def pipeline():
sampler = ds.SubsetSampler(indices, num_samples)
data = ds.NumpySlicesDataset(list(range(0, 10)), sampler=sampler)
dataset_size = data.get_dataset_size()
return [
d[0] for d in data.create_tuple_iterator(num_epochs=1,
output_numpy=True)
], dataset_size
def test_config(num_samples, start_index, subset_size):
sampler = ds.SubsetSampler(start_index, subset_size)
d = ds.ManifestDataset(manifest_file, sampler=sampler)
res = []
for item in d.create_dict_iterator():
res.append(map_[(item["image"].shape[0], item["label"].item())])
return res
def test_cv_minddataset_subset_random_sample_negative(add_and_remove_cv_file):
columns_list = ["data", "file_name", "label"]
num_readers = 4
indices = [1, 2, 4, -1, -2]
samplers = ds.SubsetRandomSampler(indices), ds.SubsetSampler(indices)
for sampler in samplers:
data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
sampler=sampler)
assert data_set.get_dataset_size() == 5
num_iter = 0
for item in data_set.create_dict_iterator(num_epochs=1, output_numpy=True):
logger.info(
"-------------- cv reader basic: {} ------------------------".format(num_iter))
logger.info(
"-------------- item[data]: {} -----------------------------".format(item["data"]))
logger.info(
"-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
logger.info(
"-------------- item[label]: {} ----------------------------".format(item["label"]))
num_iter += 1
assert num_iter == 5
