def test_serialize_deserialize_tensor(self):
data = torch.rand([12, 23])
serializer = serialization.get_serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
serialized_tensor_message = serializer.serialize(
data,
modality=bittensor.proto.Modality.TENSOR,
from_type=bittensor.proto.TensorType.TORCH)
assert data.requires_grad == serialized_tensor_message.requires_grad
assert list(data.shape) == serialized_tensor_message.shape
assert serialized_tensor_message.modality == bittensor.proto.Modality.TENSOR
assert serialized_tensor_message.dtype == bittensor.proto.DataType.FLOAT32
deserialized_tensor_message = serializer.deserialize(
serialized_tensor_message,
to_type=bittensor.proto.TensorType.TORCH)
assert serialized_tensor_message.requires_grad == deserialized_tensor_message.requires_grad
assert serialized_tensor_message.shape == list(
deserialized_tensor_message.shape)
assert serialization.torch_dtype_to_bittensor_dtype(
deserialized_tensor_message.dtype
) == bittensor.proto.DataType.FLOAT32
assert torch.all(torch.eq(deserialized_tensor_message, data))
def test_serialize_deserialize_image(self):
# Let's grab some image data
data = torchvision.datasets.MNIST(root='data/datasets/',
train=True,
download=True,
transform=transforms.ToTensor())
# Let's grab a random image, and give it a crazy type to break the system
image = data[randrange(len(data))][0]
serializer = serialization.get_serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
serialized_image_tensor_message = serializer.serialize(
image,
modality=bittensor.proto.Modality.IMAGE,
from_type=bittensor.proto.TensorType.TORCH)
assert image.requires_grad == serialized_image_tensor_message.requires_grad
assert list(image.shape) == serialized_image_tensor_message.shape
assert serialized_image_tensor_message.modality == bittensor.proto.Modality.IMAGE
assert serialized_image_tensor_message.dtype != bittensor.proto.DataType.UNKNOWN
deserialized_image_tensor_message = serializer.deserialize(
serialized_image_tensor_message,
to_type=bittensor.proto.TensorType.TORCH)
assert serialized_image_tensor_message.requires_grad == deserialized_image_tensor_message.requires_grad
assert serialized_image_tensor_message.shape == list(
deserialized_image_tensor_message.shape)
assert serialization.torch_dtype_to_bittensor_dtype(
deserialized_image_tensor_message.dtype
) != bittensor.proto.DataType.UNKNOWN
assert torch.all(torch.eq(deserialized_image_tensor_message, image))
def test_serialize_deserialize_image(self):
# Let's grab some image data
# Let's grab a random image, and give it a crazy type to break the system
image = torch.ones([1, 28, 28])
serializer = serialization.get_serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
serialized_image_tensor_message = serializer.serialize(
image,
modality=bittensor.proto.Modality.IMAGE,
from_type=bittensor.proto.TensorType.TORCH)
assert image.requires_grad == serialized_image_tensor_message.requires_grad
assert list(image.shape) == serialized_image_tensor_message.shape
assert serialized_image_tensor_message.modality == bittensor.proto.Modality.IMAGE
assert serialized_image_tensor_message.dtype != bittensor.proto.DataType.UNKNOWN
deserialized_image_tensor_message = serializer.deserialize(
serialized_image_tensor_message,
to_type=bittensor.proto.TensorType.TORCH)
assert serialized_image_tensor_message.requires_grad == deserialized_image_tensor_message.requires_grad
assert serialized_image_tensor_message.shape == list(
deserialized_image_tensor_message.shape)
assert serialization.torch_dtype_to_bittensor_dtype(
deserialized_image_tensor_message.dtype
) != bittensor.proto.DataType.UNKNOWN
assert torch.all(torch.eq(deserialized_image_tensor_message, image))
def test_serialize_deserialize_text(self):
# Let's create some text data
words = ["This", "is", "a", "word", "list"]
max_l = 0
ts_list = []
for w in words:
ts_list.append(torch.ByteTensor(list(bytes(w, 'utf8'))))
max_l = max(ts_list[-1].size()[0], max_l)
data = torch.zeros((len(ts_list), max_l), dtype=torch.int64)
for i, ts in enumerate(ts_list):
data[i, 0:ts.size()[0]] = ts
serializer = serialization.get_serializer(
serialzer_type=bittensor.proto.Serializer.MSGPACK)
serialized_data_tensor_message = serializer.serialize(
data,
modality=bittensor.proto.Modality.TEXT,
from_type=bittensor.proto.TensorType.TORCH)
assert data.requires_grad == serialized_data_tensor_message.requires_grad
assert list(data.shape) == serialized_data_tensor_message.shape
assert serialized_data_tensor_message.modality == bittensor.proto.Modality.TEXT
assert serialized_data_tensor_message.dtype != bittensor.proto.DataType.UNKNOWN
deserialized_data_tensor_message = serializer.deserialize(
serialized_data_tensor_message,
to_type=bittensor.proto.TensorType.TORCH)
assert serialized_data_tensor_message.requires_grad == deserialized_data_tensor_message.requires_grad
assert serialized_data_tensor_message.shape == list(
deserialized_data_tensor_message.shape)
assert serialization.torch_dtype_to_bittensor_dtype(
deserialized_data_tensor_message.dtype
) != bittensor.proto.DataType.UNKNOWN
assert torch.all(torch.eq(deserialized_data_tensor_message, data))
