", encoding='UTF-8'),
"image2":
bytes("image4 bytes def", encoding='UTF-8'),
"image3":
bytes("image4 bytes ghi", encoding='UTF-8'),
"image4":
bytes("image4 bytes jkl", encoding='UTF-8'),
"image5":
bytes("image4 bytes mno", encoding='UTF-8')
}, {
"file_name":
"005.jpg",
"label":
78,
"float32_array":
np.array([1.2, 2.78, 7.1234, 4.9871, 5.12341], dtype=np.float32),
"float64_array":
np.array([
48.1234556789, 49.3251241431, 90.13514312414, 51.8971298471,
123414314.2141243, 87.1212122
],
dtype=np.float64),
"float32":
3456.12745,
"float64":
1987654321.123456789,
"source_sos_ids":
np.array([41, 2, 3, 4, 5], dtype=np.int32),
"source_sos_mask":
np.array([46, 7, 8, 9, 10, 11, 12], dtype=np.int64),
"image1":
bytes("image5 bytes abc", encoding='UTF-8'),
"image2":
bytes("image5 bytes def", encoding='UTF-8'),
"image3":
bytes("image5 bytes ghi", encoding='UTF-8'),
"image4":
bytes("image5 bytes jkl", encoding='UTF-8'),
"image5":
bytes("image5 bytes mno", encoding='UTF-8')
}, {
"file_name":
"006.jpg",
"label":
37,
"float32_array":
np.array([1.2, 2.78, 7.1234, 4.9871, 5.12341], dtype=np.float32),
"float64_array":
np.array([
48.1234556789, 49.3251241431, 90.13514312414, 51.8971298471,
123414314.2141243, 87.1212122
],
dtype=np.float64),
"float32":
3456.12745,
"float64":
1987654321.123456789,
"source_sos_ids":
np.array([51, 2, 3, 4, 5], dtype=np.int32),
"source_sos_mask":
np.array([56, 7, 8, 9, 10, 11, 12], dtype=np.int64),
"image1":
bytes("image6 bytes abc", encoding='UTF-8'),
"image2":
bytes("image6 bytes def", encoding='UTF-8'),
"image3":
bytes("image6 bytes ghi", encoding='UTF-8'),
"image4":
bytes("image6 bytes jkl", encoding='UTF-8'),
"image5":
bytes("image6 bytes mno", encoding='UTF-8')
}]
schema = {
"file_name": {
"type": "string"
},
"float32_array": {
"type": "float32",
"shape": [-1]
},
"float64_array": {
"type": "float64",
"shape": [-1]
},
"float32": {
"type": "float32"
},
"float64": {
"type": "float64"
},
"source_sos_ids": {
"type": "int32",
"shape": [-1]
},
"source_sos_mask": {
"type": "int64",
"shape": [-1]
},
"image1": {
"type": "bytes"
},
"image2": {
"type": "bytes"
},
"image3": {
"type": "bytes"
},
"label": {
"type": "int32"
},
"image4": {
"type": "bytes"
},
"image5": {
"type": "bytes"
}
}
writer = FileWriter(TEMP_FILE, FILES_NUM)
writer.add_schema(schema, "schema")
writer.write_raw_data(data)
writer.commit()
d1 = ds.MindDataset(TEMP_FILE, None, num_readers, shuffle=False)
d1.save(AUTO_FILE, FILES_NUM)
data_value_to_list = []
for item in data:
new_data = {}
new_data['file_name'] = np.asarray(item["file_name"], dtype='S')
new_data['float32_array'] = item["float32_array"]
new_data['float64_array'] = item["float64_array"]
new_data['float32'] = item["float32"]
new_data['float64'] = item["float64"]
new_data['source_sos_ids'] = item["source_sos_ids"]
new_data['source_sos_mask'] = item["source_sos_mask"]
new_data['label'] = np.asarray(list([item["label"]]), dtype=np.int32)
new_data['image1'] = np.asarray(list(item["image1"]), dtype=np.uint8)
new_data['image2'] = np.asarray(list(item["image2"]), dtype=np.uint8)
new_data['image3'] = np.asarray(list(item["image3"]), dtype=np.uint8)
new_data['image4'] = np.asarray(list(item["image4"]), dtype=np.uint8)
new_data['image5'] = np.asarray(list(item["image5"]), dtype=np.uint8)
data_value_to_list.append(new_data)
d2 = ds.MindDataset(dataset_file=AUTO_FILE,
num_parallel_workers=num_readers,
shuffle=False)
assert d2.get_dataset_size() == 6
num_iter = 0
for item in d2.create_dict_iterator(num_epochs=1, output_numpy=True):
assert len(item) == 13
for field in item:
if isinstance(item[field], np.ndarray):
if item[field].dtype == np.float32:
assert (item[field] == np.array(
data_value_to_list[num_iter][field],
np.float32)).all()
else:
assert (item[field] == data_value_to_list[num_iter][field]
).all()
else:
assert item[field] == data_value_to_list[num_iter][field]
num_iter += 1
assert num_iter == 6
os.makedirs(dst_dir)
print('number of samples:', len(lines))
writer = FileWriter(file_name=args.dst_path, shard_num=args.num_shards)
writer.add_schema(seg_schema, "seg_schema")
cnt = 0
for l in lines:
img_name = l.strip('\n')
img_path = 'img/' + str(img_name) + '.jpg'
label_path = 'cls_png/' + str(img_name) + '.png'
sample_ = {"file_name": img_path.split('/')[-1]}
with open(os.path.join(args.data_root, img_path), 'rb') as f:
sample_['data'] = f.read()
with open(os.path.join(args.data_root, label_path), 'rb') as f:
sample_['label'] = f.read()
data_list.append(sample_)
cnt += 1
if cnt % 1000 == 0:
writer.write_raw_data(data_list)
print('number of samples written:', cnt)
data_list = []
if data_list:
writer.write_raw_data(data_list)
writer.commit()
print('number of samples written:', cnt)
def test_write_read_process():
mindrecord_file_name = "test.mindrecord"
data = [{
"file_name": "001.jpg",
"label": 43,
"score": 0.8,
"mask": np.array([3, 6, 9], dtype=np.int64),
"segments": np.array([[5.0, 1.6], [65.2, 8.3]], dtype=np.float32),
"data": bytes("image bytes abc", encoding='UTF-8')
}, {
"file_name": "002.jpg",
"label": 91,
"score": 5.4,
"mask": np.array([1, 4, 7], dtype=np.int64),
"segments": np.array([[5.1, 9.1], [2.0, 65.4]], dtype=np.float32),
"data": bytes("image bytes def", encoding='UTF-8')
}, {
"file_name": "003.jpg",
"label": 61,
"score": 6.4,
"mask": np.array([7, 6, 3], dtype=np.int64),
"segments": np.array([[0.0, 5.6], [3.0, 16.3]], dtype=np.float32),
"data": bytes("image bytes ghi", encoding='UTF-8')
}, {
"file_name": "004.jpg",
"label": 29,
"score": 8.1,
"mask": np.array([2, 8, 0], dtype=np.int64),
"segments": np.array([[5.9, 7.2], [4.0, 89.0]], dtype=np.float32),
"data": bytes("image bytes jkl", encoding='UTF-8')
}, {
"file_name": "005.jpg",
"label": 78,
"score": 7.7,
"mask": np.array([3, 1, 2], dtype=np.int64),
"segments": np.array([[0.6, 8.1], [5.3, 49.3]], dtype=np.float32),
"data": bytes("image bytes mno", encoding='UTF-8')
}, {
"file_name": "006.jpg",
"label": 37,
"score": 9.4,
"mask": np.array([7, 6, 7], dtype=np.int64),
"segments": np.array([[4.2, 6.3], [8.9, 81.8]], dtype=np.float32),
"data": bytes("image bytes pqr", encoding='UTF-8')
}]
writer = FileWriter(mindrecord_file_name)
schema = {
"file_name": {
"type": "string"
},
"label": {
"type": "int32"
},
"score": {
"type": "float64"
},
"mask": {
"type": "int64",
"shape": [-1]
},
"segments": {
"type": "float32",
"shape": [2, 2]
},
"data": {
"type": "bytes"
}
}
writer.add_schema(schema, "data is so cool")
writer.write_raw_data(data)
writer.commit()
reader = FileReader(mindrecord_file_name)
count = 0
for index, x in enumerate(reader.get_next()):
assert len(x) == 6
for field in x:
if isinstance(x[field], np.ndarray):
assert (x[field] == data[count][field]).all()
else:
assert x[field] == data[count][field]
count = count + 1
logger.info("#item{}: {}".format(index, x))
assert count == 6
reader.close()
os.remove("{}".format(mindrecord_file_name))
os.remove("{}.db".format(mindrecord_file_name))
def transfer_coco_to_mindrecord(self,
mindrecord_dir,
file_name="coco_det.train.mind",
shard_num=1):
"""Create MindRecord file by image_dir and anno_path."""
if not os.path.isdir(mindrecord_dir):
os.makedirs(mindrecord_dir)
if os.path.isdir(self.image_path) and os.path.exists(self.annot_path):
logger.info("Create MindRecord based on COCO_HP dataset")
else:
raise ValueError(
'data_dir {} or anno_path {} does not exist'.format(
self.image_path, self.annot_path))
mindrecord_path = os.path.join(mindrecord_dir, file_name)
writer = FileWriter(mindrecord_path, shard_num)
centernet_json = {
"img_id": {
"type": "int32",
"shape": [1]
},
"image": {
"type": "bytes"
},
"num_objects": {
"type": "int32"
},
"bboxes": {
"type": "float32",
"shape": [-1, 4]
},
"category_id": {
"type": "int32",
"shape": [-1]
},
}
writer.add_schema(centernet_json, "centernet_json")
for img_id in self.images:
image_info = self.coco.loadImgs([img_id])
annos = self.coco.loadAnns(self.anns[img_id])
# get image
img_name = image_info[0]['file_name']
img_name = os.path.join(self.image_path, img_name)
with open(img_name, 'rb') as f:
image = f.read()
bboxes = []
category_id = []
num_objects = len(annos)
for anno in annos:
bbox = self._coco_box_to_bbox(anno['bbox'])
class_name = self.classs_dict[anno["category_id"]]
if class_name in self.train_cls:
x_min, x_max = bbox[0], bbox[2]
y_min, y_max = bbox[1], bbox[3]
bboxes.append([x_min, y_min, x_max, y_max])
category_id.append(self.train_cls_dict[class_name])
row = {
"img_id": np.array([img_id], dtype=np.int32),
"image": image,
"num_objects": num_objects,
"bboxes": np.array(bboxes, np.float32),
"category_id": np.array(category_id, np.int32)
}
writer.write_raw_data([row])
writer.commit()
logger.info("Create Mindrecord Done, at {}".format(mindrecord_dir))
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--input_file", type=str, required=True,
help='Input raw text file (or comma-separated list of files).')
parser.add_argument("--output_file", type=str, required=True, help='Output MindRecord file.')
parser.add_argument("--num_splits", type=int, default=16,
help='The MindRecord file will be split into the number of partition.')
parser.add_argument("--vocab_file", type=str, required=True,
help='The vocabulary file that the Transformer model was trained on.')
parser.add_argument("--clip_to_max_len", type=bool, default=False,
help='clip sequences to maximum sequence length.')
parser.add_argument("--max_seq_length", type=int, default=128, help='Maximum sequence length.')
parser.add_argument("--bucket", type=ast.literal_eval, default=[16, 32, 48, 64, 128],
help='bucket sequence length')
args = parser.parse_args()
tokenizer = tokenization.WhiteSpaceTokenizer(vocab_file=args.vocab_file)
input_files = []
for input_pattern in args.input_file.split(","):
input_files.append(input_pattern)
logging.info("*** Read from input files ***")
for input_file in input_files:
logging.info(" %s", input_file)
output_file = args.output_file
logging.info("*** Write to output files ***")
logging.info(" %s", output_file)
total_written = 0
total_read = 0
feature_dict = {}
for i in args.bucket:
feature_dict[i] = []
for input_file in input_files:
logging.info("*** Reading from %s ***", input_file)
with open(input_file, "r") as reader:
while True:
line = tokenization.convert_to_unicode(reader.readline())
if not line:
break
total_read += 1
if total_read % 100000 == 0:
logging.info("Read %d ...", total_read)
source_line, target_line = line.strip().split("\t")
source_tokens = tokenizer.tokenize(source_line)
target_tokens = tokenizer.tokenize(target_line)
if len(source_tokens) >= args.max_seq_length or len(target_tokens) >= args.max_seq_length:
logging.info("ignore long sentence!")
continue
instance = create_training_instance(source_tokens, target_tokens, args.max_seq_length,
clip_to_max_len=args.clip_to_max_len)
if instance is None:
continue
features, seq_max_bucket_length = get_instance_features(instance, tokenizer, args.max_seq_length,
args.bucket)
for key in feature_dict:
if key == seq_max_bucket_length:
feature_dict[key].append(features)
if total_read <= 10:
logging.info("*** Example ***")
logging.info("source tokens: %s", " ".join(
[tokenization.convert_to_printable(x) for x in instance.source_eos_tokens]))
logging.info("target tokens: %s", " ".join(
[tokenization.convert_to_printable(x) for x in instance.target_sos_tokens]))
for feature_name in features.keys():
feature = features[feature_name]
logging.info("%s: %s", feature_name, feature)
for i in args.bucket:
if args.num_splits == 1:
output_file_name = output_file
else:
output_file_name = output_file + '_' + str(i) + '_'
writer = FileWriter(output_file_name, args.num_splits)
data_schema = {"source_sos_ids": {"type": "int64", "shape": [-1]},
"source_sos_mask": {"type": "int64", "shape": [-1]},
"source_eos_ids": {"type": "int64", "shape": [-1]},
"source_eos_mask": {"type": "int64", "shape": [-1]},
"target_sos_ids": {"type": "int64", "shape": [-1]},
"target_sos_mask": {"type": "int64", "shape": [-1]},
"target_eos_ids": {"type": "int64", "shape": [-1]},
"target_eos_mask": {"type": "int64", "shape": [-1]}
}
writer.add_schema(data_schema, "tranformer")
features_ = feature_dict[i]
logging.info("Bucket length %d has %d samples, start writing...", i, len(features_))
for item in features_:
writer.write_raw_data([item])
total_written += 1
writer.commit()
logging.info("Wrote %d total instances", total_written)
def test_write_read_process_with_multi_bytes_and_array():
mindrecord_file_name = "test.mindrecord"
data = [{"file_name": "001.jpg", "label": 4,
"image1": bytes("image1 bytes abc", encoding='UTF-8'),
"image2": bytes("image1 bytes def", encoding='UTF-8'),
"source_sos_ids": np.array([1, 2, 3, 4, 5], dtype=np.int64),
"source_sos_mask": np.array([6, 7, 8, 9, 10, 11, 12], dtype=np.int64),
"image3": bytes("image1 bytes ghi", encoding='UTF-8'),
"image4": bytes("image1 bytes jkl", encoding='UTF-8'),
"image5": bytes("image1 bytes mno", encoding='UTF-8'),
"target_sos_ids": np.array([28, 29, 30, 31, 32], dtype=np.int64),
"target_sos_mask": np.array([33, 34, 35, 36, 37, 38], dtype=np.int64),
"target_eos_ids": np.array([39, 40, 41, 42, 43, 44, 45, 46, 47], dtype=np.int64),
"target_eos_mask": np.array([48, 49, 50, 51], dtype=np.int64)},
{"file_name": "002.jpg", "label": 5,
"image1": bytes("image2 bytes abc", encoding='UTF-8'),
"image2": bytes("image2 bytes def", encoding='UTF-8'),
"image3": bytes("image2 bytes ghi", encoding='UTF-8'),
"image4": bytes("image2 bytes jkl", encoding='UTF-8'),
"image5": bytes("image2 bytes mno", encoding='UTF-8'),
"source_sos_ids": np.array([11, 2, 3, 4, 5], dtype=np.int64),
"source_sos_mask": np.array([16, 7, 8, 9, 10, 11, 12], dtype=np.int64),
"target_sos_ids": np.array([128, 29, 30, 31, 32], dtype=np.int64),
"target_sos_mask": np.array([133, 34, 35, 36, 37, 38], dtype=np.int64),
"target_eos_ids": np.array([139, 40, 41, 42, 43, 44, 45, 46, 47], dtype=np.int64),
"target_eos_mask": np.array([148, 49, 50, 51], dtype=np.int64)},
{"file_name": "003.jpg", "label": 6,
"source_sos_ids": np.array([21, 2, 3, 4, 5], dtype=np.int64),
"source_sos_mask": np.array([26, 7, 8, 9, 10, 11, 12], dtype=np.int64),
"target_sos_ids": np.array([228, 29, 30, 31, 32], dtype=np.int64),
"target_sos_mask": np.array([233, 34, 35, 36, 37, 38], dtype=np.int64),
"target_eos_ids": np.array([239, 40, 41, 42, 43, 44, 45, 46, 47], dtype=np.int64),
"image1": bytes("image3 bytes abc", encoding='UTF-8'),
"image2": bytes("image3 bytes def", encoding='UTF-8'),
"image3": bytes("image3 bytes ghi", encoding='UTF-8'),
"image4": bytes("image3 bytes jkl", encoding='UTF-8'),
"image5": bytes("image3 bytes mno", encoding='UTF-8'),
"target_eos_mask": np.array([248, 49, 50, 51], dtype=np.int64)},
{"file_name": "004.jpg", "label": 7,
"source_sos_ids": np.array([31, 2, 3, 4, 5], dtype=np.int64),
"source_sos_mask": np.array([36, 7, 8, 9, 10, 11, 12], dtype=np.int64),
"image1": bytes("image4 bytes abc", encoding='UTF-8'),
"image2": bytes("image4 bytes def", encoding='UTF-8'),
"image3": bytes("image4 bytes ghi", encoding='UTF-8'),
"image4": bytes("image4 bytes jkl", encoding='UTF-8'),
"image5": bytes("image4 bytes mno", encoding='UTF-8'),
"target_sos_ids": np.array([328, 29, 30, 31, 32], dtype=np.int64),
"target_sos_mask": np.array([333, 34, 35, 36, 37, 38], dtype=np.int64),
"target_eos_ids": np.array([339, 40, 41, 42, 43, 44, 45, 46, 47], dtype=np.int64),
"target_eos_mask": np.array([348, 49, 50, 51], dtype=np.int64)},
{"file_name": "005.jpg", "label": 8,
"source_sos_ids": np.array([41, 2, 3, 4, 5], dtype=np.int64),
"source_sos_mask": np.array([46, 7, 8, 9, 10, 11, 12], dtype=np.int64),
"target_sos_ids": np.array([428, 29, 30, 31, 32], dtype=np.int64),
"target_sos_mask": np.array([433, 34, 35, 36, 37, 38], dtype=np.int64),
"image1": bytes("image5 bytes abc", encoding='UTF-8'),
"image2": bytes("image5 bytes def", encoding='UTF-8'),
"image3": bytes("image5 bytes ghi", encoding='UTF-8'),
"image4": bytes("image5 bytes jkl", encoding='UTF-8'),
"image5": bytes("image5 bytes mno", encoding='UTF-8'),
"target_eos_ids": np.array([439, 40, 41, 42, 43, 44, 45, 46, 47], dtype=np.int64),
"target_eos_mask": np.array([448, 49, 50, 51], dtype=np.int64)},
{"file_name": "006.jpg", "label": 9,
"source_sos_ids": np.array([51, 2, 3, 4, 5], dtype=np.int64),
"source_sos_mask": np.array([56, 7, 8, 9, 10, 11, 12], dtype=np.int64),
"target_sos_ids": np.array([528, 29, 30, 31, 32], dtype=np.int64),
"image1": bytes("image6 bytes abc", encoding='UTF-8'),
"image2": bytes("image6 bytes def", encoding='UTF-8'),
"image3": bytes("image6 bytes ghi", encoding='UTF-8'),
"image4": bytes("image6 bytes jkl", encoding='UTF-8'),
"image5": bytes("image6 bytes mno", encoding='UTF-8'),
"target_sos_mask": np.array([533, 34, 35, 36, 37, 38], dtype=np.int64),
"target_eos_ids": np.array([539, 40, 41, 42, 43, 44, 45, 46, 47], dtype=np.int64),
"target_eos_mask": np.array([548, 49, 50, 51], dtype=np.int64)}
]
writer = FileWriter(mindrecord_file_name)
schema = {"file_name": {"type": "string"},
"image1": {"type": "bytes"},
"image2": {"type": "bytes"},
"source_sos_ids": {"type": "int64", "shape": [-1]},
"source_sos_mask": {"type": "int64", "shape": [-1]},
"image3": {"type": "bytes"},
"image4": {"type": "bytes"},
"image5": {"type": "bytes"},
"target_sos_ids": {"type": "int64", "shape": [-1]},
"target_sos_mask": {"type": "int64", "shape": [-1]},
"target_eos_ids": {"type": "int64", "shape": [-1]},
"target_eos_mask": {"type": "int64", "shape": [-1]},
"label": {"type": "int32"}}
writer.add_schema(schema, "data is so cool")
writer.write_raw_data(data)
writer.commit()
reader = FileReader(mindrecord_file_name)
count = 0
for index, x in enumerate(reader.get_next()):
assert len(x) == 13
for field in x:
if isinstance(x[field], np.ndarray):
assert (x[field] == data[count][field]).all()
else:
assert x[field] == data[count][field]
count = count + 1
logger.info("#item{}: {}".format(index, x))
assert count == 6
reader.close()
reader = FileReader(file_name=mindrecord_file_name, columns=["source_sos_ids", "source_sos_mask",
"target_sos_ids"])
count = 0
for index, x in enumerate(reader.get_next()):
assert len(x) == 3
for field in x:
if isinstance(x[field], np.ndarray):
assert (x[field] == data[count][field]).all()
else:
assert x[field] == data[count][field]
count = count + 1
logger.info("#item{}: {}".format(index, x))
assert count == 6
reader.close()
reader = FileReader(file_name=mindrecord_file_name, columns=["image2", "source_sos_mask",
"image3", "target_sos_ids"])
count = 0
for index, x in enumerate(reader.get_next()):
assert len(x) == 4
for field in x:
if isinstance(x[field], np.ndarray):
assert (x[field] == data[count][field]).all()
else:
assert x[field] == data[count][field]
count = count + 1
logger.info("#item{}: {}".format(index, x))
assert count == 6
reader.close()
reader = FileReader(file_name=mindrecord_file_name, columns=["target_sos_ids", "image4",
"source_sos_ids"])
count = 0
for index, x in enumerate(reader.get_next()):
assert len(x) == 3
for field in x:
if isinstance(x[field], np.ndarray):
assert (x[field] == data[count][field]).all()
else:
assert x[field] == data[count][field]
count = count + 1
logger.info("#item{}: {}".format(index, x))
assert count == 6
reader.close()
reader = FileReader(file_name=mindrecord_file_name, columns=["target_sos_ids", "image5",
"image4", "image3", "source_sos_ids"])
count = 0
for index, x in enumerate(reader.get_next()):
assert len(x) == 5
for field in x:
if isinstance(x[field], np.ndarray):
assert (x[field] == data[count][field]).all()
else:
assert x[field] == data[count][field]
count = count + 1
logger.info("#item{}: {}".format(index, x))
assert count == 6
reader.close()
reader = FileReader(file_name=mindrecord_file_name, columns=["target_eos_mask", "image5", "image2",
"source_sos_mask", "label"])
count = 0
for index, x in enumerate(reader.get_next()):
assert len(x) == 5
for field in x:
if isinstance(x[field], np.ndarray):
assert (x[field] == data[count][field]).all()
else:
assert x[field] == data[count][field]
count = count + 1
logger.info("#item{}: {}".format(index, x))
assert count == 6
reader.close()
os.remove("{}".format(mindrecord_file_name))
os.remove("{}.db".format(mindrecord_file_name))
def test_write_read_process_with_multi_array():
mindrecord_file_name = "test.mindrecord"
data = [{"source_sos_ids": np.array([1, 2, 3, 4, 5], dtype=np.int64),
"source_sos_mask": np.array([6, 7, 8, 9, 10, 11, 12], dtype=np.int64),
"source_eos_ids": np.array([13, 14, 15, 16, 17, 18], dtype=np.int64),
"source_eos_mask": np.array([19, 20, 21, 22, 23, 24, 25, 26, 27], dtype=np.int64),
"target_sos_ids": np.array([28, 29, 30, 31, 32], dtype=np.int64),
"target_sos_mask": np.array([33, 34, 35, 36, 37, 38], dtype=np.int64),
"target_eos_ids": np.array([39, 40, 41, 42, 43, 44, 45, 46, 47], dtype=np.int64),
"target_eos_mask": np.array([48, 49, 50, 51], dtype=np.int64)},
{"source_sos_ids": np.array([11, 2, 3, 4, 5], dtype=np.int64),
"source_sos_mask": np.array([16, 7, 8, 9, 10, 11, 12], dtype=np.int64),
"source_eos_ids": np.array([113, 14, 15, 16, 17, 18], dtype=np.int64),
"source_eos_mask": np.array([119, 20, 21, 22, 23, 24, 25, 26, 27], dtype=np.int64),
"target_sos_ids": np.array([128, 29, 30, 31, 32], dtype=np.int64),
"target_sos_mask": np.array([133, 34, 35, 36, 37, 38], dtype=np.int64),
"target_eos_ids": np.array([139, 40, 41, 42, 43, 44, 45, 46, 47], dtype=np.int64),
"target_eos_mask": np.array([148, 49, 50, 51], dtype=np.int64)},
{"source_sos_ids": np.array([21, 2, 3, 4, 5], dtype=np.int64),
"source_sos_mask": np.array([26, 7, 8, 9, 10, 11, 12], dtype=np.int64),
"source_eos_ids": np.array([213, 14, 15, 16, 17, 18], dtype=np.int64),
"source_eos_mask": np.array([219, 20, 21, 22, 23, 24, 25, 26, 27], dtype=np.int64),
"target_sos_ids": np.array([228, 29, 30, 31, 32], dtype=np.int64),
"target_sos_mask": np.array([233, 34, 35, 36, 37, 38], dtype=np.int64),
"target_eos_ids": np.array([239, 40, 41, 42, 43, 44, 45, 46, 47], dtype=np.int64),
"target_eos_mask": np.array([248, 49, 50, 51], dtype=np.int64)},
{"source_sos_ids": np.array([31, 2, 3, 4, 5], dtype=np.int64),
"source_sos_mask": np.array([36, 7, 8, 9, 10, 11, 12], dtype=np.int64),
"source_eos_ids": np.array([313, 14, 15, 16, 17, 18], dtype=np.int64),
"source_eos_mask": np.array([319, 20, 21, 22, 23, 24, 25, 26, 27], dtype=np.int64),
"target_sos_ids": np.array([328, 29, 30, 31, 32], dtype=np.int64),
"target_sos_mask": np.array([333, 34, 35, 36, 37, 38], dtype=np.int64),
"target_eos_ids": np.array([339, 40, 41, 42, 43, 44, 45, 46, 47], dtype=np.int64),
"target_eos_mask": np.array([348, 49, 50, 51], dtype=np.int64)},
{"source_sos_ids": np.array([41, 2, 3, 4, 5], dtype=np.int64),
"source_sos_mask": np.array([46, 7, 8, 9, 10, 11, 12], dtype=np.int64),
"source_eos_ids": np.array([413, 14, 15, 16, 17, 18], dtype=np.int64),
"source_eos_mask": np.array([419, 20, 21, 22, 23, 24, 25, 26, 27], dtype=np.int64),
"target_sos_ids": np.array([428, 29, 30, 31, 32], dtype=np.int64),
"target_sos_mask": np.array([433, 34, 35, 36, 37, 38], dtype=np.int64),
"target_eos_ids": np.array([439, 40, 41, 42, 43, 44, 45, 46, 47], dtype=np.int64),
"target_eos_mask": np.array([448, 49, 50, 51], dtype=np.int64)},
{"source_sos_ids": np.array([51, 2, 3, 4, 5], dtype=np.int64),
"source_sos_mask": np.array([56, 7, 8, 9, 10, 11, 12], dtype=np.int64),
"source_eos_ids": np.array([513, 14, 15, 16, 17, 18], dtype=np.int64),
"source_eos_mask": np.array([519, 20, 21, 22, 23, 24, 25, 26, 27], dtype=np.int64),
"target_sos_ids": np.array([528, 29, 30, 31, 32], dtype=np.int64),
"target_sos_mask": np.array([533, 34, 35, 36, 37, 38], dtype=np.int64),
"target_eos_ids": np.array([539, 40, 41, 42, 43, 44, 45, 46, 47], dtype=np.int64),
"target_eos_mask": np.array([548, 49, 50, 51], dtype=np.int64)}
]
writer = FileWriter(mindrecord_file_name)
schema = {"source_sos_ids": {"type": "int64", "shape": [-1]},
"source_sos_mask": {"type": "int64", "shape": [-1]},
"source_eos_ids": {"type": "int64", "shape": [-1]},
"source_eos_mask": {"type": "int64", "shape": [-1]},
"target_sos_ids": {"type": "int64", "shape": [-1]},
"target_sos_mask": {"type": "int64", "shape": [-1]},
"target_eos_ids": {"type": "int64", "shape": [-1]},
"target_eos_mask": {"type": "int64", "shape": [-1]}}
writer.add_schema(schema, "data is so cool")
writer.write_raw_data(data)
writer.commit()
reader = FileReader(mindrecord_file_name)
count = 0
for index, x in enumerate(reader.get_next()):
assert len(x) == 8
for field in x:
if isinstance(x[field], np.ndarray):
assert (x[field] == data[count][field]).all()
else:
assert x[field] == data[count][field]
count = count + 1
logger.info("#item{}: {}".format(index, x))
assert count == 6
reader.close()
reader = FileReader(file_name=mindrecord_file_name, columns=["source_eos_ids", "source_eos_mask",
"target_sos_ids", "target_sos_mask",
"target_eos_ids", "target_eos_mask"])
count = 0
for index, x in enumerate(reader.get_next()):
assert len(x) == 6
for field in x:
if isinstance(x[field], np.ndarray):
assert (x[field] == data[count][field]).all()
else:
assert x[field] == data[count][field]
count = count + 1
logger.info("#item{}: {}".format(index, x))
assert count == 6
reader.close()
reader = FileReader(file_name=mindrecord_file_name, columns=["source_sos_ids",
"target_sos_ids",
"target_eos_mask"])
count = 0
for index, x in enumerate(reader.get_next()):
assert len(x) == 3
for field in x:
if isinstance(x[field], np.ndarray):
assert (x[field] == data[count][field]).all()
else:
assert x[field] == data[count][field]
count = count + 1
logger.info("#item{}: {}".format(index, x))
assert count == 6
reader.close()
reader = FileReader(file_name=mindrecord_file_name, columns=["target_eos_mask",
"source_eos_mask",
"source_sos_mask"])
count = 0
for index, x in enumerate(reader.get_next()):
assert len(x) == 3
for field in x:
if isinstance(x[field], np.ndarray):
assert (x[field] == data[count][field]).all()
else:
assert x[field] == data[count][field]
count = count + 1
logger.info("#item{}: {}".format(index, x))
assert count == 6
reader.close()
reader = FileReader(file_name=mindrecord_file_name, columns=["target_eos_ids"])
count = 0
for index, x in enumerate(reader.get_next()):
assert len(x) == 1
for field in x:
if isinstance(x[field], np.ndarray):
assert (x[field] == data[count][field]).all()
else:
assert x[field] == data[count][field]
count = count + 1
logger.info("#item{}: {}".format(index, x))
assert count == 6
reader.close()
os.remove("{}".format(mindrecord_file_name))
os.remove("{}.db".format(mindrecord_file_name))
def test_write_read_process_with_multi_bytes():
mindrecord_file_name = "test.mindrecord"
data = [{"file_name": "001.jpg", "label": 43,
"image1": bytes("image1 bytes abc", encoding='UTF-8'),
"image2": bytes("image1 bytes def", encoding='UTF-8'),
"image3": bytes("image1 bytes ghi", encoding='UTF-8'),
"image4": bytes("image1 bytes jkl", encoding='UTF-8'),
"image5": bytes("image1 bytes mno", encoding='UTF-8')},
{"file_name": "002.jpg", "label": 91,
"image1": bytes("image2 bytes abc", encoding='UTF-8'),
"image2": bytes("image2 bytes def", encoding='UTF-8'),
"image3": bytes("image2 bytes ghi", encoding='UTF-8'),
"image4": bytes("image2 bytes jkl", encoding='UTF-8'),
"image5": bytes("image2 bytes mno", encoding='UTF-8')},
{"file_name": "003.jpg", "label": 61,
"image1": bytes("image3 bytes abc", encoding='UTF-8'),
"image2": bytes("image3 bytes def", encoding='UTF-8'),
"image3": bytes("image3 bytes ghi", encoding='UTF-8'),
"image4": bytes("image3 bytes jkl", encoding='UTF-8'),
"image5": bytes("image3 bytes mno", encoding='UTF-8')},
{"file_name": "004.jpg", "label": 29,
"image1": bytes("image4 bytes abc", encoding='UTF-8'),
"image2": bytes("image4 bytes def", encoding='UTF-8'),
"image3": bytes("image4 bytes ghi", encoding='UTF-8'),
"image4": bytes("image4 bytes jkl", encoding='UTF-8'),
"image5": bytes("image4 bytes mno", encoding='UTF-8')},
{"file_name": "005.jpg", "label": 78,
"image1": bytes("image5 bytes abc", encoding='UTF-8'),
"image2": bytes("image5 bytes def", encoding='UTF-8'),
"image3": bytes("image5 bytes ghi", encoding='UTF-8'),
"image4": bytes("image5 bytes jkl", encoding='UTF-8'),
"image5": bytes("image5 bytes mno", encoding='UTF-8')},
{"file_name": "006.jpg", "label": 37,
"image1": bytes("image6 bytes abc", encoding='UTF-8'),
"image2": bytes("image6 bytes def", encoding='UTF-8'),
"image3": bytes("image6 bytes ghi", encoding='UTF-8'),
"image4": bytes("image6 bytes jkl", encoding='UTF-8'),
"image5": bytes("image6 bytes mno", encoding='UTF-8')}
]
writer = FileWriter(mindrecord_file_name)
schema = {"file_name": {"type": "string"},
"image1": {"type": "bytes"},
"image2": {"type": "bytes"},
"image3": {"type": "bytes"},
"label": {"type": "int32"},
"image4": {"type": "bytes"},
"image5": {"type": "bytes"}}
writer.add_schema(schema, "data is so cool")
writer.write_raw_data(data)
writer.commit()
reader = FileReader(mindrecord_file_name)
count = 0
for index, x in enumerate(reader.get_next()):
assert len(x) == 7
for field in x:
if isinstance(x[field], np.ndarray):
assert (x[field] == data[count][field]).all()
else:
assert x[field] == data[count][field]
count = count + 1
logger.info("#item{}: {}".format(index, x))
assert count == 6
reader.close()
reader2 = FileReader(file_name=mindrecord_file_name, columns=["image1", "image2", "image5"])
count = 0
for index, x in enumerate(reader2.get_next()):
assert len(x) == 3
for field in x:
if isinstance(x[field], np.ndarray):
assert (x[field] == data[count][field]).all()
else:
assert x[field] == data[count][field]
count = count + 1
logger.info("#item{}: {}".format(index, x))
assert count == 6
reader2.close()
reader3 = FileReader(file_name=mindrecord_file_name, columns=["image2", "image4"])
count = 0
for index, x in enumerate(reader3.get_next()):
assert len(x) == 2
for field in x:
if isinstance(x[field], np.ndarray):
assert (x[field] == data[count][field]).all()
else:
assert x[field] == data[count][field]
count = count + 1
logger.info("#item{}: {}".format(index, x))
assert count == 6
reader3.close()
reader4 = FileReader(file_name=mindrecord_file_name, columns=["image5", "image2"])
count = 0
for index, x in enumerate(reader4.get_next()):
assert len(x) == 2
for field in x:
if isinstance(x[field], np.ndarray):
assert (x[field] == data[count][field]).all()
else:
assert x[field] == data[count][field]
count = count + 1
logger.info("#item{}: {}".format(index, x))
assert count == 6
reader4.close()
reader5 = FileReader(file_name=mindrecord_file_name, columns=["image5", "image2", "label"])
count = 0
for index, x in enumerate(reader5.get_next()):
assert len(x) == 3
for field in x:
if isinstance(x[field], np.ndarray):
assert (x[field] == data[count][field]).all()
else:
assert x[field] == data[count][field]
count = count + 1
logger.info("#item{}: {}".format(index, x))
assert count == 6
reader5.close()
os.remove("{}".format(mindrecord_file_name))
os.remove("{}.db".format(mindrecord_file_name))